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"title": "Analysis of Hotel Customer Satisfaction in Heilongjiang Province Based on OTA Data",
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"abstract": "In order to uphold the concept of high-quality service development and improve the service quality of hotel industry in Heilongjiang Province, on the basis of customer satisfaction theory and combined with the development status of hotel industry in Heilongjiang Province, this paper uses keyword extraction algorithm, AHP analytic hierarchy process and emotional analysis technology to extract 12000 Hotel commentary data on OTA platform from equipment and facilities, services, transportation location, catering, price and health care. Six levels and 20 indicators are used to construct the evaluation model of hotel customer satisfaction in Heilongjiang Province. It is found that the factors that have significant impact on high-star hotels are relatively not obvious. The analysis results show that the overall customer satisfaction of hotels in Heilongjiang Province is relatively high, but there are still some improvements. For high-star hotels, we should pay more attention to equipment, facilities and service factors. Middle and low star hotels should pay attention to traffic location and price factors. Finally, according to the conclusion of the model and local characteristics, the corresponding countermeasures and suggestions are proposed.",
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"content": "In order to uphold the concept of high-quality service development and improve the service quality of hotel industry in Heilongjiang Province, on the basis of customer satisfaction theory and combined with the development status of hotel industry in Heilongjiang Province, this paper uses keyword extraction algorithm, AHP analytic hierarchy process and emotional analysis technology to extract 12000 Hotel commentary data on OTA platform from equipment and facilities, services, transportation location, catering, price and health care. Six levels and 20 indicators are used to construct the evaluation model of hotel customer satisfaction in Heilongjiang Province. It is found that the factors that have significant impact on high-star hotels are relatively not obvious. The analysis results show that the overall customer satisfaction of hotels in Heilongjiang Province is relatively high, but there are still some improvements. For high-star hotels, we should pay more attention to equipment, facilities and service factors. Middle and low star hotels should pay attention to traffic location and price factors. Finally, according to the conclusion of the model and local characteristics, the corresponding countermeasures and suggestions are proposed.",
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"abstract": "The main objective of this paper is to ascertain the factors influencing the rating of hotels. With the continuous development of tourism, the hotel industry has also developed with it, and the competition has become more intense. If a hotel wants to be preeminent, its owner needs to know where and how they could improve to satisfy customers. We prefer to determine whether factors of hotel location, guest type, room qualities, and cleanliness will influence customer satisfaction of hotels. In this research paper, using a dataset containing many different hotels, we will utilize three methods to explore this study: methods of literature research, case study, and modelling. Then we analyze important indicators such as r-squared values and z-scores from several multiple linear regression models using different predictors to test the factors that might influence the rating of hotels. We will also make graphs based on different models and predictors to make the influences more visible. Finally, we conclude that room qualities and hotel cleanliness are the two main factors influencing the hotel rating. Possible solutions to improve the satisfaction levels are introduced, including standard cleaning training, transportation service, and customized service.",
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"abstract": "This study investigated the impact of technology-generated listening environments upon processing of cognitive information. The main question asked was: To what extent does background sound impact accuracy on a cognitive task? A total of 217 museum-goers (127 f, 90 m) completed a language categorization task in four different listening environments: rain sounds, fast music, calm music, and silence. Overall, task scores during the four listening conditions were similar, with male scores stronger than female in every condition. Significant differences were seen between children ages 7-12 and teenage and adult groups, and between young adults and adults over 60 years. Strong, but not significant, differences were noted between older teenagers (16 to 19 years) and the youngest adults (20 to 30 years). In addition, strong correlations were seen between the four sound conditions and personal media usage, as well as between sound conditions and years of musical training. Findings support the duplex-mechanism theory (Hughes, Vachon, & Jones, 2007), and suggest that the ability to screen out irrelevant sounds may be more controllable through greater top-down task engagement.",
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"abstract": "This study analyzes the walking improvement of young students, who have not suffered any orthopedic problems, while walking listening to sounds, based on 1/f (f=frequency) fluctuation of center body gravity (COG) using Auto Regressive (AR) modeling, provides clear results for the evaluation of improvement to walking stability. While walking listening to Mozart's music, the power spectra of COG fluctuation was close to 1/f fluctuation, and their curves in high frequency ranges were smooth in comparison to that of non-while listening to music. In the case of listening to rock music, the power spectra of COG fluctuation while walking was out of proportion of 1/f fluctuation in high frequency ranges in comparison to that of students who listened to Mozart's music and those which didn't. Mozart music indicates walking improvement whether students like it or not. This study provides a useful method to improve physical fitness and a means for subjects to maintain a state of well-being.",
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"abstract": "Music-listening on personal devices is arguably the most wide-spread form of human-computer interaction. Reasons for this include that small, widely-available, high-capacity devices offer users high-quality, choice, and mobility. However, they also offer what could be called, alternatively, either privacy or isolation. To understand more about the space of people listening to music and their sociality, we performed a diary study in the United States of 19 participants aged 9-15 over a two-week period. Despite their interest in communicating with their friends and in listening to music, the participants rarely listened to music with their friends, and they rarely recommended music to- or received music recommendations from their friends.",
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"abstract": "This research investigates and compares the effects of listening to Al-Quran and classical music on human brain wave using EEG. The EEG signal was recorded for three states, which are before, during and after listening to Al-Quran and classical music. 28 healthy participants were randomly chosen among Universiti Teknologi MARA students with age range 20 to 28 years old. During this experiment, each sample was exposed to surah Yasin of Al-Quran and Pachelbel's Canon D major of classical music. Interview sessions were carried out prior to the EEG signals acquisition. Results in terms of correlation between the left and the right brainwaves show that there are increment of 12.67% during listening to A1-Quran and the increment for classical music stands at 9.96%. This finding indicates that listening to the Al-Quran recitation increases the alpha band more than listening to the classical music. Consequently, listening to Al-Quran in particular can result in a more relaxing and alert condition compared to classical music.",
"abstracts": [
{
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"content": "This research investigates and compares the effects of listening to Al-Quran and classical music on human brain wave using EEG. The EEG signal was recorded for three states, which are before, during and after listening to Al-Quran and classical music. 28 healthy participants were randomly chosen among Universiti Teknologi MARA students with age range 20 to 28 years old. During this experiment, each sample was exposed to surah Yasin of Al-Quran and Pachelbel's Canon D major of classical music. Interview sessions were carried out prior to the EEG signals acquisition. Results in terms of correlation between the left and the right brainwaves show that there are increment of 12.67% during listening to A1-Quran and the increment for classical music stands at 9.96%. This finding indicates that listening to the Al-Quran recitation increases the alpha band more than listening to the classical music. Consequently, listening to Al-Quran in particular can result in a more relaxing and alert condition compared to classical music.",
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"normalizedAbstract": "This research investigates and compares the effects of listening to Al-Quran and classical music on human brain wave using EEG. The EEG signal was recorded for three states, which are before, during and after listening to Al-Quran and classical music. 28 healthy participants were randomly chosen among Universiti Teknologi MARA students with age range 20 to 28 years old. During this experiment, each sample was exposed to surah Yasin of Al-Quran and Pachelbel's Canon D major of classical music. Interview sessions were carried out prior to the EEG signals acquisition. Results in terms of correlation between the left and the right brainwaves show that there are increment of 12.67% during listening to A1-Quran and the increment for classical music stands at 9.96%. This finding indicates that listening to the Al-Quran recitation increases the alpha band more than listening to the classical music. Consequently, listening to Al-Quran in particular can result in a more relaxing and alert condition compared to classical music.",
"fno": "4668a181",
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"fullName": "Noor Ashikin Zulkurnaini",
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"title": "Automatic Emotion Recognition Based on EEG and ECG Signals While Listening to Quranic Recitation Compared with Listening to Music",
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"abstract": "Relaxation and calmness are two emotions that people continually seek. One popular method people frequently use to reduce their tension and pressure levels is listening to various types of relaxing music. However, the Quran is composed of Allah's words, which were ultimately given for the benefit of humanity. Muslims strongly believe that listening to or reading the Quran brings them comfort, pleasure and confidence that would otherwise elude them, however, scientific evidence is still required to prove that this belief has a scientific basis. Recently, researchers have used electrophysiology to explore the relationships between electrical phenomena and body processes. This research aims to study and analyse the electrical activity of people's brains and hearts when listening to Quranic recitation compared with listening to relaxing music. Two types of electrophysiology readings are used in this research: electroencephalograms (EEGs) and electrocardiograms (ECGs). An EEG measures brain electrical activity, and an ECG measures heart electrical activity. EEG and ECG data were collected from twenty-five subjects. Then, machine learning algorithms were applied to the EEG and ECG signals. The result showed that Quranic recitation demonstrated a positive transformation of the subjects' emotions: from negative precursor emotions to calmness and happiness conditions denoted by a positive valence for the EEG and ECG signals. In contrast, relaxing music showed a positive transformation with regard to the valence in the EEG analysis, however, with respect to the ECG music data analysis, the results revealed a negative transformation for most of the music tracks.",
"abstracts": [
{
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"content": "Relaxation and calmness are two emotions that people continually seek. One popular method people frequently use to reduce their tension and pressure levels is listening to various types of relaxing music. However, the Quran is composed of Allah's words, which were ultimately given for the benefit of humanity. Muslims strongly believe that listening to or reading the Quran brings them comfort, pleasure and confidence that would otherwise elude them, however, scientific evidence is still required to prove that this belief has a scientific basis. Recently, researchers have used electrophysiology to explore the relationships between electrical phenomena and body processes. This research aims to study and analyse the electrical activity of people's brains and hearts when listening to Quranic recitation compared with listening to relaxing music. Two types of electrophysiology readings are used in this research: electroencephalograms (EEGs) and electrocardiograms (ECGs). An EEG measures brain electrical activity, and an ECG measures heart electrical activity. EEG and ECG data were collected from twenty-five subjects. Then, machine learning algorithms were applied to the EEG and ECG signals. The result showed that Quranic recitation demonstrated a positive transformation of the subjects' emotions: from negative precursor emotions to calmness and happiness conditions denoted by a positive valence for the EEG and ECG signals. In contrast, relaxing music showed a positive transformation with regard to the valence in the EEG analysis, however, with respect to the ECG music data analysis, the results revealed a negative transformation for most of the music tracks.",
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"normalizedAbstract": "Relaxation and calmness are two emotions that people continually seek. One popular method people frequently use to reduce their tension and pressure levels is listening to various types of relaxing music. However, the Quran is composed of Allah's words, which were ultimately given for the benefit of humanity. Muslims strongly believe that listening to or reading the Quran brings them comfort, pleasure and confidence that would otherwise elude them, however, scientific evidence is still required to prove that this belief has a scientific basis. Recently, researchers have used electrophysiology to explore the relationships between electrical phenomena and body processes. This research aims to study and analyse the electrical activity of people's brains and hearts when listening to Quranic recitation compared with listening to relaxing music. Two types of electrophysiology readings are used in this research: electroencephalograms (EEGs) and electrocardiograms (ECGs). An EEG measures brain electrical activity, and an ECG measures heart electrical activity. EEG and ECG data were collected from twenty-five subjects. Then, machine learning algorithms were applied to the EEG and ECG signals. The result showed that Quranic recitation demonstrated a positive transformation of the subjects' emotions: from negative precursor emotions to calmness and happiness conditions denoted by a positive valence for the EEG and ECG signals. In contrast, relaxing music showed a positive transformation with regard to the valence in the EEG analysis, however, with respect to the ECG music data analysis, the results revealed a negative transformation for most of the music tracks.",
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"authors": [
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"affiliation": "Dept. of Comput. Sci., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia",
"fullName": "Sabaa Ahmed Yahya Al-Galal",
"givenName": "Sabaa Ahmed Yahya",
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{
"affiliation": "Dept. of Comput. Sci., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia",
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{
"affiliation": "Dept. of Comput. Sci., Int. Islamic Univ. Malaysia, Kuala Lumpur, Malaysia",
"fullName": "Abdul Wahab Bin Abdul Rahman",
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"abstract": "Recent years have witnessed a growing interest in modeling user behaviors in multimedia research, emphasizing the need to consider human factors such as preference, activity, and emotion in system development and evaluation. Following this research line, we present in this paper the LiveJournal two-million post (LJ2M) dataset to foster research on user-centered music information retrieval. The new dataset is characterized by the great diversity of real-life listening contexts where people and music interact. It contains blog articles from the social blogging website LiveJournal, along with tags self-reporting a user's emotional state while posting and the musical track that the user considered as the best match for the post. More importantly, the data are contributed by users spontaneously in their daily lives, instead of being collected in a controlled environment. Therefore, it offers new opportunities to understand the interrelationship among the personal, situational, and musical factors of music listening. As an example application, we present research investigating the interaction between the affective context of the listener and the affective content of music, using audio-based music emotion recognition techniques and a psycholinguistic tool. The study offers insights into the role of music in mood regulation and demonstrates how LJ2M can contribute to studies on real-world music listening behavior.",
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"affiliation": "Dalhousie University",
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"abstract": "DBC (Differential Box-Counting) has been proved the least complex and the most convenient way to calculate the fractal dimension of images. However, for images with low resolution, the existence of empty boxes will influence the accuracy of fractal dimension. In order to reduce its effect, a new approach ADBC (Actual Differential Box-counting) is proposed in this paper. First, the empty boxes are classified into two categories: real empty boxes and potential ones. Then, the probability of the empty boxes being potential ones under higher resolution is determined by associating the spatial domain relations between the Fractional Brownian surface model and the pixel’s gray-level. Thus, the more accurate fractal dimension can be obtained even if the image resolution is not high enough. Experimental tests also indicate that with the complexity of calculation being basically the same, ADBC can effectively improve the accuracy of fractal dimension.",
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"content": "DBC (Differential Box-Counting) has been proved the least complex and the most convenient way to calculate the fractal dimension of images. However, for images with low resolution, the existence of empty boxes will influence the accuracy of fractal dimension. In order to reduce its effect, a new approach ADBC (Actual Differential Box-counting) is proposed in this paper. First, the empty boxes are classified into two categories: real empty boxes and potential ones. Then, the probability of the empty boxes being potential ones under higher resolution is determined by associating the spatial domain relations between the Fractional Brownian surface model and the pixel’s gray-level. Thus, the more accurate fractal dimension can be obtained even if the image resolution is not high enough. Experimental tests also indicate that with the complexity of calculation being basically the same, ADBC can effectively improve the accuracy of fractal dimension.",
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"abstract": "This paper presents an improved method for estimating the local fractal dimension. The bright points in the local fractal dimension (LFD) map image derived from previous method may influence the effect of edge detection. To solve this problem, we use 0 as the replaced value to supplement the values which do not exist. At the same time, based on the improved local fractal dimension we have furthermore proposed an edge detection method according to the difference between two LFD map images which are computed using local windows of different sizes. The experimental results have proved the effectiveness of our method.",
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"content": "This paper presents an improved method for estimating the local fractal dimension. The bright points in the local fractal dimension (LFD) map image derived from previous method may influence the effect of edge detection. To solve this problem, we use 0 as the replaced value to supplement the values which do not exist. At the same time, based on the improved local fractal dimension we have furthermore proposed an edge detection method according to the difference between two LFD map images which are computed using local windows of different sizes. The experimental results have proved the effectiveness of our method.",
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"title": "Study on Recognition Characteristics of Acoustic Emission Based on Fractal Dimension",
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"abstract": "It is difficult to recognise acustic emission (AE) signal because of serious pollution by noise. Fractal dimension is a new method to describe the characteristics of AE singal. According to the complex computation and low precision of box counting dimension, correlation dimension and Katz??dimension, an algorithm of logarithmic fractal dimension based on waveform length was proposed in this paper the deduction process was introduced. The experiment datas was rub impact AE signals sampled from rotating test stand. Gaussian white noise and non-stationary noise were added to simulate the field AE signal which polluted seriously by noise. Then, three algorithms based on box counting dimension, Katz dimension and logarithmic fractal dimension were compared in AE signal recognition. The results show that the algorithm of logarithmic dimension distinguishes rub impact AE signals from strong noise is more effectively, and has lower computation and higher precision than others. It provides a new approach to identify characteristics of??AE signal and detect rub impact fault of rotating machinery.",
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"content": "It is difficult to recognise acustic emission (AE) signal because of serious pollution by noise. Fractal dimension is a new method to describe the characteristics of AE singal. According to the complex computation and low precision of box counting dimension, correlation dimension and Katz??dimension, an algorithm of logarithmic fractal dimension based on waveform length was proposed in this paper the deduction process was introduced. The experiment datas was rub impact AE signals sampled from rotating test stand. Gaussian white noise and non-stationary noise were added to simulate the field AE signal which polluted seriously by noise. Then, three algorithms based on box counting dimension, Katz dimension and logarithmic fractal dimension were compared in AE signal recognition. The results show that the algorithm of logarithmic dimension distinguishes rub impact AE signals from strong noise is more effectively, and has lower computation and higher precision than others. It provides a new approach to identify characteristics of??AE signal and detect rub impact fault of rotating machinery.",
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"fullName": "Li Zhao",
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"abstract": "A solar fine tracking system has been designed and simulated for the pointing of a hard X-ray experiment (pinhole/occulter facility) to be flown on the Space Shuttle or a high altitude balloon. The system hardware consists of a uniform-diameter pinhole camera, photodetector, and sample/hold and analog-to-digital converting circuits. The high-precision algorithm is based on the numerically derived intensity distribution of the solar image. Simulation results show that the algorithm can estimate the absolute planar pointing angle to the Sun with the resolution of +or-0.001 arc seconds.<>",
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"abstract": "The NSF sponsored Scientific Leadership Scholars (SLS) program at Humboldt State University (HSU) provides scholarships to a diverse cohort of students in Computing Science, Environmental Resources Engineering or Mathematics. The program targeted financially eligible Native American and first generation students. The SLS group persisting into year three remained more diverse than earlier corresponding major cohorts: 22 (59%) were either women and/or underrepresented minority (URM) students in STEM. In the 2007-08 academic year, 36 Cohort 1 students entered the program. Of the Cohort 1 freshmen, 23 (66%) persisted into their second year and 12 (33%) persisted into their third year as SLS majors. White and Asian Cohort 1 students were more likely to persist than STEM URM students (47%, n= 8 compared to 21%, n=4). In 2008-09, 15 additional SLS students were added. Of these students, 12 (80%) persisted into their third year as an SLS major. While all white students (6) in Cohort 2 persisted, 67% (6) of the underrepresented students persisted. All 4 women in Cohort 2 persisted. There was no significant difference in the persistence based on gender or first generation status. Recommendations are included.",
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"content": "The NSF sponsored Scientific Leadership Scholars (SLS) program at Humboldt State University (HSU) provides scholarships to a diverse cohort of students in Computing Science, Environmental Resources Engineering or Mathematics. The program targeted financially eligible Native American and first generation students. The SLS group persisting into year three remained more diverse than earlier corresponding major cohorts: 22 (59%) were either women and/or underrepresented minority (URM) students in STEM. In the 2007-08 academic year, 36 Cohort 1 students entered the program. Of the Cohort 1 freshmen, 23 (66%) persisted into their second year and 12 (33%) persisted into their third year as SLS majors. White and Asian Cohort 1 students were more likely to persist than STEM URM students (47%, n= 8 compared to 21%, n=4). In 2008-09, 15 additional SLS students were added. Of these students, 12 (80%) persisted into their third year as an SLS major. While all white students (6) in Cohort 2 persisted, 67% (6) of the underrepresented students persisted. All 4 women in Cohort 2 persisted. There was no significant difference in the persistence based on gender or first generation status. Recommendations are included.",
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"normalizedAbstract": "The NSF sponsored Scientific Leadership Scholars (SLS) program at Humboldt State University (HSU) provides scholarships to a diverse cohort of students in Computing Science, Environmental Resources Engineering or Mathematics. The program targeted financially eligible Native American and first generation students. The SLS group persisting into year three remained more diverse than earlier corresponding major cohorts: 22 (59%) were either women and/or underrepresented minority (URM) students in STEM. In the 2007-08 academic year, 36 Cohort 1 students entered the program. Of the Cohort 1 freshmen, 23 (66%) persisted into their second year and 12 (33%) persisted into their third year as SLS majors. White and Asian Cohort 1 students were more likely to persist than STEM URM students (47%, n= 8 compared to 21%, n=4). In 2008-09, 15 additional SLS students were added. Of these students, 12 (80%) persisted into their third year as an SLS major. While all white students (6) in Cohort 2 persisted, 67% (6) of the underrepresented students persisted. All 4 women in Cohort 2 persisted. There was no significant difference in the persistence based on gender or first generation status. Recommendations are included.",
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"fullName": "M Virnoche",
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"fullName": "E A Eschenbach",
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"title": "2009 International Conference on Artificial Intelligence and Computational Intelligence",
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"id": "12OmNs5rkQw",
"doi": "10.1109/AICI.2009.258",
"title": "Fusion Segmentation Algorithm for SAR Images Based on the Persistence and Clustering in the Contourlet Domain",
"normalizedTitle": "Fusion Segmentation Algorithm for SAR Images Based on the Persistence and Clustering in the Contourlet Domain",
"abstract": "In view of the speckle noise in the SAR images, utilizing the Contourlet’s advantages of multiscale, localization, directionality and anisotropy, a new SAR image fusion segmentation algorithm based on the persistence and clustering in the Contourlet domain is proposed in this paper. The algorithm captures the persistence and clustering of the Contourlet transform, which is modeled by HMT and MRF, respectively. Then, these two models are fused by fuzzy logic, resulting in a Contourlet domain HMT-MRF fusion model. Finally, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis indicate that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In view of the speckle noise in the SAR images, utilizing the Contourlet’s advantages of multiscale, localization, directionality and anisotropy, a new SAR image fusion segmentation algorithm based on the persistence and clustering in the Contourlet domain is proposed in this paper. The algorithm captures the persistence and clustering of the Contourlet transform, which is modeled by HMT and MRF, respectively. Then, these two models are fused by fuzzy logic, resulting in a Contourlet domain HMT-MRF fusion model. Finally, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis indicate that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.",
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"normalizedAbstract": "In view of the speckle noise in the SAR images, utilizing the Contourlet’s advantages of multiscale, localization, directionality and anisotropy, a new SAR image fusion segmentation algorithm based on the persistence and clustering in the Contourlet domain is proposed in this paper. The algorithm captures the persistence and clustering of the Contourlet transform, which is modeled by HMT and MRF, respectively. Then, these two models are fused by fuzzy logic, resulting in a Contourlet domain HMT-MRF fusion model. Finally, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis indicate that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.",
"fno": "3816c402",
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"SAR Images Segmentation",
"Contourlet Transform",
"Persistence",
"Clustering",
"Fuzzy Logic Fusion"
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{
"affiliation": null,
"fullName": "Yan Wu",
"givenName": "Yan",
"surname": "Wu",
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{
"affiliation": null,
"fullName": "Ping Xiao",
"givenName": "Ping",
"surname": "Xiao",
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{
"affiliation": null,
"fullName": "Haitao Zong",
"givenName": "Haitao",
"surname": "Zong",
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{
"affiliation": null,
"fullName": "Xin Wang",
"givenName": "Xin",
"surname": "Wang",
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{
"affiliation": null,
"fullName": "Ming Li",
"givenName": "Ming",
"surname": "Li",
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"proceeding": {
"id": "12OmNy3iFty",
"title": "Proceedings of IEEE 36th Annual Foundations of Computer Science",
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"volume": "0",
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"year": "1995",
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"article": {
"id": "12OmNvAiSjM",
"doi": "10.1109/SFCS.1995.492666",
"title": "Amortization, lazy evaluation, and persistence: lists with catenation via lazy linking",
"normalizedTitle": "Amortization, lazy evaluation, and persistence: lists with catenation via lazy linking",
"abstract": "Amortization has been underutilized in the design of persistent data structures, largely because traditional accounting schemes break down in a persistent setting. Such schemes depend on saving \"credits\" for future use, but a persistent data structure may have multiple \"futures\", each competing for the same credits. We describe how lazy evaluation can often remedy this problem, yielding persistent data structures with good amortized efficiency. In fact, such data structures can be implemented purely functionally in any functional language supporting lazy evaluation. As can example of this technique, we present a purely functional (and therefore persistent) implementation of lists that simultaneously support catenation and all other usual list primitives in constant amortized time. This data structure is much simpler than the only existing data structure with comparable bounds, the recently discovered catenable lists of Kaplan and Tarjan, which support all operations in constant worst-case time.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Amortization has been underutilized in the design of persistent data structures, largely because traditional accounting schemes break down in a persistent setting. Such schemes depend on saving \"credits\" for future use, but a persistent data structure may have multiple \"futures\", each competing for the same credits. We describe how lazy evaluation can often remedy this problem, yielding persistent data structures with good amortized efficiency. In fact, such data structures can be implemented purely functionally in any functional language supporting lazy evaluation. As can example of this technique, we present a purely functional (and therefore persistent) implementation of lists that simultaneously support catenation and all other usual list primitives in constant amortized time. This data structure is much simpler than the only existing data structure with comparable bounds, the recently discovered catenable lists of Kaplan and Tarjan, which support all operations in constant worst-case time.",
"__typename": "ArticleAbstractType"
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],
"normalizedAbstract": "Amortization has been underutilized in the design of persistent data structures, largely because traditional accounting schemes break down in a persistent setting. Such schemes depend on saving \"credits\" for future use, but a persistent data structure may have multiple \"futures\", each competing for the same credits. We describe how lazy evaluation can often remedy this problem, yielding persistent data structures with good amortized efficiency. In fact, such data structures can be implemented purely functionally in any functional language supporting lazy evaluation. As can example of this technique, we present a purely functional (and therefore persistent) implementation of lists that simultaneously support catenation and all other usual list primitives in constant amortized time. This data structure is much simpler than the only existing data structure with comparable bounds, the recently discovered catenable lists of Kaplan and Tarjan, which support all operations in constant worst-case time.",
"fno": "71830646",
"keywords": [
"Data Structures List Processing Lazy Evaluation Persistence Catenation Lazy Linking Amortization Persistent Data Structures"
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"authors": [
{
"affiliation": "Sch. of Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA",
"fullName": "C. Okasaki",
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"surname": "Okasaki",
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"title": "2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
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"doi": "10.1109/CVPR.2016.412",
"title": "Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data is Continuous and Weakly Labelled",
"normalizedTitle": "Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data is Continuous and Weakly Labelled",
"abstract": "This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.",
"abstracts": [
{
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"content": "This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.",
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"normalizedAbstract": "This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm. This allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos. Although we demonstrate this in the context of hand shape recognition, the approach has wider application to any video recognition task where frame level labelling is not available. The iterative EM algorithm leverages the discriminative ability of the CNN to iteratively refine the frame level annotation and subsequent training of the CNN. By embedding the classifier within an EM framework the CNN can easily be trained on 1 million hand images. We demonstrate that the final classifier generalises over both individuals and data sets. The algorithm is evaluated on over 3000 manually labelled hand shape images of 60 different classes which will be released to the community. Furthermore, we demonstrate its use in continuous sign language recognition on two publicly available large sign language data sets, where it outperforms the current state-of-the-art by a large margin. To our knowledge no previous work has explored expectation maximization without Gaussian mixture models to exploit weak sequence labels for sign language recognition.",
"fno": "8851d793",
"keywords": [
"Shape",
"Hidden Markov Models",
"Assistive Technology",
"Gesture Recognition",
"Videos",
"Training",
"Adaptation Models"
],
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"abstract": "Transfer function (TF) specification is an important research issue in direct volume rendering (DVR). In this paper, the problem has been developed as a parameter optimization model and a modern evolutionary algorithm, the particle swarm optimization (PSO), has been applied to solve it effectively. Initial solutions are formed and encoded into particles of PSO, the particles fly intelligently in the search space to achieve the best sequence. We realize the implementation of the algorithm in automatic and interactive approaches, and modify updating strategy emphasizing the user requirement in the interactive method. Experiments results confirm the performance and efficiency of the automatic implement of origin algorithm with a comparison to genetic algorithm (GA) which has been applying to this field previously. A contrast has been made between the conventional and modified PSO algorithm and GA in interactive implement to verify the feasibility and applicability of modified PSO algorithm.",
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"title": "2010 International Conference on Measuring Technology and Mechatronics Automation",
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"title": "5-axis CNC Whirlwind Milling Method on Helical Surfaces of PDM's Rotors",
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"abstract": "The positive displacement motor (PDM) is a very important downhole motor in petroleum exploration and production. The key element in PDMs is the rotor-stator coupling which converts hydraulic potential of high-pressure fluid medium into mechanical energy. Normally both the rotor and the stator are constituted of cylindrical helix surfaces. The PDM’s characteristics directly rely on the machining quality of rotor-stator coupling. 3-axis and 4-axis NC milling machines are usually used to machine the rotor helix surface or helix surface of stator mandrel, which is an injecting molding part utilized to shape the internal cavity of rubber stator. The current CNC milling technology for manufacturing large and complex spiral curved surfaces still retains the drawbacks such as consuming time of long duration, over-tolerance and gouging scores. A new 5-axis CNC machining scheme with the whirlwind milling method is proposed based on 5-axis CNC rotary-table horizontal milling machine specialized. The important strategy of the whirlwind milling technique is to optimize the directions of principal curvatures of the surface being machined with those of the rotational surface of milling cutter to generate a perfect fit. So, it can avoid the above-mentioned shortcomings and enhance the productivity. Some analytical characteristics of helical surfaces are discussed. The fundamental mathematical equations on whirlwind milling technique and its implementation are introduced.",
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"content": "The positive displacement motor (PDM) is a very important downhole motor in petroleum exploration and production. The key element in PDMs is the rotor-stator coupling which converts hydraulic potential of high-pressure fluid medium into mechanical energy. Normally both the rotor and the stator are constituted of cylindrical helix surfaces. The PDM’s characteristics directly rely on the machining quality of rotor-stator coupling. 3-axis and 4-axis NC milling machines are usually used to machine the rotor helix surface or helix surface of stator mandrel, which is an injecting molding part utilized to shape the internal cavity of rubber stator. The current CNC milling technology for manufacturing large and complex spiral curved surfaces still retains the drawbacks such as consuming time of long duration, over-tolerance and gouging scores. A new 5-axis CNC machining scheme with the whirlwind milling method is proposed based on 5-axis CNC rotary-table horizontal milling machine specialized. The important strategy of the whirlwind milling technique is to optimize the directions of principal curvatures of the surface being machined with those of the rotational surface of milling cutter to generate a perfect fit. So, it can avoid the above-mentioned shortcomings and enhance the productivity. Some analytical characteristics of helical surfaces are discussed. The fundamental mathematical equations on whirlwind milling technique and its implementation are introduced.",
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"normalizedAbstract": "The positive displacement motor (PDM) is a very important downhole motor in petroleum exploration and production. The key element in PDMs is the rotor-stator coupling which converts hydraulic potential of high-pressure fluid medium into mechanical energy. Normally both the rotor and the stator are constituted of cylindrical helix surfaces. The PDM’s characteristics directly rely on the machining quality of rotor-stator coupling. 3-axis and 4-axis NC milling machines are usually used to machine the rotor helix surface or helix surface of stator mandrel, which is an injecting molding part utilized to shape the internal cavity of rubber stator. The current CNC milling technology for manufacturing large and complex spiral curved surfaces still retains the drawbacks such as consuming time of long duration, over-tolerance and gouging scores. A new 5-axis CNC machining scheme with the whirlwind milling method is proposed based on 5-axis CNC rotary-table horizontal milling machine specialized. The important strategy of the whirlwind milling technique is to optimize the directions of principal curvatures of the surface being machined with those of the rotational surface of milling cutter to generate a perfect fit. So, it can avoid the above-mentioned shortcomings and enhance the productivity. Some analytical characteristics of helical surfaces are discussed. The fundamental mathematical equations on whirlwind milling technique and its implementation are introduced.",
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"Helical Surfaces",
"Whirlwind Milling Technique",
"Rotors",
"CNC Manufacturing",
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"affiliation": null,
"fullName": "Xianzhong Yi",
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"fullName": "Tiegang Xu",
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"affiliation": null,
"fullName": "Weiguo Ma",
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"affiliation": null,
"fullName": "Zesheng Yan",
"givenName": "Zesheng",
"surname": "Yan",
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"affiliation": null,
"fullName": "Deli Gao",
"givenName": "Deli",
"surname": "Gao",
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"title": "Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)",
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"doi": "10.1109/3DPVT.2006.56",
"title": "Estimating a-priori Unknown 3D Axially Symmetric Surfaces from Noisy Measurements of Their Fragments",
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"abstract": "In this paper, we present a computationally efficient technique for solving the difficult problem of estimating the global shape of a ceramic pot from measurements of its fragments. Each unknown pot is modeled as a surface of revolution, i.e., a 3D line- the central axis of the pot- and a 2D profile curve with respect to that axis. For each fragment, a probabilistic distribution is estimated which models both the geometric shape of the fragment and the variability of the estimated fragment shape. Estimation of the global pot shape is then a Maximum Likelihood Estimation (MLE) problem where we seek the values of the Euclidean transformation parameters that maximize the joint probability of the matched fragments' axis/profile-curve models (which includes the additional constraint that the matched fragments must share a common central axis). This is a new type of curve-analysis problem and our solution is a new and effective approach applicable for generic constrained 2D curve alignment and for modeling of 3D axially-symmetric surfaces and for comparing geometric models which may correspond over a subset of the complete model.",
"abstracts": [
{
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"content": "In this paper, we present a computationally efficient technique for solving the difficult problem of estimating the global shape of a ceramic pot from measurements of its fragments. Each unknown pot is modeled as a surface of revolution, i.e., a 3D line- the central axis of the pot- and a 2D profile curve with respect to that axis. For each fragment, a probabilistic distribution is estimated which models both the geometric shape of the fragment and the variability of the estimated fragment shape. Estimation of the global pot shape is then a Maximum Likelihood Estimation (MLE) problem where we seek the values of the Euclidean transformation parameters that maximize the joint probability of the matched fragments' axis/profile-curve models (which includes the additional constraint that the matched fragments must share a common central axis). This is a new type of curve-analysis problem and our solution is a new and effective approach applicable for generic constrained 2D curve alignment and for modeling of 3D axially-symmetric surfaces and for comparing geometric models which may correspond over a subset of the complete model.",
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"normalizedAbstract": "In this paper, we present a computationally efficient technique for solving the difficult problem of estimating the global shape of a ceramic pot from measurements of its fragments. Each unknown pot is modeled as a surface of revolution, i.e., a 3D line- the central axis of the pot- and a 2D profile curve with respect to that axis. For each fragment, a probabilistic distribution is estimated which models both the geometric shape of the fragment and the variability of the estimated fragment shape. Estimation of the global pot shape is then a Maximum Likelihood Estimation (MLE) problem where we seek the values of the Euclidean transformation parameters that maximize the joint probability of the matched fragments' axis/profile-curve models (which includes the additional constraint that the matched fragments must share a common central axis). This is a new type of curve-analysis problem and our solution is a new and effective approach applicable for generic constrained 2D curve alignment and for modeling of 3D axially-symmetric surfaces and for comparing geometric models which may correspond over a subset of the complete model.",
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"Maximum Likelihood Estimation",
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"affiliation": "University of North Carolina, USA",
"fullName": "Andrew Willis",
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"affiliation": "Brown University, USA",
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"title": "Computer and Computational Sciences, International Multi-Symposiums on",
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"article": {
"id": "12OmNBpEeVi",
"doi": "10.1109/IMSCCS.2006.111",
"title": "Reconstruction of Surfaces of Revolution",
"normalizedTitle": "Reconstruction of Surfaces of Revolution",
"abstract": "In the process of reconstruction of 3D textured models of revolution from images, the precise 3D shape of the revolution must be obtained in advance. This paper addresses the problem of recovering the 3D shape of revolution by symmetry property of revolution. A specially-designed rotary platform is used to obtain images of revolution and calculate image parameters at first, and then the topmost circle of the revolution is accurately reconstructed with a object-space reconstruction method. The rotation axis of revolution is calculated from the topmost circle of the revolution. The algorithm makes use of symmetry properties of surfaces of revolutions and their silhouette to rectify images so that the resulting silhouettes exhibits bilateral symmetry. Then the rectified image is then used to recover the contour generator and the object is reconstructed. Experimental results with real images are presented, which demonstrate the effectiveness of the approach.",
"abstracts": [
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"abstractType": "Regular",
"content": "In the process of reconstruction of 3D textured models of revolution from images, the precise 3D shape of the revolution must be obtained in advance. This paper addresses the problem of recovering the 3D shape of revolution by symmetry property of revolution. A specially-designed rotary platform is used to obtain images of revolution and calculate image parameters at first, and then the topmost circle of the revolution is accurately reconstructed with a object-space reconstruction method. The rotation axis of revolution is calculated from the topmost circle of the revolution. The algorithm makes use of symmetry properties of surfaces of revolutions and their silhouette to rectify images so that the resulting silhouettes exhibits bilateral symmetry. Then the rectified image is then used to recover the contour generator and the object is reconstructed. Experimental results with real images are presented, which demonstrate the effectiveness of the approach.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "In the process of reconstruction of 3D textured models of revolution from images, the precise 3D shape of the revolution must be obtained in advance. This paper addresses the problem of recovering the 3D shape of revolution by symmetry property of revolution. A specially-designed rotary platform is used to obtain images of revolution and calculate image parameters at first, and then the topmost circle of the revolution is accurately reconstructed with a object-space reconstruction method. The rotation axis of revolution is calculated from the topmost circle of the revolution. The algorithm makes use of symmetry properties of surfaces of revolutions and their silhouette to rectify images so that the resulting silhouettes exhibits bilateral symmetry. Then the rectified image is then used to recover the contour generator and the object is reconstructed. Experimental results with real images are presented, which demonstrate the effectiveness of the approach.",
"fno": "25810720",
"keywords": [
"Revolution 3 D Reconstruction Symmetry"
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"authors": [
{
"affiliation": "Wuhan University, China",
"fullName": "Shunyi Zheng",
"givenName": "Shunyi",
"surname": "Zheng",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Wuhan University, China",
"fullName": "Ling Yang",
"givenName": "Ling",
"surname": "Yang",
"__typename": "ArticleAuthorType"
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"pubDate": "2006-06-01T00:00:00",
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"abstract": "The paper is devoted to modeling of single-layer garments as developable median surfaces, modeled in the form of a thin supple shell. The products are modeled in Maple 9.5 and Embarcadero RAD Studio. The modelling of conical skirts considering seams is performed to demonstrate the space form of garments in the gravity field of elastic materials. Also the product aeroman is modelled which brings the surface of a human body by a set of cylinders and cones. According to the Maple 9.5 and Embarcadero RAD Studio we developed a software product required to demonstrate the spatial form of single layer garments. The visualization of the shape of the skirt depending on material stiffness, a number of seams and a number of revolutions of seams around its axis.",
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"abstract": "We present a new method for guiding virtual colonoscopic navigation and registration by using teniae coli as anatomical landmarks. As most existing protocols require a patient to be scanned in both supine and prone positions to increase sensitivity in detecting colonic polyps, reference and registration between scans are necessary. However, the conventional centerline approach, generating only the longitudinal distance along the colon, lacks the necessary orientation information to synchronize the virtual navigation cameras in both scanned positions. In this paper we describe a semi-automatic method to detect teniae coli from a colonic surface model reconstructed from CT colonography. Teniae coli are three bands of longitudinal smooth muscle on the surface of the colon. They form a triple helix structure from the appendix to the sigmoid colon and are ideal references for virtual navigation. Our method was applied to 3 patients resulting in 6 data sets (supine and prone scans). The detected teniae coli matched well with our visual inspection. In addition, we demonstrate that polyps visible on both scans can be located and matched more efficiently with the aid of a teniae coli guided navigation implementation.",
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"abstract": "Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides information on changes occurring during tumor growth in the tumor micro-environment and vasculature. In the present paper, tumor voxel-wise estimates of tumor descriptors including total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level derived from DCE-MRI data are used to guide the deformable registration of subsequent time points over the tumor growth cycle, evaluating their predictive value for tumor growth. The analysis of three preclinical colon carcinoma longitudinal cases shows that using physiologically meaningful measures of tumor as guidance information can improve non-rigid registration of longitudinal tumor imaging data when compared to a state-of-the-art local correlation coefficient Demons approach. Moreover, using the determinant of the Jacobian of the estimated displacement field as an indicator of volume change allows us to observe a correlation between the tumor descriptor values and tumor growth, especially when maps of hypoxic cells and level of oxygen were used to aid registration. To the best of our knowledge, this work demonstrates for the first time the feasibility of using biologically meaningful tumor descriptors (total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level) derived from DCE-MRI to aid non-rigid registration of longitudinal tumor data as well as to estimate tumor growth.",
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"content": "Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides information on changes occurring during tumor growth in the tumor micro-environment and vasculature. In the present paper, tumor voxel-wise estimates of tumor descriptors including total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level derived from DCE-MRI data are used to guide the deformable registration of subsequent time points over the tumor growth cycle, evaluating their predictive value for tumor growth. The analysis of three preclinical colon carcinoma longitudinal cases shows that using physiologically meaningful measures of tumor as guidance information can improve non-rigid registration of longitudinal tumor imaging data when compared to a state-of-the-art local correlation coefficient Demons approach. Moreover, using the determinant of the Jacobian of the estimated displacement field as an indicator of volume change allows us to observe a correlation between the tumor descriptor values and tumor growth, especially when maps of hypoxic cells and level of oxygen were used to aid registration. To the best of our knowledge, this work demonstrates for the first time the feasibility of using biologically meaningful tumor descriptors (total cell number, proliferative cell number, hypoxic cell number, necrotic cell number and oxygen level) derived from DCE-MRI to aid non-rigid registration of longitudinal tumor data as well as to estimate tumor growth.",
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"abstract": "A novel magnetic resonance approach to imaging turbulence in the velocity profiles in major blood vessels is presented. A nuclear magnetic resonance (NMR) image, in general, is dependent on parameters like r, v, a, which represent deterministic parameters of motion. In addition, spins undergo random motion such as diffusion and turbulence, which cannot be described by a predetermined set of parameters of motion. The differentiation between turbulent and nonturbulent flow is based on the deterministic or indeterministic nature of the flow as a function of time. A method of building up a general gradient from a basic set of gradients is investigated here. Image blurring due to the nondeterministic behavior of spins (e.g. diffusion and turbulence) is not removed by this technique of a composite gradient encoding field. This fact is the key to the possibility of identifying regions of turbulent flow. Because of its random nature, elimination of turbulent effects by use of a deterministic method is bound to fail. On the other hand, regions of turbulent flow may be identified owing to signal decrease from those regions.<>",
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"abstract": "Preliminary results concerning the feasibility of using nuclear magnetic resonance (NMR) imaging techniques to visualize 3-D time-dependent flow fields are presented. The possibility of clinical application of this technique is evaluated by measuring steady flow in straight circular tubes, converging sections, and downstream of prosthetic aortic valves, as well as pulsatile flow in circular tubes. Measurements by NMR are compared with previously published results and theoretical data using different visualization techniques. The strategies and techniques used to display the flow images optimally are presented. Preliminary results on the accuracy of flow images obtained from NMR data are presented.<>",
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"normalizedAbstract": "Optical flow estimation remains challenging due to untextured areas, motion boundaries, occlusions, and more. Thus, the estimated flow is not equally reliable across the image. To that end, post-hoc confidence measures have been introduced to assess the per-pixel reliability of the flow. We overcome the artificial separation of optical flow and confidence estimation by introducing a method that jointly predicts optical flow and its underlying uncertainty. Starting from common energy-based formulations, we rely on the corresponding posterior distribution of the flow given the images. We derive a variational inference scheme based on mean field, which incorporates best practices from energy minimization. An uncertainty measure is obtained along the flow at every pixel as the (marginal) entropy of the variational distribution. We demonstrate the flexibility of our probabilistic approach by applying it to two different energies and on two benchmarks. We not only obtain flow results that are competitive with the underlying energy minimization approach, but also a reliable uncertainty measure that significantly outperforms existing post-hoc approaches.",
"fno": "1032b182",
"keywords": [
"Bayes Methods",
"Entropy",
"Image Sequences",
"Motion Estimation",
"Energy Based Formulations",
"Energy Minimization Approach",
"Posterior Distribution",
"Reliable Uncertainty Measure",
"Per Pixel Reliability",
"Post Hoc Confidence Measures",
"Optical Flow Estimation",
"Uncertainty Estimation",
"Uncertainty",
"Optical Imaging",
"Adaptive Optics",
"Optical Variables Measurement",
"Measurement Uncertainty",
"Estimation",
"Probabilistic Logic"
],
"authors": [
{
"affiliation": null,
"fullName": "Anne S. Wannenwetsch",
"givenName": "Anne S.",
"surname": "Wannenwetsch",
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{
"affiliation": null,
"fullName": "Margret Keuper",
"givenName": "Margret",
"surname": "Keuper",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Stefan Roth",
"givenName": "Stefan",
"surname": "Roth",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2017-10-01T00:00:00",
"pubType": "proceedings",
"pages": "1182-1191",
"year": "2017",
"issn": "2380-7504",
"isbn": "978-1-5386-1032-9",
"notes": null,
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