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"title": "2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)",
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"doi": "10.1109/CBMS.2019.00029",
"title": "Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods",
"normalizedTitle": "Retinal OCT Segmentation Using Fuzzy Region Competition and Level Set Methods",
"abstract": "Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.",
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"content": "Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.",
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"normalizedAbstract": "Optical coherence tomography (OCT) is a noninvasive imaging modality that provides in-depth images of the retina. Properties of individual layers on OCT have become important markers for diagnosing and tracking medication of various eye diseases in current ophthalmology. Manual segmentation of OCT scans posed many challenges (errors, inconsistency), which can be addressed by automated segmentation methods. Level set method is one of the most popular methods in the literature used for this purpose. Although level set methods have a fundamental way of handling topological changes, the weak boundaries and noise in addition to inhomogeneity in OCT images make it difficult to segment the layers accurately. Inspired by the concept of region competition, we incorporate prior knowledge of the retinal structure to segment nine (9) layers of the retina. Mainly, we establish a specific region of interest, then use selected components from fuzzy C-Means for initialisation. The clustering in the initialisation stage is also used to guide the evolution through; a Mumford-Shah (MS) selective region competition force and a Hamilton-Jacobi (HJ) balloon force. The forces ensure evolution close to actual retinal boundaries. Finally, the convergence of the method is based on an improved HJ object indication function influenced by the fuzzy membership to prevent leakages at weak boundaries. Experimental results are promising based on 200 OCT images.",
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"keywords": [
"Biomedical Optical Imaging",
"Diseases",
"Eye",
"Fuzzy Set Theory",
"Image Segmentation",
"Medical Image Processing",
"Optical Tomography",
"Pattern Clustering",
"OCT Image",
"Fuzzy C Means",
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"Retinal OCT Segmentation",
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"Mumford Shah Selective Region Competition Force",
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"Force",
"Nonhomogeneous Media",
"Convergence",
"Pigments",
"Retinal OCT Region Competition Level Set Medical Image Analysis Image Segmentation"
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"affiliation": "Brunel University London",
"fullName": "Bashir I. Dodo",
"givenName": "Bashir I.",
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"affiliation": "Brunel University London",
"fullName": "Yongmin Li",
"givenName": "Yongmin",
"surname": "Li",
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{
"affiliation": "Brunel University London",
"fullName": "Allan Tucker",
"givenName": "Allan",
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{
"affiliation": "The Alan Turing Institute, London, United Kingdom.",
"fullName": "Djibril Kaba",
"givenName": "Djibril",
"surname": "Kaba",
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},
{
"affiliation": "Brunel University London",
"fullName": "Xiaohui Liu",
"givenName": "Xiaohui",
"surname": "Liu",
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}
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"title": "Deep Level Set with Confidence Map and Boundary Loss for Medical Image Segmentation",
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"abstract": "Level set method is widely used for image segmentation. Recent work combined traditional level set method with deep learning architecture for image segmentation. However, it is limited when dealing with medical images because of the blurred edges and complex intensity distribution, which leads to the loss of spatial details. To address this problem, we propose a deep level set method to refine object boundary details and improve the segmentation accuracy. We integrate augmented prior knowledge into inputs of CNN, which can make the level set evolution result has more accurate shape. In addition, to consider the spatial correlation of the object, we combine a boundary loss with deep level set model for preventing the reduction of details. We evaluate the proposed method on two medical image data sets, which are prostate magnetic resonance images and retinal fundus images. The experimental results show that the proposed method achieves state-of-the-art performance.",
"abstracts": [
{
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"content": "Level set method is widely used for image segmentation. Recent work combined traditional level set method with deep learning architecture for image segmentation. However, it is limited when dealing with medical images because of the blurred edges and complex intensity distribution, which leads to the loss of spatial details. To address this problem, we propose a deep level set method to refine object boundary details and improve the segmentation accuracy. We integrate augmented prior knowledge into inputs of CNN, which can make the level set evolution result has more accurate shape. In addition, to consider the spatial correlation of the object, we combine a boundary loss with deep level set model for preventing the reduction of details. We evaluate the proposed method on two medical image data sets, which are prostate magnetic resonance images and retinal fundus images. The experimental results show that the proposed method achieves state-of-the-art performance.",
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"affiliation": "Xi’an Jiaotong University,School of Software Engineering,Xi’an,China,710049",
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"affiliation": "Xi’an Jiaotong University,School of Software Engineering,Xi’an,China,710049",
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"affiliation": "Xi’an Jiaotong University,School of Software Engineering,Xi’an,China,710049",
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"affiliation": "Xi’an Jiaotong University,School of Software Engineering,Xi’an,China,710049",
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"title": "Computing with Cells: Membrane Systems",
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"abstract": "Membrane computing, first introduced in 1998 by Gheorghe P\\u aun, is a part of the general research effort of describing and investigating computing models, ideas, architectures, and paradigms from the processes taking place in nature. It is a branch of molecular computing that is motivated by cell biology. Membrane computing identifies an unconventional computing model, namely a P system, which abstracts from the way living cells process chemical compounds in their compartmental structure. Regions defined by a membrane structure contain multisets of objects that evolve according to specified rules. The objects can be represented as symbols or strings of symbols. By using the rules in a nondeterministic (deterministic) maximally parallel manner, transitions between the system configurationscan be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one region to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. P systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future bio-technology gives way to a practical bio-realization. Since its introduction, the literature in this area has grown rapidly (in 2003, the Institute for Scientific Information designated the intial paper as \"fast breaking'' and the domain as an \"emerging research front in computer science''). We give a brief overview of membrane computing and report on recent results that answer some interesting and fundamental open questions in the field. We also look at the recently introduced neural-like systems, called spiking neural P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We present various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind countermachines. The latter devices are known to be equivalent to vector addition systems (or Petri nets) and, hence, have many decidable properties.",
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"content": "Membrane computing, first introduced in 1998 by Gheorghe P\\u aun, is a part of the general research effort of describing and investigating computing models, ideas, architectures, and paradigms from the processes taking place in nature. It is a branch of molecular computing that is motivated by cell biology. Membrane computing identifies an unconventional computing model, namely a P system, which abstracts from the way living cells process chemical compounds in their compartmental structure. Regions defined by a membrane structure contain multisets of objects that evolve according to specified rules. The objects can be represented as symbols or strings of symbols. By using the rules in a nondeterministic (deterministic) maximally parallel manner, transitions between the system configurationscan be obtained. A sequence of transitions is a computation of how the system is evolving. Various ways of controlling the transfer of objects from one region to another and applying the rules, as well as possibilities to dissolve, divide or create membranes have been studied. P systems have a great potential for implementing massively concurrent systems in an efficient way that would allow us to solve currently intractable problems once future bio-technology gives way to a practical bio-realization. Since its introduction, the literature in this area has grown rapidly (in 2003, the Institute for Scientific Information designated the intial paper as \"fast breaking'' and the domain as an \"emerging research front in computer science''). We give a brief overview of membrane computing and report on recent results that answer some interesting and fundamental open questions in the field. We also look at the recently introduced neural-like systems, called spiking neural P systems. These systems incorporate the ideas of spiking neurons into membrane computing. We present various classes and characterize their computing power and complexity. In particular, we analyze asynchronous and sequential systems and present some conditions under which they become (non-)universal. The non-universal variants are characterized by monotonic counter machines and partially blind countermachines. The latter devices are known to be equivalent to vector addition systems (or Petri nets) and, hence, have many decidable properties.",
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"fullName": "Oscar H. Ibarra",
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"title": "The Study on Optimized the Clarification Membrane Filtration Process of Drinking Water Treatment",
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"abstract": "In this paper, the relationship of membrane fouling rates and the operation conditions of Clarification Membrane process had been investigated with Response Surface Methodology (RSM). Base on the data of operation conditions experiment, including: filtration flux, backwashing pressure (the ratio of the pressure of backwashing/filtration), backwashing rate (the ratio of backwashing time/filtration time) and membrane fouling rates, a quadratic polynomial regression model had been established. The model indicates that there is a minimum membrane fouling rates at 0.214 Kpa/d, when the operation conditions are filter time 30min, filtration flux 33.16L/m2•h, backwashing pressure 156.39%, backwashing rates 4.14%. It was verified by the actual experiment that the absolute error between the actual membrane fouling rates at the optimal operation conditions and the prediction of the model is 3.27%, indicating that the model is valid.",
"abstracts": [
{
"abstractType": "Regular",
"content": "In this paper, the relationship of membrane fouling rates and the operation conditions of Clarification Membrane process had been investigated with Response Surface Methodology (RSM). Base on the data of operation conditions experiment, including: filtration flux, backwashing pressure (the ratio of the pressure of backwashing/filtration), backwashing rate (the ratio of backwashing time/filtration time) and membrane fouling rates, a quadratic polynomial regression model had been established. The model indicates that there is a minimum membrane fouling rates at 0.214 Kpa/d, when the operation conditions are filter time 30min, filtration flux 33.16L/m2•h, backwashing pressure 156.39%, backwashing rates 4.14%. It was verified by the actual experiment that the absolute error between the actual membrane fouling rates at the optimal operation conditions and the prediction of the model is 3.27%, indicating that the model is valid.",
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"normalizedAbstract": "In this paper, the relationship of membrane fouling rates and the operation conditions of Clarification Membrane process had been investigated with Response Surface Methodology (RSM). Base on the data of operation conditions experiment, including: filtration flux, backwashing pressure (the ratio of the pressure of backwashing/filtration), backwashing rate (the ratio of backwashing time/filtration time) and membrane fouling rates, a quadratic polynomial regression model had been established. The model indicates that there is a minimum membrane fouling rates at 0.214 Kpa/d, when the operation conditions are filter time 30min, filtration flux 33.16L/m2•h, backwashing pressure 156.39%, backwashing rates 4.14%. It was verified by the actual experiment that the absolute error between the actual membrane fouling rates at the optimal operation conditions and the prediction of the model is 3.27%, indicating that the model is valid.",
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"fullName": "Yun Wu",
"givenName": "Yun",
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{
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"fullName": "Hongwei Zhang",
"givenName": "Hongwei",
"surname": "Zhang",
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{
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"fullName": "Jie Wang",
"givenName": "Jie",
"surname": "Wang",
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{
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"fullName": "Ling Wang",
"givenName": "Ling",
"surname": "Wang",
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"title": "Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
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"doi": "10.1109/CVPR.1997.609392",
"title": "A Minimum-Variance Adaptive Surface Mesh",
"normalizedTitle": "A Minimum-Variance Adaptive Surface Mesh",
"abstract": "Our main contribution in this paper is to describe a new class of the adaptive mesh. The mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. The adaptive behavior is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. We show that the density of control points on the mesh is regulated by the curvature of the underlying surface. Finally, we illustrate the effectiveness of the method on both real-world and simulated data-sets.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Our main contribution in this paper is to describe a new class of the adaptive mesh. The mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. The adaptive behavior is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. We show that the density of control points on the mesh is regulated by the curvature of the underlying surface. Finally, we illustrate the effectiveness of the method on both real-world and simulated data-sets.",
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"normalizedAbstract": "Our main contribution in this paper is to describe a new class of the adaptive mesh. The mesh uses both split and merge operations to adapt itself to the structure of volumetric data-points. The adaptive behavior is controlled by the variance of the data-point positions about maximum-likelihood quadric patches. We show that the density of control points on the mesh is regulated by the curvature of the underlying surface. Finally, we illustrate the effectiveness of the method on both real-world and simulated data-sets.",
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"affiliation": "University of York",
"fullName": "Richard C Wilson",
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{
"affiliation": "University of York",
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"title": "RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks",
"normalizedTitle": "RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks",
"abstract": "The brain-inspired and event-driven Spiking Neural Network (SNN) aiming at mimicking the synaptic activity of biological neurons has received increasing attention. It transmits binary spike signals between network units when the membrane potential exceeds the firing threshold. This biomimetic mechanism of SNN appears energy-efficiency with its power sparsity and asynchronous operations on spike events. Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence. Such undesired shifts would prevent the SNN from performing well and going deep. To tackle these problems, we attempt to rectify the membrane potential distribution (MPD) by designing a novel distribution loss, MPD-Loss, which can explicitly penalize the un-desired shifts without introducing any additional operations in the inference phase. Moreover, the proposed method can also mitigate the quantization error in SNNs, which is usually ignored in other works. Experimental results demonstrate that the proposed method can directly train a deeper, larger, and better-performing SNN within fewer timesteps.",
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{
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"content": "The brain-inspired and event-driven Spiking Neural Network (SNN) aiming at mimicking the synaptic activity of biological neurons has received increasing attention. It transmits binary spike signals between network units when the membrane potential exceeds the firing threshold. This biomimetic mechanism of SNN appears energy-efficiency with its power sparsity and asynchronous operations on spike events. Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence. Such undesired shifts would prevent the SNN from performing well and going deep. To tackle these problems, we attempt to rectify the membrane potential distribution (MPD) by designing a novel distribution loss, MPD-Loss, which can explicitly penalize the un-desired shifts without introducing any additional operations in the inference phase. Moreover, the proposed method can also mitigate the quantization error in SNNs, which is usually ignored in other works. Experimental results demonstrate that the proposed method can directly train a deeper, larger, and better-performing SNN within fewer timesteps.",
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"fno": "694600a326",
"keywords": [
"Bioelectric Potentials",
"Biomimetics",
"Brain",
"Learning Artificial Intelligence",
"Neural Chips",
"Neural Nets",
"Neurophysiology",
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"Binary Spikes",
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"title": "RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Network",
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"abstract": "Spiking Neural Networks (SNNs) have recently attracted significant research interest as the third generation of artificial neural networks that can enable low-power event-driven data analytics. The best performing SNNs for image recognition tasks are obtained by converting a trained Analog Neural Network (ANN), consisting of Rectified Linear Units (ReLU), to SNN composed of integrate-and-fire neurons with \"proper\" firing thresholds. The converted SNNs typically incur loss in accuracy compared to that provided by the original ANN and require sizable number of inference time-steps to achieve the best accuracy. We find that performance degradation in the converted SNN stems from using \"hard reset\" spiking neuron that is driven to fixed reset potential once its membrane potential exceeds the firing threshold, leading to information loss during SNN inference. We propose ANN-SNN conversion using \"soft reset\" spiking neuron model, referred to as Residual Membrane Potential (RMP) spiking neuron, which retains the \"residual\" membrane potential above threshold at the firing instants. We demonstrate near loss-less ANN-SNN conversion using RMP neurons for VGG-16, ResNet-20, and ResNet-34 SNNs on challenging datasets including CIFAR-10 (93.63% top-1), CIFAR-100 (70.93% top-1), and ImageNet (73.09% top-1 accuracy). Our results also show that RMP-SNN surpasses the best inference accuracy provided by the converted SNN with \"hard reset\" spiking neurons using 2-8 times fewer inference time-steps across network architectures and datasets.",
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"affiliation": "School of Electrical and Computer Engineering, Purdue University",
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"abstract": "Human soft tissue simulation is an important problem in computer graphics. This paper presents a human soft tissue simulation method based on mass-spring model. Applied weight vector into Euler integration method, the dynamic equation is worked out, then the process of deformation can be expressed. Experimental results show that the model creates realistic simulations and has a fast speed.",
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"content": "Human soft tissue simulation is an important problem in computer graphics. This paper presents a human soft tissue simulation method based on mass-spring model. Applied weight vector into Euler integration method, the dynamic equation is worked out, then the process of deformation can be expressed. Experimental results show that the model creates realistic simulations and has a fast speed.",
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"title": "Mathematical modeling of vehicle frontal crash by a double spring-mass-damper model",
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"abstract": "This paper presents development of a mathematical model to represent the real vehicle frontal crash scenario. The vehicle is modeled by a double spring-mass-damper system. The front mass m1 represents the chassi of the vehicle and rear mass m2 represents the passenger compartment. The physical parameters of the model (Stiffness and dampers) are estimated using Nonlinear least square method (Levenberg-Marquart algorithm) by curve fitting the response of a double spring-mass-damper system to the experimental displacement data from the real vehicle crash. The model is validated by comparing the results from the model with the experimental results from real crash tests available.",
"abstracts": [
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"content": "This paper presents development of a mathematical model to represent the real vehicle frontal crash scenario. The vehicle is modeled by a double spring-mass-damper system. The front mass m1 represents the chassi of the vehicle and rear mass m2 represents the passenger compartment. The physical parameters of the model (Stiffness and dampers) are estimated using Nonlinear least square method (Levenberg-Marquart algorithm) by curve fitting the response of a double spring-mass-damper system to the experimental displacement data from the real vehicle crash. The model is validated by comparing the results from the model with the experimental results from real crash tests available.",
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"fno": "06684071",
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"authors": [
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"affiliation": "Dept. of Eng., Univ. of Agder, Grimstad, Norway",
"fullName": "Bernard B. Mnyazikwiye",
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"affiliation": "Dept. of Eng., Univ. of Agder, Grimstad, Norway",
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"abstract": "In this paper, the nonlinear dynamic characteristics of vehicle semi-active suspension system with SMA spring was studied. SMA spring was applied in semi-active suspension system to substitute ER and MR fluid to provide controllable force. Hysteretic cycle and multinomial model were introduced to set up a new kind of continuous SMA strain-stress-temperature model, based on which the nonlinear dynamic model of vehicle semi-active suspension system with SMA spring was set up. The first-order nonlinear approximate solution of suspension system was given, and based on which the stability and bifurcation characteristic of suspension system were analyzed. The result of analysis shows that the nonlinear stiffness parameters can not cause the bifurcation of suspension system, and the qualitative change of the dynamic characteristic of suspension system has relationship with the nonlinear damping parameters. Finally, the result of analysis was proved by simulation.",
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"content": "In this paper, the nonlinear dynamic characteristics of vehicle semi-active suspension system with SMA spring was studied. SMA spring was applied in semi-active suspension system to substitute ER and MR fluid to provide controllable force. Hysteretic cycle and multinomial model were introduced to set up a new kind of continuous SMA strain-stress-temperature model, based on which the nonlinear dynamic model of vehicle semi-active suspension system with SMA spring was set up. The first-order nonlinear approximate solution of suspension system was given, and based on which the stability and bifurcation characteristic of suspension system were analyzed. The result of analysis shows that the nonlinear stiffness parameters can not cause the bifurcation of suspension system, and the qualitative change of the dynamic characteristic of suspension system has relationship with the nonlinear damping parameters. Finally, the result of analysis was proved by simulation.",
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"abstract": "Photorealistic Augmented Reality (AR) requires knowledge of the scene geometry and environment lighting to compute photometric registration. Recent work has introduced probeless photometric registration, where environment lighting is estimated directly from observations of reflections in the scene rather than through an invasive probe such as a reflective ball. However, computing the dense radiance transfer of a dynamically changing scene is computationally challenging. In this work, we present an improved radiance transfer sampling approach, which combines adaptive sampling in image and visibility space with robust caching of radiance transfer to yield real time framerates for photorealistic AR scenes with dynamically changing scene geometry and environment lighting.",
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"content": "Photorealistic Augmented Reality (AR) requires knowledge of the scene geometry and environment lighting to compute photometric registration. Recent work has introduced probeless photometric registration, where environment lighting is estimated directly from observations of reflections in the scene rather than through an invasive probe such as a reflective ball. However, computing the dense radiance transfer of a dynamically changing scene is computationally challenging. In this work, we present an improved radiance transfer sampling approach, which combines adaptive sampling in image and visibility space with robust caching of radiance transfer to yield real time framerates for photorealistic AR scenes with dynamically changing scene geometry and environment lighting.",
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"authors": [
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"affiliation": "Graz University of Technology",
"fullName": "Lukas Gruber",
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"affiliation": "Graz University of Technology",
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"affiliation": "Graz University of Technology",
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"doi": "10.1109/VISUAL.1998.745290",
"title": "Extremal Feature Extraction from 3-D Vector and Noisy Scalar Fields",
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"abstract": "We are interested in feature extraction from volume data in terms of coherent surfaces and 3-D space curves. The input can be an inaccurate scalar or vector field, sampled densely or sparsely on a regular 3-D grid, in which poor resolution and the presence of spurious noisy samples make traditional iso-surface techniques inappropriate. In this paper, we present a general-purpose methodology to extract surfaces or curves from a digital 3-D potential vector field { (s,\\overline v )} in which each voxel holds a scalar s designating strength, and a vector \\overline v indicating direction. For scalar, sparse or low resolution data,we \"vectorize\" and \"densify\" the volume by Tensor Voting to produce dense vector fields suitable as input to our algorithms, the Extremal Surface and Curve Algorithms. Both algorithms extract, with sub-voxel precision, coherent features representing local extrema in the given vector field. These coherent features are a hole-free triangulation mesh (in the surface case), and a set of connected, oriented, and non-intersecting polyline segments (in the curve case). We demonstrate the general usefulness of both extremal algorithms on a variety of real data by properly extracting their inherent extremal properties, such as (a) shock waves induced by abrupt velocity or direction changes in a flow field, (b) interacting vortex cores and vorticity lines in a velocity field, (c) crestlines and ridges implicit in a digital terrain map, and (d) grooves, anatomical lines and complex surfaces from noisy dental data.",
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"content": "We are interested in feature extraction from volume data in terms of coherent surfaces and 3-D space curves. The input can be an inaccurate scalar or vector field, sampled densely or sparsely on a regular 3-D grid, in which poor resolution and the presence of spurious noisy samples make traditional iso-surface techniques inappropriate. In this paper, we present a general-purpose methodology to extract surfaces or curves from a digital 3-D potential vector field { (s,\\overline v )} in which each voxel holds a scalar s designating strength, and a vector \\overline v indicating direction. For scalar, sparse or low resolution data,we \"vectorize\" and \"densify\" the volume by Tensor Voting to produce dense vector fields suitable as input to our algorithms, the Extremal Surface and Curve Algorithms. Both algorithms extract, with sub-voxel precision, coherent features representing local extrema in the given vector field. These coherent features are a hole-free triangulation mesh (in the surface case), and a set of connected, oriented, and non-intersecting polyline segments (in the curve case). We demonstrate the general usefulness of both extremal algorithms on a variety of real data by properly extracting their inherent extremal properties, such as (a) shock waves induced by abrupt velocity or direction changes in a flow field, (b) interacting vortex cores and vorticity lines in a velocity field, (c) crestlines and ridges implicit in a digital terrain map, and (d) grooves, anatomical lines and complex surfaces from noisy dental data.",
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"abstract": "Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.",
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"content": "Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.",
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"abstract": "Vection is defined as the compelling sensation of illusory self- motion elicited by a moving sensory, usually visual, stimulus. This paper presents collected introspective data on the experience of linear, circular, and curvilinear vection. We evaluate the differences between twelve different trajectories and the influence of the floor projection on the illusion of self-motion. All of the simulated self- motions examined are of a constant velocity, except for a brief simulated initial acceleration. First, we find that linear translations to the left and right are perceived as the least convincing, while linear down is perceived as the most convincing of the linear trajectories. Second, we find that the floor projection significantly improves the introspective measures of linear vection experienced in a photorealistic three-dimensional town. Finally, we find that while linear forward vection is not perceived to be very convincing, curvilinear forward vection is reported to be as convincing as circular vection. Considering our experimental results, our suggestions for simulators and VE applications where vection is desirable is to increase the number of curvilinear trajectories (as opposed to linear ones) and, if possible, add floor projection in order to improve the illusory sense of self-motion.",
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"abstract": "With fast development of graphics hardware, the current algorithms on rendering of landscape and seascape, which are focused on precise view-frustum culling and complicated decision of level of detail (LOD), lead the CPU to be the bottleneck of system. This paper presents a new framework based on some new data structures and new rendering algorithms for navigation and rendering of very large-scale landscape and seascape. Firstly, we construct a new terrain data model, which consists of Terrain Tile Pyramid and Terrain Summary Pyramid. Secondly, in order to stitch the terrain tile boundaries seamlessly, the Index Template is introduced, and an algorithm of LOD based on \"Remarkability\" is described detailedly. Finally, Perlin noise is used to simulate the ocean surface and a framework of navigation of very large-scale scene is completed. Experimental results show that this system can satisfy the requirements of real-time navigation of large-scale landscape and seascape.",
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"content": "With fast development of graphics hardware, the current algorithms on rendering of landscape and seascape, which are focused on precise view-frustum culling and complicated decision of level of detail (LOD), lead the CPU to be the bottleneck of system. This paper presents a new framework based on some new data structures and new rendering algorithms for navigation and rendering of very large-scale landscape and seascape. Firstly, we construct a new terrain data model, which consists of Terrain Tile Pyramid and Terrain Summary Pyramid. Secondly, in order to stitch the terrain tile boundaries seamlessly, the Index Template is introduced, and an algorithm of LOD based on \"Remarkability\" is described detailedly. Finally, Perlin noise is used to simulate the ocean surface and a framework of navigation of very large-scale scene is completed. Experimental results show that this system can satisfy the requirements of real-time navigation of large-scale landscape and seascape.",
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"title": "RSF: A Novel Saliency Fusion Framework for Image Saliency Detection",
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"abstract": "Image saliency detection has become a hot topic in computer vision tasks. A large number of saliency models have been developed in recent years. Considering that these models use different prior knowledge, features, and theoretical methods, they have their own strengths and weaknesses in different images. Based on this observation, some works aim to fuse multiple weak saliency models based on different fusion strategies (Each saliency model in fusion framework is called a weak saliency model). However, these fusion methods lose effectiveness when image content is very complex, because they ignore the fact that various regions in an image have different characteristics in saliency fusion. Different with them, we propose a novel Region-level Saliency Fusion framework (RSF) by exploring the relationship between weak saliency models and image regions. For the input image and J weak saliency models, we firstly segment input image into N regions. Then, our goal is to learn to infer the reliability of using each weak saliency model to predict each image region saliency value. This way, we can select more reliable weak saliency models for each region to predict its saliency value. Finally, we use smoothness prior to further smooth the saliency map obtained by RSF. Experimental results on three datasets demonstrate the superiority of the proposed method than other state-of-the-art methods.",
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"content": "Image saliency detection has become a hot topic in computer vision tasks. A large number of saliency models have been developed in recent years. Considering that these models use different prior knowledge, features, and theoretical methods, they have their own strengths and weaknesses in different images. Based on this observation, some works aim to fuse multiple weak saliency models based on different fusion strategies (Each saliency model in fusion framework is called a weak saliency model). However, these fusion methods lose effectiveness when image content is very complex, because they ignore the fact that various regions in an image have different characteristics in saliency fusion. Different with them, we propose a novel Region-level Saliency Fusion framework (RSF) by exploring the relationship between weak saliency models and image regions. For the input image and J weak saliency models, we firstly segment input image into N regions. Then, our goal is to learn to infer the reliability of using each weak saliency model to predict each image region saliency value. This way, we can select more reliable weak saliency models for each region to predict its saliency value. Finally, we use smoothness prior to further smooth the saliency map obtained by RSF. Experimental results on three datasets demonstrate the superiority of the proposed method than other state-of-the-art methods.",
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"content": "With the proliferation of multimedia data and evergrowing requests for multimedia applications, new challenges are emerged for efficient and effective managing and accessing large audio-visual collections. In this paper, we present a novel framework for video event detection, which plays an essential role in high-level video indexing and retrieval. Especially, since temporal information in a video sequence is critical in conveying video content, a hierarchical temporal association mining approach is developed to systematically capture the characteristic temporal patterns with respect to the events of interest. In this process, the unique challenges caused by the loose video structure and skewed data distribution issues are effectively tackled. In addition, an adaptive mechanism is proposed to determine the essential thresholds which are generally defined manually in the traditional association rule mining (ARM) approach. This framework thus largely relaxes the dependence on the domain knowledge and contributes to the ultimate goal of automatic video content analysis.",
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"abstract": "Large software systems have a rich development history. Mining certain aspects of this rich history can reveal interesting insights into the system and its structure. Previous approaches to visualize the evolution of software systems provide static views. These static views often do not fully capture the dynamic nature of evolution. We introduce the Evolution Storyboard, a visualization which provides dynamic views of the evolution of a software?s structure. Our tool implementation takes as input a series of software graphs, e.g., call graphs or co-change graphs, and automatically generates an evolution storyboard. To illustrate the concept, we present a storyboard for PostgreSQL, as a representative example for large open source systems. Evolution storyboards help to understand a system?s structure and to reveal its possible decay over time. The storyboard highlights important changes in the structure during the lifetime of a software system, and how artifacts changed their dependencies over time.",
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"abstract": "We analyze the effect of perturbations on the estimation of Depth from Defocus (DFD) implemented by changing the focus setting (e.g., axially moving the sensor). The analysis yields the optimal change of focus setting, and the spatial frequencies for which estimation is most robust. For stable estimation at all spatial frequencies, the change in focus setting should be less than twice the depth of field. For the most robust estimation in the highest spatial frequencies the axial interval should be equal to the depth of field.",
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"abstract": "Currently, many visualization methods are used in computer assisted medicine. It is commonly considered that a unique visualization scheme makes difficult the interaction and limits the quality and quantity of the information shown. In this paper we study the specific requirements of a maxillofacial surgery simulation tool for facial appearance prediction. The different stages of the application lead to present medical information in different ways. We propose to adapt visualization techniques to give a more suitable answer to these needs: a hybrid volumetric and polygonal visualization for the planning stage, as well as a scheme for surgery definition in 2D. Finally, we propose the use of mesh visualization for the simulated model, which previously requires the 3D reconstruction of the surface in order to be visualized.",
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"abstract": "We present a prototype of a system in development for pre-operative planning. The proposed NeuroTable uses a combination of traditional rendering and novel visualization techniques rendered to facilitate real-time collaboration between neurosurgeons during intervention planning. A set of multimodal 2D and 3D renderings convey the relation between the region of interest and the surrounding anatomical structures. A haptic device is used for interaction with the NeuroTable to facilitate immersive control over the 3D cursor and navigation modes for the neurosurgeons during their discourse of planning. A pilot experimental study was conducted to assess the performance of users in targeting points within the preoperative 3D scan. Then, two clinicians participated in the evaluation of the table in discussing and planning a case. Results indicate that the NeuroTable facilitated the discourse and we discuss the results of the speed and accuracy for the specification of entry and target points.",
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"affiliation": "University of Western Ontario",
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"content": "Three dimensional (3D) visualization of anatomy plays an important role in image guided orthopedic surgery and ultimately motivates minimally invasive procedures. However, direct 3D imaging modalities such as Computed Tomography (CT) are restricted to a minority of complex orthopedic procedures. Thus the diagnostics and planning of many interventions still rely on two dimensional (2D) radiographic images, where the surgeon has to mentally visualize the anatomy of interest. The purpose of this paper is to apply and validate a bi-planar 3D reconstruction methodology driven by prominent bony anatomy edges and contours identified on orthogonal radiographs. The results obtained through the proposed methodology are benchmarked against 3D CT scan data to assess the accuracy of reconstruction. The human femur has been used as the anatomy of interest throughout the paper. The novelty of this methodology is that it not only involves the outer contours of the bony anatomy in the reconstruction but also several key interior edges identifiable on radiographic images. Hence, this framework is not simply limited to long bones, but is generally applicable to a multitude of other bony anatomies as illustrated in the results section.",
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