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26,502 | Compressive Holographic Video | cs.CV | Compressed sensing has been discussed separately in spatial and temporal
domains. Compressive holography has been introduced as a method that allows 3D
tomographic reconstruction at different depths from a single 2D image. Coded
exposure is a temporal compressed sensing method for high speed video
acquisition. In this ... | computer science |
26,503 | Icon: An Interactive Approach to Train Deep Neural Networks for
Segmentation of Neuronal Structures | cs.CV | We present an interactive approach to train a deep neural network pixel
classifier for the segmentation of neuronal structures. An interactive training
scheme reduces the extremely tedious manual annotation task that is typically
required for deep networks to perform well on image segmentation problems. Our
proposed me... | computer science |
26,504 | Recent advances in content based video copy detection | cs.CV | With the immense number of videos being uploaded to the video sharing sites,
issue of copyright infringement arises with uploading of illicit copies or
transformed versions of original video. Thus safeguarding copyright of digital
media has become matter of concern. To address this concern, it is obliged to
have a vide... | computer science |
26,505 | Towards automatic pulmonary nodule management in lung cancer screening
with deep learning | cs.CV | The introduction of lung cancer screening programs will produce an
unprecedented amount of chest CT scans in the near future, which radiologists
will have to read in order to decide on a patient follow-up strategy. According
to the current guidelines, the workup of screen-detected nodules strongly
relies on nodule size... | computer science |
26,506 | Judging a Book By its Cover | cs.CV | Book covers communicate information to potential readers, but can that same
information be learned by computers? We propose using a deep Convolutional
Neural Network (CNN) to predict the genre of a book based on the visual clues
provided by its cover. The purpose of this research is to investigate whether
relationships... | computer science |
26,507 | Learnable Visual Markers | cs.CV | We propose a new approach to designing visual markers (analogous to QR-codes,
markers for augmented reality, and robotic fiducial tags) based on the advances
in deep generative networks. In our approach, the markers are obtained as color
images synthesized by a deep network from input bit strings, whereas another
deep ... | computer science |
26,508 | The TUM LapChole dataset for the M2CAI 2016 workflow challenge | cs.CV | In this technical report we present our collected dataset of laparoscopic
cholecystectomies (LapChole). Laparoscopic videos of a total of 20 surgeries
were recorded and annotated with surgical phase labels, of which 15 were
randomly pre-determined as training data, while the remaining 5 videos are
selected as test data... | computer science |
26,509 | Real-time Online Action Detection Forests using Spatio-temporal Contexts | cs.CV | Online action detection (OAD) is challenging since 1) robust yet
computationally expensive features cannot be straightforwardly used due to the
real-time processing requirements and 2) the localization and classification of
actions have to be performed even before they are fully observed. We propose a
new random forest... | computer science |
26,510 | Learning Adaptive Parameter Tuning for Image Processing | cs.CV | The non-stationary nature of image characteristics calls for adaptive
processing, based on the local image content. We propose a simple and flexible
method to learn local tuning of parameters in adaptive image processing: we
extract simple local features from an image and learn the relation between
these features and t... | computer science |
26,511 | Selective De-noising of Sparse-Coloured Images | cs.CV | Since time immemorial, noise has been a constant source of disturbance to the
various entities known to mankind. Noise models of different kinds have been
developed to study noise in more detailed fashion over the years. Image
processing, particularly, has extensively implemented several algorithms to
reduce noise in p... | computer science |
26,512 | A MAP-MRF filter for phase-sensitive coil combination in autocalibrating
partially parallel susceptibility weighted MRI | cs.CV | A statistical approach for combination of channel phases is developed for
optimizing the Contrast-to-Noise Ratio (CNR) in Susceptibility Weighted Images
(SWI) acquired using autocalibrating partially parallel techniques. The
unwrapped phase images of each coil are filtered using local random field based
probabilistic w... | computer science |
26,513 | Multi-Camera Occlusion and Sudden-Appearance-Change Detection Using
Hidden Markovian Chains | cs.CV | This paper was originally submitted to Xinova as a response to a Request for
Invention (RFI) on new event monitoring methods. In this paper, a new object
tracking algorithm using multiple cameras for surveillance applications is
proposed. The proposed system can detect sudden-appearance-changes and
occlusions using a h... | computer science |
26,514 | Diversity Promoting Online Sampling for Streaming Video Summarization | cs.CV | Many applications benefit from sampling algorithms where a small number of
well chosen samples are used to generalize different properties of a large
dataset. In this paper, we use diverse sampling for streaming video
summarization. Several emerging applications support streaming video, but
existing summarization algor... | computer science |
26,515 | Compressed Learning: A Deep Neural Network Approach | cs.CV | Compressed Learning (CL) is a joint signal processing and machine learning
framework for inference from a signal, using a small number of measurements
obtained by linear projections of the signal. In this paper we present an
end-to-end deep learning approach for CL, in which a network composed of
fully-connected layers... | computer science |
26,516 | Accurate Deep Representation Quantization with Gradient Snapping Layer
for Similarity Search | cs.CV | Recent advance of large scale similarity search involves using deeply learned
representations to improve the search accuracy and use vector quantization
methods to increase the search speed. However, how to learn deep
representations that strongly preserve similarities between data pairs and can
be accurately quantized... | computer science |
26,517 | Visual Tracking via Boolean Map Representations | cs.CV | In this paper, we present a simple yet effective Boolean map based
representation that exploits connectivity cues for visual tracking. We describe
a target object with histogram of oriented gradients and raw color features, of
which each one is characterized by a set of Boolean maps generated by uniformly
thresholding ... | computer science |
26,518 | Real-Time Image Distortion Correction: Analysis and Evaluation of
FPGA-Compatible Algorithms | cs.CV | Image distortion correction is a critical pre-processing step for a variety
of computer vision and image processing algorithms. Standard real-time software
implementations are generally not suited for direct hardware porting, so
appropriated versions need to be designed in order to obtain implementations
deployable on ... | computer science |
26,519 | A deep convolutional neural network using directional wavelets for
low-dose X-ray CT reconstruction | cs.CV | Due to the potential risk of inducing cancers, radiation dose of X-ray CT
should be reduced for routine patient scanning. However, in low-dose X-ray CT,
severe artifacts usually occur due to photon starvation, beamhardening, etc,
which decrease the reliability of diagnosis. Thus, high quality reconstruction
from low-do... | computer science |
26,520 | A New Distance Measure for Non-Identical Data with Application to Image
Classification | cs.CV | Distance measures are part and parcel of many computer vision algorithms. The
underlying assumption in all existing distance measures is that feature
elements are independent and identically distributed. However, in real-world
settings, data generally originate from heterogeneous sources even if they do
possess a commo... | computer science |
26,521 | Robust Gait Recognition by Integrating Inertial and RGBD Sensors | cs.CV | Gait has been considered as a promising and unique biometric for person
identification. Traditionally, gait data are collected using either color
sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or
inertial sensors, such as an accelerometer. However, a single type of sensors
may only capture pa... | computer science |
26,522 | A Detailed Rubric for Motion Segmentation | cs.CV | Motion segmentation is currently an active area of research in computer
Vision. The task of comparing different methods of motion segmentation is
complicated by the fact that researchers may use subtly different definitions
of the problem. Questions such as "Which objects are moving?", "What is
background?", and "How c... | computer science |
26,523 | Bi-modal First Impressions Recognition using Temporally Ordered Deep
Audio and Stochastic Visual Features | cs.CV | We propose a novel approach for First Impressions Recognition in terms of the
Big Five personality-traits from short videos. The Big Five personality traits
is a model to describe human personality using five broad categories:
Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness. We
train two bi-mod... | computer science |
26,524 | A Benchmark Dataset and Saliency-guided Stacked Autoencoders for
Video-based Salient Object Detection | cs.CV | Image-based salient object detection (SOD) has been extensively studied in
the past decades. However, video-based SOD is much less explored since there
lack large-scale video datasets within which salient objects are unambiguously
defined and annotated. Toward this end, this paper proposes a video-based SOD
dataset tha... | computer science |
26,525 | Deep fusion of visual signatures for client-server facial analysis | cs.CV | Facial analysis is a key technology for enabling human-machine interaction.
In this context, we present a client-server framework, where a client transmits
the signature of a face to be analyzed to the server, and, in return, the
server sends back various information describing the face e.g. is the person
male or femal... | computer science |
26,526 | Best-Buddies Tracking | cs.CV | Best-Buddies Tracking (BBT) applies the Best-Buddies Similarity measure (BBS)
to the problem of model-free online tracking. BBS was introduced as a
similarity measure between two point sets and was shown to be very effective
for template matching. Originally, BBS was designed to work with point sets of
equal size, and ... | computer science |
26,527 | Sliding Dictionary Based Sparse Representation For Action Recognition | cs.CV | The task of action recognition has been in the forefront of research, given
its applications in gaming, surveillance and health care. In this work, we
propose a simple, yet very effective approach which works seamlessly for both
offline and online action recognition using the skeletal joints. We construct a
sliding dic... | computer science |
26,528 | Dictionary Integration using 3D Morphable Face Models for Pose-invariant
Collaborative-representation-based Classification | cs.CV | The paper presents a dictionary integration algorithm using 3D morphable face
models (3DMM) for pose-invariant collaborative-representation-based face
classification. To this end, we first fit a 3DMM to the 2D face images of a
dictionary to reconstruct the 3D shape and texture of each image. The 3D faces
are used to re... | computer science |
26,529 | Combining Multiple Cues for Visual Madlibs Question Answering | cs.CV | This paper presents an approach for answering fill-in-the-blank multiple
choice questions from the Visual Madlibs dataset. Instead of generic and
commonly used representations trained on the ImageNet classification task, our
approach employs a combination of networks trained for specialized tasks such
as scene recognit... | computer science |
26,530 | Flood-Filling Networks | cs.CV | State-of-the-art image segmentation algorithms generally consist of at least
two successive and distinct computations: a boundary detection process that
uses local image information to classify image locations as boundaries between
objects, followed by a pixel grouping step such as watershed or connected
components tha... | computer science |
26,531 | CRF-CNN: Modeling Structured Information in Human Pose Estimation | cs.CV | Deep convolutional neural networks (CNN) have achieved great success. On the
other hand, modeling structural information has been proved critical in many
vision problems. It is of great interest to integrate them effectively. In a
classical neural network, there is no message passing between neurons in the
same layer. ... | computer science |
26,532 | Dual Attention Networks for Multimodal Reasoning and Matching | cs.CV | We propose Dual Attention Networks (DANs) which jointly leverage visual and
textual attention mechanisms to capture fine-grained interplay between vision
and language. DANs attend to specific regions in images and words in text
through multiple steps and gather essential information from both modalities.
Based on this ... | computer science |
26,533 | Wearable Vision Detection of Environmental Fall Risks using
Convolutional Neural Networks | cs.CV | In this paper, a method to detect environmental hazards related to a fall
risk using a mobile vision system is proposed. First-person perspective videos
are proposed to provide objective evidence on cause and circumstances of
perturbed balance during activities of daily living, targeted to seniors. A
classification pro... | computer science |
26,534 | Learning Deep Embeddings with Histogram Loss | cs.CV | We suggest a loss for learning deep embeddings. The new loss does not
introduce parameters that need to be tuned and results in very good embeddings
across a range of datasets and problems. The loss is computed by estimating two
distribution of similarities for positive (matching) and negative
(non-matching) sample pai... | computer science |
26,535 | Optical Flow Estimation using a Spatial Pyramid Network | cs.CV | We learn to compute optical flow by combining a classical spatial-pyramid
formulation with deep learning. This estimates large motions in a
coarse-to-fine approach by warping one image of a pair at each pyramid level by
the current flow estimate and computing an update to the flow. Instead of the
standard minimization ... | computer science |
26,536 | An All-In-One Convolutional Neural Network for Face Analysis | cs.CV | We present a multi-purpose algorithm for simultaneous face detection, face
alignment, pose estimation, gender recognition, smile detection, age estimation
and face recognition using a single deep convolutional neural network (CNN).
The proposed method employs a multi-task learning framework that regularizes
the shared ... | computer science |
26,537 | Rough Set Based Color Channel Selection | cs.CV | Color channel selection is essential for accurate segmentation of sky and
clouds in images obtained from ground-based sky cameras. Most prior works in
cloud segmentation use threshold based methods on color channels selected in an
ad-hoc manner. In this letter, we propose the use of rough sets for color
channel selecti... | computer science |
26,538 | Adaptive mixed norm optical flow estimation | cs.CV | The pel-recursive computation of 2-D optical flow has been extensively
studied in computer vision to estimate motion from image sequences, but it
still raises a wealth of issues, such as the treatment of outliers, motion
discontinuities and occlusion. It relies on spatio-temporal brightness
variations due to motion. Ou... | computer science |
26,539 | Integrating Atlas and Graph Cut Methods for LV Segmentation from Cardiac
Cine MRI | cs.CV | Magnetic Resonance Imaging (MRI) has evolved as a clinical standard-of-care
imaging modality for cardiac morphology, function assessment, and guidance of
cardiac interventions. All these applications rely on accurate extraction of
the myocardial tissue and blood pool from the imaging data. Here we propose a
framework f... | computer science |
26,540 | Regularized Pel-Recursive Motion Estimation Using Generalized
Cross-Validation and Spatial Adaptation | cs.CV | The computation of 2-D optical flow by means of regularized pel-recursive
algorithms raises a host of issues, which include the treatment of outliers,
motion discontinuities and occlusion among other problems. We propose a new
approach which allows us to deal with these issues within a common framework.
Our approach is... | computer science |
26,541 | Nonnegative Matrix Underapproximation for Robust Multiple Model Fitting | cs.CV | In this work, we introduce a highly efficient algorithm to address the
nonnegative matrix underapproximation (NMU) problem, i.e., nonnegative matrix
factorization (NMF) with an additional underapproximation constraint. NMU
results are interesting as, compared to traditional NMF, they present
additional sparsity and par... | computer science |
26,542 | STDP-based spiking deep convolutional neural networks for object
recognition | cs.CV | Previous studies have shown that spike-timing-dependent plasticity (STDP) can
be used in spiking neural networks (SNN) to extract visual features of low or
intermediate complexity in an unsupervised manner. These studies, however, used
relatively shallow architectures, and only one layer was trainable. Another
line of ... | computer science |
26,543 | UMDFaces: An Annotated Face Dataset for Training Deep Networks | cs.CV | Recent progress in face detection (including keypoint detection), and
recognition is mainly being driven by (i) deeper convolutional neural network
architectures, and (ii) larger datasets. However, most of the large datasets
are maintained by private companies and are not publicly available. The
academic computer visio... | computer science |
26,544 | Efficient Branching Cascaded Regression for Face Alignment under
Significant Head Rotation | cs.CV | Despite much interest in face alignment in recent years, the large majority
of work has focused on near-frontal faces. Algorithms typically break down on
profile faces, or are too slow for real-time applications. In this work we
propose an efficient approach to face alignment that can handle 180 degrees of
head rotatio... | computer science |
26,545 | What Is the Best Practice for CNNs Applied to Visual Instance Retrieval? | cs.CV | Previous work has shown that feature maps of deep convolutional neural
networks (CNNs) can be interpreted as feature representation of a particular
image region. Features aggregated from these feature maps have been exploited
for image retrieval tasks and achieved state-of-the-art performances in recent
years. The key ... | computer science |
26,546 | GPU-based Pedestrian Detection for Autonomous Driving | cs.CV | We propose a real-time pedestrian detection system for the embedded Nvidia
Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following
state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and
Histograms of Oriented Gradients (HOG) features extracted from the input image;
Pyramidal Slidi... | computer science |
26,547 | Boosting Image Captioning with Attributes | cs.CV | Automatically describing an image with a natural language has been an
emerging challenge in both fields of computer vision and natural language
processing. In this paper, we present Long Short-Term Memory with Attributes
(LSTM-A) - a novel architecture that integrates attributes into the successful
Convolutional Neural... | computer science |
26,548 | Validation of Tsallis Entropy In Inter-Modality Neuroimage Registration | cs.CV | Medical image registration plays an important role in determining topographic
and morphological changes for functional diagnostic and therapeutic purposes.
Manual alignment and semi-automated software still have been used; however they
are subjective and make specialists spend precious time. Fully automated
methods are... | computer science |
26,549 | Deep Label Distribution Learning with Label Ambiguity | cs.CV | Convolutional Neural Networks (ConvNets) have achieved excellent recognition
performance in various visual recognition tasks. A large labeled training set
is one of the most important factors for its success. However, it is difficult
to collect sufficient training images with precise labels in some domains such
as appa... | computer science |
26,550 | Deep Convolutional Neural Network Features and the Original Image | cs.CV | Face recognition algorithms based on deep convolutional neural networks
(DCNNs) have made progress on the task of recognizing faces in unconstrained
viewing conditions. These networks operate with compact feature-based face
representations derived from learning a very large number of face images. While
the learned feat... | computer science |
26,551 | The Shallow End: Empowering Shallower Deep-Convolutional Networks
through Auxiliary Outputs | cs.CV | The depth is one of the key factors behind the great success of convolutional
neural networks (CNNs), with the gradient vanishing issue having been largely
addressed by various nets, e.g. ResNet. However, when the depth goes very deep,
the supervision information from the loss function will vanish due to the long
backp... | computer science |
26,552 | Action2Activity: Recognizing Complex Activities from Sensor Data | cs.CV | As compared to simple actions, activities are much more complex, but
semantically consistent with a human's real life. Techniques for action
recognition from sensor generated data are mature. However, there has been
relatively little work on bridging the gap between actions and activities. To
this end, this paper prese... | computer science |
26,553 | High-Resolution Semantic Labeling with Convolutional Neural Networks | cs.CV | Convolutional neural networks (CNNs) have received increasing attention over
the last few years. They were initially conceived for image categorization,
i.e., the problem of assigning a semantic label to an entire input image.
In this paper we address the problem of dense semantic labeling, which
consists in assignin... | computer science |
26,554 | Chinese/English mixed Character Segmentation as Semantic Segmentation | cs.CV | OCR character segmentation for multilingual printed documents is difficult
due to the diversity of different linguistic characters. Previous approaches
mainly focus on monolingual texts and are not suitable for multilingual-lingual
cases. In this work, we particularly tackle the Chinese/English mixed case by
reframing ... | computer science |
26,555 | A Fully Convolutional Neural Network based Structured Prediction
Approach Towards the Retinal Vessel Segmentation | cs.CV | Automatic segmentation of retinal blood vessels from fundus images plays an
important role in the computer aided diagnosis of retinal diseases. The task of
blood vessel segmentation is challenging due to the extreme variations in
morphology of the vessels against noisy background. In this paper, we formulate
the segmen... | computer science |
26,556 | Texture and Color-based Image Retrieval Using the Local Extrema Features
and Riemannian Distance | cs.CV | A novel efficient method for content-based image retrieval (CBIR) is
developed in this paper using both texture and color features. Our motivation
is to represent and characterize an input image by a set of local descriptors
extracted at characteristic points (i.e. keypoints) within the image. Then,
dissimilarity measu... | computer science |
26,557 | Spatiotemporal Residual Networks for Video Action Recognition | cs.CV | Two-stream Convolutional Networks (ConvNets) have shown strong performance
for human action recognition in videos. Recently, Residual Networks (ResNets)
have arisen as a new technique to train extremely deep architectures. In this
paper, we introduce spatiotemporal ResNets as a combination of these two
approaches. Our ... | computer science |
26,558 | Unsupervised Cross-Domain Image Generation | cs.CV | We study the problem of transferring a sample in one domain to an analog
sample in another domain. Given two related domains, S and T, we would like to
learn a generative function G that maps an input sample from S to the domain T,
such that the output of a given function f, which accepts inputs in either
domains, woul... | computer science |
26,559 | Meat adulteration detection through digital image analysis of
histological cuts using LBP | cs.CV | Food fraud has been an area of great concern due to its risk to public
health, reduction of food quality or nutritional value and for its economic
consequences. For this reason, it's been object of regulation in many countries
(e.g. [1], [2]). One type of food that has been frequently object of fraud
through the additi... | computer science |
26,560 | Quantum spectral analysis: frequency in time, with applications to
signal and image processing | cs.CV | A quantum time-dependent spectrum analysis, or simply, quantum spectral
analysis (QSA) is presented in this work, and it is based on Schrodinger
equation, which is a partial differential equation that describes how the
quantum state of a non-relativistic physical system changes with time. In
classic world is named freq... | computer science |
26,561 | Multiple Object Tracking with Kernelized Correlation Filters in Urban
Mixed Traffic | cs.CV | Recently, the Kernelized Correlation Filters tracker (KCF) achieved
competitive performance and robustness in visual object tracking. On the other
hand, visual trackers are not typically used in multiple object tracking. In
this paper, we investigate how a robust visual tracker like KCF can improve
multiple object trac... | computer science |
26,562 | Action Recognition Based on Joint Trajectory Maps Using Convolutional
Neural Networks | cs.CV | Recently, Convolutional Neural Networks (ConvNets) have shown promising
performances in many computer vision tasks, especially image-based recognition.
How to effectively use ConvNets for video-based recognition is still an open
problem. In this paper, we propose a compact, effective yet simple method to
encode spatio-... | computer science |
26,563 | The Loss Surface of Residual Networks: Ensembles and the Role of Batch
Normalization | cs.CV | Deep Residual Networks present a premium in performance in comparison to
conventional networks of the same depth and are trainable at extreme depths. It
has recently been shown that Residual Networks behave like ensembles of
relatively shallow networks. We show that these ensembles are dynamic: while
initially the virt... | computer science |
26,564 | Estimating motion with principal component regression strategies | cs.CV | In this paper, two simple principal component regression methods for
estimating the optical flow between frames of video sequences according to a
pel-recursive manner are introduced. These are easy alternatives to dealing
with mixtures of motion vectors in addition to the lack of prior information on
spatial-temporal s... | computer science |
26,565 | Multispectral Deep Neural Networks for Pedestrian Detection | cs.CV | Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedest... | computer science |
26,566 | Robust Cardiac Motion Estimation using Ultrafast Ultrasound Data: A
Low-Rank-Topology-Preserving Approach | cs.CV | Cardiac motion estimation is an important diagnostic tool to detect heart
diseases and it has been explored with modalities such as MRI and conventional
ultrasound (US) sequences. US cardiac motion estimation still presents
challenges because of the complex motion patterns and the presence of noise. In
this work, we pr... | computer science |
26,567 | A backward pass through a CNN using a generative model of its
activations | cs.CV | Neural networks have shown to be a practical way of building a very complex
mapping between a pre-specified input space and output space. For example, a
convolutional neural network (CNN) mapping an image into one of a thousand
object labels is approaching human performance in this particular task. However
the mapping ... | computer science |
26,568 | Deep Convolutional Neural Network for 6-DOF Image Localization | cs.CV | We present an accurate and robust method for six degree of freedom image
localization. There are two key-points of our method, 1. automatic immense
photo synthesis and labeling from point cloud model and, 2. pose estimation
with deep convolutional neural networks regression. Our model can directly
regresses 6-DOF camer... | computer science |
26,569 | Generative Shape Models: Joint Text Recognition and Segmentation with
Very Little Training Data | cs.CV | We demonstrate that a generative model for object shapes can achieve state of
the art results on challenging scene text recognition tasks, and with orders of
magnitude fewer training images than required for competing discriminative
methods. In addition to transcribing text from challenging images, our method
performs ... | computer science |
26,570 | Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard
Species using Artificial Vision Algorithms | cs.CV | Animal biometrics is an important requirement for monitoring and conservation
tasks. The classical animal biometrics risk the animals' integrity, are
expensive for numerous animals, and depend on expert criterion. The
non-invasive biometrics techniques offer alternatives to manage the
aforementioned problems. In this p... | computer science |
26,571 | Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation
for Random Forest | cs.CV | Random Forest (RF) is a successful paradigm for learning classifiers due to
its ability to learn from large feature spaces and seamlessly integrate
multi-class classification, as well as the achieved accuracy and processing
efficiency. However, as many other classifiers, RF requires domain adaptation
(DA) provided that... | computer science |
26,572 | Optimal Multiple Surface Segmentation with Convex Priors in Irregularly
Sampled Space | cs.CV | Optimal surface segmentation is widely used in numerous medical image
segmentation applications. However, nodes in the graph based optimal surface
segmentation method typically encode uniformly distributed orthogonal voxels of
the volume. Thus the segmentation cannot attain an accuracy greater than a
single unit voxel,... | computer science |
26,573 | Real Time Video Analysis using Smart Phone Camera for Stroboscopic Image | cs.CV | Motion capturing and there by segmentation of the motion of any moving object
from a sequence of continuous images or a video is not an exceptional task in
computer vision area. Smart-phone camera application is an added integration
for the development of such tasks and it also provides for a smooth testing. A
new appr... | computer science |
26,574 | Error concealment by means of motion refinement and regularized Bregman
divergence | cs.CV | This work addresses the problem of error concealment in video transmission
systems over noisy channels employing Bregman divergences along with
regularization. Error concealment intends to improve the effects of
disturbances at the reception due to bit-errors or cell loss in packet
networks. Bregman regularization give... | computer science |
26,575 | Detecting Moving Regions in CrowdCam Images | cs.CV | We address the novel problem of detecting dynamic regions in CrowdCam images,
a set of still images captured by a group of people. These regions capture the
most interesting parts of the scene, and detecting them plays an important role
in the analysis of visual data. Our method is based on the observation that
matchin... | computer science |
26,576 | X-ray Scattering Image Classification Using Deep Learning | cs.CV | Visual inspection of x-ray scattering images is a powerful technique for
probing the physical structure of materials at the molecular scale. In this
paper, we explore the use of deep learning to develop methods for automatically
analyzing x-ray scattering images. In particular, we apply Convolutional Neural
Networks an... | computer science |
26,577 | Variables effecting photomosaic reconstruction and ortho-rectification
from aerial survey datasets | cs.CV | Unmanned aerial vehicles now make it possible to obtain high quality aerial
imagery at a low cost, but processing those images into a single, useful entity
is neither simple nor seamless. Specifically, there are factors that must be
addressed when merging multiple images into a single coherent one. While
ortho-rectific... | computer science |
26,578 | Fast Algorithm of High-resolution Microwave Imaging Using the
Non-parametric Generalized Reflectivity Model | cs.CV | This paper presents an efficient algorithm of high-resolution microwave
imaging based on the concept of generalized reflectivity. The contribution made
in this paper is two-fold. We introduce the concept of non-parametric
generalized reflectivity (GR, for short) as a function of operational
frequencies and view angles,... | computer science |
26,579 | Evaluating Urbanization from Satellite and Aerial Images by means of a
statistical approach to the texture analysis | cs.CV | Statistical methods are usually applied in the processing of digital images
for the analysis of the textures displayed by them. Aiming to evaluate the
urbanization of a given location from satellite or aerial images, here we
consider a simple processing to distinguish in them the 'urban' from the
'rural' texture. The m... | computer science |
26,580 | Construction Inspection through Spatial Database | cs.CV | This paper presents a novel pipeline for development of an efficient set of
tools for extracting information from the video of a structure, captured by an
Unmanned Aircraft System (UAS) to produce as-built documentation to aid
inspection of large multi-storied building during construction. Our system uses
the output fr... | computer science |
26,581 | Adaptive Deep Pyramid Matching for Remote Sensing Scene Classification | cs.CV | Convolutional neural networks (CNNs) have attracted increasing attention in
the remote sensing community. Most CNNs only take the last fully-connected
layers as features for the classification of remotely sensed images, discarding
the other convolutional layer features which may also be helpful for
classification purpo... | computer science |
26,582 | Learning Multi-Scale Deep Features for High-Resolution Satellite Image
Classification | cs.CV | In this paper, we propose a multi-scale deep feature learning method for
high-resolution satellite image classification. Specifically, we firstly warp
the original satellite image into multiple different scales. The images in each
scale are employed to train a deep convolutional neural network (DCNN).
However, simultan... | computer science |
26,583 | Deep Convolutional Neural Network for Inverse Problems in Imaging | cs.CV | In this paper, we propose a novel deep convolutional neural network
(CNN)-based algorithm for solving ill-posed inverse problems. Regularized
iterative algorithms have emerged as the standard approach to ill-posed inverse
problems in the past few decades. These methods produce excellent results, but
can be challenging ... | computer science |
26,584 | MCMC Shape Sampling for Image Segmentation with Nonparametric Shape
Priors | cs.CV | Segmenting images of low quality or with missing data is a challenging
problem. Integrating statistical prior information about the shapes to be
segmented can improve the segmentation results significantly. Most shape-based
segmentation algorithms optimize an energy functional and find a point estimate
for the object t... | computer science |
26,585 | Effective sparse representation of X-Ray medical images | cs.CV | Effective sparse representation of X-Ray medical images within the context of
data reduction is considered. The proposed framework is shown to render an
enormous reduction in the cardinality of the data set required to represent
this class of images at very good quality. The particularity of the approach is
that it can... | computer science |
26,586 | When Fashion Meets Big Data: Discriminative Mining of Best Selling
Clothing Features | cs.CV | With the prevalence of e-commence websites and the ease of online shopping,
consumers are embracing huge amounts of various options in products.
Undeniably, shopping is one of the most essential activities in our society and
studying consumer's shopping behavior is important for the industry as well as
sociology and ps... | computer science |
26,587 | Learning Scene-specific Object Detectors Based on a
Generative-Discriminative Model with Minimal Supervision | cs.CV | One object class may show large variations due to diverse illuminations,
backgrounds and camera viewpoints. Traditional object detection methods often
perform worse under unconstrained video environments. To address this problem,
many modern approaches model deep hierarchical appearance representations for
object detec... | computer science |
26,588 | Optimized clothes segmentation to boost gender classification in
unconstrained scenarios | cs.CV | Several applications require demographic information of ordinary people in
unconstrained scenarios. This is not a trivial task due to significant human
appearance variations. In this work, we introduce trixels for clustering image
regions, enumerating their advantages compared to superpixels. The classical
GrabCut algo... | computer science |
26,589 | Least Squares Generative Adversarial Networks | cs.CV | Unsupervised learning with generative adversarial networks (GANs) has proven
hugely successful. Regular GANs hypothesize the discriminator as a classifier
with the sigmoid cross entropy loss function. However, we found that this loss
function may lead to the vanishing gradients problem during the learning
process. To o... | computer science |
26,590 | Responses to Critiques on Machine Learning of Criminality Perceptions
(Addendum of arXiv:1611.04135) | cs.CV | In November 2016 we submitted to arXiv our paper "Automated Inference on
Criminality Using Face Images". It generated a great deal of discussions in the
Internet and some media outlets. Our work is only intended for pure academic
discussions; how it has become a media consumption is a total surprise to us.
Although in ... | computer science |
26,591 | Hand Gesture Recognition for Contactless Device Control in Operating
Rooms | cs.CV | Hand gesture is one of the most important means of touchless communication
between human and machines. There is a great interest for commanding electronic
equipment in surgery rooms by hand gesture for reducing the time of surgery and
the potential for infection. There are challenges in implementation of a hand
gesture... | computer science |
26,592 | Semi-Dense 3D Semantic Mapping from Monocular SLAM | cs.CV | The bundle of geometry and appearance in computer vision has proven to be a
promising solution for robots across a wide variety of applications. Stereo
cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and
trajectory tracking in a dense way. However, they lack flexibility of seamless
switch be... | computer science |
26,593 | Convolutional Regression for Visual Tracking | cs.CV | Recently, discriminatively learned correlation filters (DCF) has drawn much
attention in visual object tracking community. The success of DCF is
potentially attributed to the fact that a large amount of samples are utilized
to train the ridge regression model and predict the location of object. To
solve the regression ... | computer science |
26,594 | Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning | cs.CV | This paper proposes a learning strategy that extracts object-part concepts
from a pre-trained convolutional neural network (CNN), in an attempt to 1)
explore explicit semantics hidden in CNN units and 2) gradually grow a
semantically interpretable graphical model on the pre-trained CNN for
hierarchical object understan... | computer science |
26,595 | Baseline CNN structure analysis for facial expression recognition | cs.CV | We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recog... | computer science |
26,596 | A DNN Framework For Text Image Rectification From Planar Transformations | cs.CV | In this paper, a novel neural network architecture is proposed attempting to
rectify text images with mild assumptions. A new dataset of text images is
collected to verify our model and open to public. We explored the capability of
deep neural network in learning geometric transformation and found the model
could segme... | computer science |
26,597 | Herding Generalizes Diverse M -Best Solutions | cs.CV | We show that the algorithm to extract diverse M -solutions from a Conditional
Random Field (called divMbest [1]) takes exactly the form of a Herding
procedure [2], i.e. a deterministic dynamical system that produces a sequence
of hypotheses that respect a set of observed moment constraints. This
generalization enables ... | computer science |
26,598 | Selfie Detection by Synergy-Constraint Based Convolutional Neural
Network | cs.CV | Categorisation of huge amount of data on the multimedia platform is a crucial
task. In this work, we propose a novel approach to address the subtle problem
of selfie detection for image database segregation on the web, given rapid rise
in number of selfies clicked. A Convolutional Neural Network (CNN) is modeled
to lea... | computer science |
26,599 | Automatic discovery of discriminative parts as a quadratic assignment
problem | cs.CV | Part-based image classification consists in representing categories by small
sets of discriminative parts upon which a representation of the images is
built. This paper addresses the question of how to automatically learn such
parts from a set of labeled training images. The training of parts is cast as a
quadratic ass... | computer science |
26,600 | Can fully convolutional networks perform well for general image
restoration problems? | cs.CV | We present a fully convolutional network(FCN) based approach for color image
restoration. FCNs have recently shown remarkable performance for high-level
vision problem like semantic segmentation. In this paper, we investigate if FCN
models can show promising performance for low-level problems like image
restoration as ... | computer science |
26,601 | Fast Task-Specific Target Detection via Graph Based Constraints
Representation and Checking | cs.CV | In this work, we present a fast target detection framework for real-world
robotics applications. Considering that an intelligent agent attends to a
task-specific object target during execution, our goal is to detect the object
efficiently. We propose the concept of early recognition, which influences the
candidate prop... | computer science |
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