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Automatic Feature Localization in Thermal Images for Facial Expression recognition

Automatic Feature Localization in Thermal Images for Facial ...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： L. Trujillo G. Olague R. Hammoud B. Hernandez Proyecto Evovision Departmento de Ciencias de la Computacion CICESE Ensenada Baja California Mexico Delphi Electronics and Safety Delphi Communications Systems Inc. IN USA Instituto de Astronomia Universidad Nacional Autonoma de Mexico Ensenada Baja California Mexico

We propose an unsupervised Local and Global feature extraction paradigm to approach the problem of facial expression recognition in thermal images. Starting from local, low-level features computed at interest point locations, our approach combines the localization of facial features with the holistic approach. The detailed steps are as follows: First, face localization using bi-modal thresholding is accomplished in order to localize facial features by way of a novel interest point detection and clustering approach. Second, we compute representative Eigenfeatures for feature extraction. Third, facial expression classification is made with a Support Vector Machine Committiee. Finally, the experiments over the IRIS data-set show that automation was achieved with good feature localization and classification performance.

关键词： Face recognition Image recognition Support vector machines Image analysis Feature extraction Facial features Face detection Humans Information analysis Data mining

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A new active contour method based on elastic interaction

A new active contour method based on elastic interaction

引用

conference on computer vision and pattern recognition (cvpr)

作者： Yang Xiang A.C.S. Chung Jian Ye Department of Mathematics Hong Kong University of Science and Technology Hong Kong China

Image segmentation is defined as partitioning an image into non-overlapping regions based on the intensity or texture. The active contour methods provide an effective way for segmentation, in which the boundary of an object (usually with large image gradient value) is detected by an evolving curve. But, these methods have limitations due to the fact that real images may have objects with complex geometric structures and shapes, and are often corrupted by noise. Developing more robust and accurate active contour methods has been an active research area since the idea of the methods was proposed. In this paper, we propose a new active contour method and apply the method to medical image segmentation. This new method uses a long-ranged interaction between image boundaries and the moving curves, which is inspired by the elastic interaction between line defects in solids (dislocations). The new method is more efficient and effective, especially in detecting thin, weak and blurred structures such as the images of blood vessels.

关键词： Active contours Image segmentation Level set Biomedical imaging Equations Mathematics computer science Object detection Shape Noise shaping

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Posture and Gesture recognition using 3D Body Shapes Decomposition

Posture and Gesture Recognition using 3D Body Shapes Decompo...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Chi-Wei Chu I. Cohen Institute for Robotics and Intelligent Systems University of Southern California USA Institute for Robotics and Intelligent Systems University of Southern California

We present a method for describing arbitrary human posture as a combination of basic postures. This decomposition allows for recognition of a larger number of postures and gestures from a small set of elementary postures called atoms. We propose a modified version of the matching pursuit algorithm for decomposing an arbitrary input posture into a linear combination of primary and secondary atoms. These atoms are represented through their shape descriptor inferred from the 3D visual-hull of the human body posture. Using an atom-based description of postures increases tremendously the set of recognizable postures while reducing the required training data set. A gesture recognition system based on the atom decomposition and Hidden Markov Model (HMM) is also described. Instead of representing gestures as HMM transition of postures, we separate the description of gestures as two HMMs, each describing the transition of Primary/Secondary atoms; thus greatly reducing the size of state space of HMM. We illustrate the proposed approach for posture and gesture recognition method on a set of video streams

关键词： Shape Humans Hidden Markov models Cameras Dictionaries Intelligent robots Matching pursuit algorithms User interfaces Biological system modeling Intelligent systems

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On modelling nonlinear shape-and-texture appearance manifolds

On modelling nonlinear shape-and-texture appearance manifold...

引用

conference on computer vision and pattern recognition (cvpr)

作者： C.M. Christoudias T. Darrell Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA

Statistical shape-and-texture appearance models employ image metamorphosis to form a rich, compact representation of object appearance. They achieve their efficiency by decomposing appearance into simpler shape-and-texture representations. In general, the shape and texture of an object can vary nonlinearly and in this case the conventional shape-and-texture mappings using principle component analysis (PCA) may poorly approximate the true space. In this paper we propose two nonlinear techniques for modelling shape-and-texture appearance manifolds. Our first method uses a mixture of Gaussians in image space to separate the different parts of the shape and texture spaces. A linear shape-and-texture model is defined at each component to form the overall model. Our second approach employs a nearest-neighbor method to find a local set of shapes and images that can be morphed to explain a new input. We test each approach using a speaking-mouth video sequence and compare both approaches to a conventional active appearance model (AAM).

关键词： Shape Mouth Principal component analysis Active appearance model Image texture analysis Gaussian processes Testing Video sequences Biological system modeling Spatial databases

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Polarization multiplexing for bidirectional imaging

Polarization multiplexing for bidirectional imaging

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conference on computer vision and pattern recognition (cvpr)

作者： O.G. Cula K.J. Dana D.K. Pai D. Wang Computer Science Department Rutgers University Piscataway NJ USA

Our goal is to incorporate polarization in appearance-based modeling in an efficient and meaningful way. Polarization has been used in numerous prior studies for separating diffuse and specular reflectance components, but in this work we show that it also can be used to separate surface reflectance contributions from individual light sources. Our approach is called polarization multiplexing and it has significant impact in appearance modeling and bidirectional imaging where the image as a function of illumination direction is needed. Multiple unknown light sources can illuminate the scene simultaneously, and the individual contributions to the overall surface reflectance can be estimated. To develop the method of polarization multiplexing, we use a relationship between light source direction and intensity modulation. Inverting this transformation enables the individual intensity contributions to be estimated. In addition to polarization multiplexing, we show that phase histograms from the intensity modulations can be used to estimate scene properties including the number of light sources.

关键词： Optical polarization Light sources Reflectivity Lighting Intensity modulation Optical modulation Layout Cameras Rough surfaces Surface roughness

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Tracking Cell Signals in Fluorescent Images

Tracking Cell Signals in Fluorescent Images

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： V. Gor M. Elowitz T. Bacarian E. Mjolsness Jet Propulsion Laboratory Instruments and Science Data Systems USA Institute for Genomics and Bioinformatics and Department of Computer Science University of California Irvine USA Department of Biology and Applied Physics Caltech USA

In this paper we present the techniques for tracking cell signal in GFP (Green Fluorescent Protein) images of growing cell colonies. We use such tracking for both data extraction and dynamic modeling of intracellular processes. The techniques are based on optimization of energy functions, which simultaneously determines cell correspondences, while estimating the mapping functions. In addition to spatial mappings such as affine and Thin-Plate Spline mapping, the cell growth and cell division histories must be estimated as well. Different levels of joint optimization are discussed. The most unusual tracking feature addressed in this paper is the possibility of one-to-two correspondences caused by cell division. A novel extended softassign algorithm for solutions of one-to-many correspondences is detailed in this paper. The techniques are demonstrated on three sets of data: growing bacillus Subtillus and e-coli colonies and a developing plant shoot apical meristem. The techniques are currently used by biologists for data extraction and hypothesis formation.

关键词： Fluorescence Bioinformatics Proteins Data mining Genomics computer science Organisms Circuit testing computer vision Propulsion

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Probabilistic kernels for the classification of auto-regressive visual processes

Probabilistic kernels for the classification of auto-regress...

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conference on computer vision and pattern recognition (cvpr)

作者： A.B. Chan N. Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

We present a framework for the classification of visual processes that are best modeled with spatio-temporal autoregressive models. The new framework combines the modeling power of a family of models known as dynamic textures and the generalization guarantees, for classification, of the support vector machine classifier. This combination is achieved by the derivation of a new probabilistic kernel based on the Kullback-Leibier divergence (KL) between Gauss-Markov processes. In particular, we derive the KL-kernel for dynamic textures in both 1) the image space, which describes both the motion and appearance components of the spatio-temporal process, and 2) the hidden state space, which describes the temporal component alone. Together, the two kernels cover a large variety of video classification problems, including the cases where classes can differ in both appearance and motion and the cases where appearance is similar for all classes and only motion is discriminant. Experimental evaluation on two databases shows that the new classifier achieves superior performance over existing solutions.

关键词： Kernel Support vector machines Support vector machine classification State-space methods Gaussian processes Visual databases Layout Tracking Fires Educational institutions

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A statistical field model for pedestrian detection

A statistical field model for pedestrian detection

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conference on computer vision and pattern recognition (cvpr)

作者： Ying Wu Ting Yu Gang Hua Department of Electrical & Computer Engineering Northwestern University Evanston IL USA

ISBN: (纸本)0769523722

This paper presents a new statistical model for detecting and tracking deformable objects such as pedestrians, where large shape variations induced by local shape deformation can not be well captured by global methods such as PCA. The proposed model employs a Boltzmann distribution to capture the prior of local deformation, and embeds it into a Markov network which can be learned from data. A mean field variational analysis of this model provides computationally efficient algorithms for computing the likelihood of image observations and facilitate fast model training. Based on that, effective detection and tracking algorithms for deformable objects are proposed and applied to pedestrian detection and tracking. The proposed method has several advantages. Firstly, it captures local deformation well and thus is robust to occlusions and clutter. In addition, it is computationally tractable. Moreover, it divides deformation into local deformation and global deformation, then conquers them by combining bottom-up and top-down methodologies. Extensive experiments demonstrate the effectiveness of the proposed model for deformable objects.

关键词： Deformable models Object detection Shape Principal component analysis Boltzmann distribution Markov random fields Image analysis Algorithm design and analysis Computational modeling Robustness

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Imaging the cardiovascular pulse

Imaging the cardiovascular pulse

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conference on computer vision and pattern recognition (cvpr)

作者： Nanfei Sun M. Garbey A. Merla I. Pavlidis Department of Computer Sc. University of Houston Clear Lake Houston TX USA Department of Clinical Sc. University G. D''Annunzio Chieti Scalo Italy

We have developed a novel method to measure human cardiac pulse at a distance. It is based on the information contained in the thermal signal emitted from major superficial vessels. This signal is acquired through a highly sensitive thermal imaging system. Temperature on the vessel is modulated by pulsative blood flow. To compute the frequency of modulation (pulse), we extract a line-based region along the vessel. Then, we apply Fast Fourier Transform (FFT) to individual points along this line of interest to capitalize on the pulse propagation effect. Finally, we use an adaptive estimation function on the average FFT outcome to quantify the pulse. We have tested the accuracy of our method on 5 subjects with highly successful results. The technology is expected to find applications among others in sustained physiological monitoring of cardiopulmonary diseases, sport training, sleep studies, and psychophysiology (polygraph).

关键词： Cardiology Pulse measurements Pulse modulation Anthropometry Humans Temperature sensors Blood flow Frequency modulation Data mining Fast Fourier transforms

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Real-time tracking using level sets

Real-time tracking using level sets

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Shi W.C. Karl Electrical and Computer Engineering Department Boston University Boston MA USA

In this paper we propose a novel implementation of the level set method that achieves real-time level-set-based video tracking. In our fast algorithm, the evolution of the curve is realized by simple operations such as switching elements between two linked lists and there is no need to solve any partial differential equations. Furthermore, a novel procedure based on Gaussian filtering is introduced to incorporate boundary smoothness regularization. By replacing the standard curve length penalty with this new smoothing procedure, further speedups are obtained. Another advantage of our fast algorithm is that the topology of the curves can be controlled easily. For the tracking of multiple objects, we extend our fast algorithm to maintain the desired topology for multiple object boundaries based on ideas from discrete topology. With our fast algorithm, a real-time system has been implemented on a standard PC and only a small fraction of the CPU power is used for tracking. Results from standard test sequences and our realtime system are presented.

关键词： Level set Topology Narrowband Partial differential equations Filtering Smoothing methods Real time systems System testing Video surveillance Videoconference

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