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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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Range data registration using photometric features

Range data registration using photometric features

引用

conference on computer vision and pattern recognition (cvpr)

作者： Joon Kyu Seo G.C. Sharp Sang Wook Lee Department of Media Technology Sogang University Seoul South Korea Department of Radiation Oncology Massachusetts General Hospital MA USA

This paper investigates the use of photometric features for the pair-wise registration of range images. Many artificial and natural objects exhibit abundant surface texture that may not be revealed in range data, and most structured light and laser range sensors are capable of capturing either grayscale or color photometric intensity in addition to range data. Nevertheless, the use of photometric features has not been widely investigated for range data registration, despite widespread research into local feature descriptors for object recognition in 2D photometric images. This paper addresses some of the problems that arise in using photometric features for range data registration, and presents a systematic method for their use. Potentially useful photometric features are detected on planar regions in 3D, and then reprojected to 2D to remove the perspective distortion. Then, a well-established 2D rotation- and brightness-invariant image feature descriptor is used for matching. Range data alignment is performed using a RANSAC algorithm, with verification performed in 3D. Experimental results demonstrate the effectiveness of this method.

关键词： Photometry Layout Data mining Shape computer vision Iterative closest point algorithm Feature extraction Oncology Hospitals Surface texture

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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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Tracking Cell Signals in Fluorescent Images

Tracking Cell Signals in Fluorescent Images

引用

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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pattern recognition based tools enabling autonomic computing

Pattern recognition based tools enabling autonomic computing

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2nd International conference on Autonomic Computing, ICAC 2005

作者： Bougaev, Anton A. Purdue University

ISBN: (纸本)0769522769

Fault detection is one of the important constituents of fault tolerance, which in turn defines the dependability of autonomic computing. In presented work several pattern recognition tools were investigated in application to early fault detection. The optimal margin classifier technique was utilized to detect the abnormal behavior of software processes. The comparison with the performance of the quadratic classifiers is reported. The optimal margin classifiers were also implemented to the fault detection in hardware components. The impulse parameter probing technique was introduced to mitigate intermittent and transient fault problems. The pattern recognition framework of analysis of responses to a controlled component perturbation yielded promising results.

关键词： pattern recognition

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Designing *** Transformations based Face recognition Algorithms

Designing *** Transformations based Face Recognition Algorit...

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

作者： P.K. Mohanty S. Sarkar R. Kasturi Computer Science and Engineering University of South Florida Tampa FL USA

We investigate methods to infer the best afine transformation based face recognition algorithm; which operates by projecting given images to a low-dimensional space, followed by distance computations. This category includes the following well known methods for recognition: the Principal Component Analysis (PCA), Linear Discriminant Analysis(LDA), and Independent Component Analysis (ICA). The desired afine transformation is not restricted to that which results in an orthogonal space and can involve shear and stretch. We adopt an approach that has a reverse engineering flavor. Starting from distances computed by any face recognition algorithm, such as the FRGC baseline algorithm, we learn the best afine transform that approximates it. We propose a closed form solution for this based on classical Multidimensional Scaling (MDS). Next, this afine transform is refined by considering the modification of a given distance matrix, which will enhance the separation of match and non-match scores. The afine transform that produces the best Receiver Operating Characteristic (ROC) is selected. The data from Face recognition Grand Challenge (FRGC-v2.0) reveals that learned afine transformation results in a better performance than the FRGC baseline algorithm.

关键词： Algorithm design and analysis Face recognition Principal component analysis Independent component analysis Reverse engineering Closed-form solution Multidimensional systems Statistical analysis computer science Linear discriminant analysis

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A productivity metric based on statistical pattern recognition

A productivity metric based on statistical pattern recogniti...

引用

29th Annual International computer Software and Applications conference, COMPSAC 2005

作者： Sharpe, Jody L. Cangussu, João W. Department of Computer Science University of Texas Dallas

ISBN: (纸本)0769522092

The generally accepted calculation to measure the productivity of a software engineer is based on economic theory and is borrowed from traditional product manufacturing environments. Managers often measure the productivity of a worker to determine merit-based raises or to provide feedback to workers with poor productivity. The assumption is that this calculation of a worker's productivity is directly proportional to a worker's value to the company. The motivation for the approach proposed here is that such relationship may not be algebraically captured with respect to the productivity of software engineers. To better capture the productivity of a software engineer and his value to a company, the productivity problem is reformulated here as a pattern recognition problem and solved using clustering. By defining a general productivity operator, clustering has been used to map the domain of the productivity operator to a range of productivity classes. This new approach has been successfully applied to randomly generated project data and actual project data from the NASA SATC software metric database. © 2005 ieee.

关键词： Productivity

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Bayesian image segmentation using wavelet-based priors

Bayesian image segmentation using wavelet-based priors

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conference on computer vision and pattern recognition (cvpr)

作者： M.A.T. Figueiredo Department of Electrical and Computer Engineering Instituto Superior Técnico Instituto de Telecomunicaćões Lisboa Portugal

This paper introduces a formulation which allows using wavelet-based priors for image segmentation. This formulation can be used in supervised, unsupervised, or semi-supervised modes, and with any probabilistic observation model (intensity, multispectral, texture). Our main goal is to exploit the well-known ability of wavelet-based priors to model piece-wise smoothness (which underlies state-of-the-art methods for denoising, coding, and restoration) and the availability of fast algorithms for wavelet-based processing. The main obstacle to using wavelet-based priors for segmentation is that they're aimed at representing real values, rather than discrete labels, as needed for segmentation. This difficulty is sidestepped by the introduction of real-valued hidden fields, to which the labels are probabilistically related. These hidden fields, being unconstrained and real-valued, can be given any type of spatial prior, such as one based on wavelets. Under this model, Bayesian MAP segmentation is carried out by a (generalized) EM algorithm. Experiments on synthetic and real data testify for the adequacy of the approach.

关键词： Bayesian methods Image segmentation Image restoration computer vision Biomedical imaging Markov random fields Spatial coherence Telecommunications Noise reduction Discrete wavelet transforms

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Using coupled subspace models for recovery of reflectance spectra from airborne images

Using coupled subspace models for recovery of reflectance sp...

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conference on computer vision and pattern recognition (cvpr)

作者： K. Chandra G. Healey Computer Vision Laboratory Department of Electrical Engineering and Computer Science University of California Irvine Irvine CA USA

The spectral radiance measured by an airborne sensor is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present a nonlinear algorithm for estimating surface spectral reflectance of a surface on the ground from the spectral radiance measured by an airborne sensor. The nonlinear separation algorithm uses a low-dimensional subspace model for the reflectance spectra. The algorithm also considers the inter-dependence of the path radiance and illumination spectra by using a coupled subspace model. We have applied the algorithm to a large set of 0.4-1.74 micron sensor radiance spectra. We have examined the use of the recovered reflectance vectors for material identification over a database of materials from the US geological Survey (USGS) library. We also extend the nonlinear algorithm to estimate the reflectance spectra of materials having varying orientations. We have applied the algorithm to 0.42-1.74 micron sensor radiance spectra in digital imaging and remote sensing image generation (DIRSIG) model scenes that contain 3D objects.

关键词： Reflectivity Atmospheric modeling Atmospheric measurements Geologic measurements Lighting Image databases Geology Software libraries Image sensors Digital images

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Pose Angle Determination by Face, Eyes and Nose Localization

Pose Angle Determination by Face, Eyes and Nose Localization

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

作者： P. Sankaran S. Gundimada R.C. Tompkins V.K. Asari Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

FRGC aims to develop algorithms that make use of the high quality images in the face database. The algorithms in this paper have been developed with this in view. We present methods to estimate pose using multi-view classifiers. Based on the knowledge of pose and face geometry a region of interest of possible eye locations is found. An adaptive thresholding is used to pick out the darkest pixels in this region, which forms the eye region. Using this information region of interest of nose location is formed. A gradient operator is used to obtain the edges in this region. Histogram thresholding is used to find the nose in this region. Lines are drawn joining the center points of each of the regions. Perpendicular is drawn to the line joining the eye centers. Based on the slopes of these lines the pose angle is determined. The FRGC version 2 experiment number 4 image set is used to test the eye location algorithm while the nose location and pose angle methods are tested on version 1 image set.

关键词： Eyes Nose Face detection Image edge detection Head Testing Face recognition Mouth Light emitting diodes Iris

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