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Cast Shadow Removal Combining Local and Global Features

Cast Shadow Removal Combining Local and Global Features

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Zhou Liu Kaiqi Huang Tieniu Tan Liangsheng Wang Institute of Automation National Laboratory of Pattern Recognition Chinese Academy and Sciences Beijing China

In this paper, we present a method using pixel-level information, local region-level information and global-level information to remove shadow. At the pixel-level, we employ GMM to model the behavior of cast shadow for every pixel in the HSV color space, as it can deal with complex illumination conditions. However, unlike the GMM for background which can obtain sample every frame, this model for shadow needs more frames to get the same number of sample, because shadow may not appear at the same pixel for each frame. Therefore, it will take a long time to converge. To overcome this drawback, we use the local region-level information to get more samples and global-level information to improve a preclassifier and then, by using it, we get samples which are more likely to be shadow. Also, at the local region-level, we use Markov random fields to represent dependencies between the label of single pixel and labels of its neighborhood. Moreover, to make global level information more robust, tracking information is used. Experimental results show that the proposed method is efficient and robust.

关键词： Lighting Object detection Layout Robustness Cameras Light sources Laboratories pattern recognition Automation Markov random fields

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Sequential Architecture for Efficient Car Detection

Sequential Architecture for Efficient Car Detection

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Zhenfeng Zhu Yao Zhao Hanqing Lu National Key Laboratory on Machine Perception Peking University China Institute of Information Science Beijing Jiaotong University China National Laboratory of Pattern Recognition Institute of Automation China

Based on multi-cue integration and hierarchical SVM, we present a sequential architecture for efficient car detection under complex outdoor scene in this paper. On the low level, two novel area templates based on edge and interest-point cues respectively are first constructed, which can be applied to forming the identities of visual perception to some extent and thus utilized to reject rapidly most of the negative non-car objects at the cost of missing few of the true ones. Moreover on the high level, both global structure and local texture cues are exploited to characterize the car objects precisely. To improve the computational efficiency of general SVM, a solution approximating based two-level hierarchical SVM is proposed. The experimental results show that the integration of global structure and local texture properties provides more powerful ability in discrimination of car objects from non-car ones. The final high detection performance also contributes to the utilizing of two novel low level visual cues and the hierarchical SVM.

关键词： Object detection Support vector machines Face detection Support vector machine classification Vehicle detection Computational efficiency Information science Laboratories Layout pattern recognition

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Scene Classification Using Bag-of-Regions Representations

Scene Classification Using Bag-of-Regions Representations

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Demir Gokalp Selim Aksoy Department of Computer Engineering Bilkent University Ankara Turkey

This paper describes our work on classification of outdoor scenes. First, images are partitioned into regions using one-class classification and patch-based clustering algorithms where one-class classifiers model the regions with relatively uniform color and texture properties, and clustering of patches aims to detect structures in the remaining regions. Next, the resulting regions are clustered to obtain a codebook of region types, and two models are constructed for scene representation: a "bag of individual regions" representation where each region is regarded separately, and a "bag of region pairs" representation where regions with particular spatial relationships are considered together. Given these representations, scene classification is done using Bayesian classifiers. We also propose a novel region selection algorithm that identifies region types that are frequently found in a particular class of scenes but rarely exist in other classes, and also consistently occur together in the same class of scenes. Experiments on the LabelMe data set showed that the proposed models significantly outperform a baseline global feature-based approach.

关键词： Layout Bayesian methods Histograms Clustering algorithms Partitioning algorithms Marine vehicles computer vision Application software Image retrieval Content based retrieval

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Discriminative Cluster Refinement: Improving Object Category recognition Given Limited Training Data

Discriminative Cluster Refinement: Improving Object Category...

引用

Conference on computer vision and pattern recognition (CVPR)

作者： Liu Yang Rong Jin Caroline Pantofaru Rahul Sukthankar Department of Computer Science and Engineering Michigan State University USA The Robotics Institute Carnegie Mellon University USA Intel Research Pittsburgh USA

A popular approach to problems in image classification is to represent the image as a bag of visual words and then employ a classifier to categorize the image. Unfortunately, a significant shortcoming of this approach is that the clustering and classification are disconnected. Since the clustering into visual words is unsupervised, the representation does not necessarily capture the aspects of the data that are most useful for classification. More seriously, the semantic relationship between clusters is lost, causing the overall classification performance to suffer. We introduce "discriminative cluster refinement" (DCR), a method that explicitly models the pairwise relationships between different visual words by exploiting their co-occurrence information. The assigned class labels are used to identify the co-occurrence patterns that are most informative for object classification. DCR employs a maximum-margin approach to generate an optimal kernel matrix for classification. One important benefit of DCR is that it integrates smoothly into existing bag-of-words information retrieval systems by employing the set of visual words generated by any clustering method. While DCR could improve a broad class of information retrieval systems, this paper focuses on object category recognition. We present a direct comparison with a state-of-the art method on the PASCAL 2006 database and show that cluster refinement results in a significant improvement in classification accuracy given a small number of training examples.

关键词： Training data Information retrieval computer vision Application software Image recognition Image retrieval Content based retrieval Histograms computer science Data engineering

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Thermal-Visible Video Fusion for Moving Target Tracking and Pedestrian Classification

Thermal-Visible Video Fusion for Moving Target Tracking and ...

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Conference on computer vision and pattern recognition (CVPR)

作者： Alex Leykin Yang Ran Riad Hammoud Department of Computer Science Indiana University Bloomington IN USA Center for Automation Research University of Maryland College Park MD USA Delphi Electronics and Safety World Headquarters Delphi Communications Systems Inc. IN USA

The paper presents a fusion-tracker and pedestrian classifier for color and thermal cameras. The tracker builds a background model as a multi-modal distribution of colors and temperatures. It is constructed as a particle filter that makes a number of informed reversible transformations to sample the model probability space in order to maximize posterior probability of the scene model. Observation likelihoods of moving objects account their 3D locations with respect to the camera and occlusions by other tracked objects as well as static obstacles. After capturing the coordinates and dimensions of moving objects we apply a pedestrian classifier based on periodic gait analysis. To separate humans from other moving objects, such as cars, we detect, in human gait, a symmetrical double helical pattern, that can then be analyzed using the Frieze Group theory. The results of tracking on color and thermal sequences demonstrate that our algorithm is robust to illumination noise and performs well in the outdoor environments.

关键词： Target tracking Cameras Humans Colored noise Temperature distribution Particle filters Layout Object detection pattern analysis Noise robustness

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Real-time Automatic Deceit Detection from Involuntary Facial Expressions

Real-time Automatic Deceit Detection from Involuntary Facial...

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Conference on computer vision and pattern recognition (CVPR)

作者： Zhi Zhang Vartika Singh Thomas E. Slowe Sergey Tulyakov Venugopal Govindaraju Center for Unified Biometrics and Sensors State University of New York University at Buffalo NY USA CUBRC Buffalo NY USA

Being the most broadly used tool for deceit measurement, the polygraph is a limited method as it suffers from human operator subjectivity and the fact that target subjects are aware of the measurement, which invites the opportunity to alter their behavior or plan counter-measures in advance. The approach presented in this paper attempts to circumvent these problems by unobtrusively and automatically measuring several prior identified deceit indicators (DIs) based upon involuntary, so-called reliable facial expressions through computer vision analysis of image sequences in real time. Reliable expressions are expressions said by the psychology community to be impossible for a significant percentage of the population to convincingly simulate, without feeling a true inner felt emotion. The strategy is to detect the difference between those expressions which arise from internal emotion, implying verity, and those expressions which are simulated, implying deceit. First, a group of facial action units (AUs) related to the reliable expressions are detected based on distance and texture based features. The DIs then can be measured and finally a decision of deceit or verity will be made accordingly. The performance of this proposed approach is evaluated by its real time implementation for deceit detection.

关键词： Face detection Gold Humans Psychology Biometrics Biosensors Testing Facial muscles computer vision Image analysis

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Facial Strain pattern as a Soft Forensic Evidence

Facial Strain Pattern as a Soft Forensic Evidence

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IEEE Workshop on Applications of computer vision (WACV)

作者： Vasant Manohar Dmitry Goldgof Sudeep Sarkar Yong Zhang Computer Science & Engineering University of South Florida Tampa FL USA Computer Science & Information Systems Youngstown State University Youngstown OH USA

The success of forensic identification largely depends on the availability of strong evidence or traces that substantiate the prosecution hypothesis that a certain person is guilty of crime. In light of this, extracting subtle evidences which the criminals leave behind at the crime scene will be of valuable help to investigators. We propose a novel method of using strain pattern extracted from changing facial expressions in video as an auxiliary evidence for person identification. The strength of strain evidence is analyzed based on the increase in likelihood ratio it provides in a suspect population. Results show that strain pattern can be used as a supplementary biometric evidence in adverse operational conditions such as shadow lighting and face camouflage where pure intensity-based face recognition algorithms will fail

关键词： Capacitive sensors Forensics Face recognition Biological tissues Humans Biometrics Face detection computer science Optical imaging Infrared imaging

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An optimized eye locating and tracking system for driver fatigue monitoring

An optimized eye locating and tracking system for driver fat...

引用

International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： He Huang You-Sheng Zhou Fan Zhang Feng-Chen Liu College of Software Beijing Aeronautics and Astronautics University China

The eye locating and tracking are the most difficult and crucial tasks of real-time driver fatigue monitoring system based on computer vision. An optimized eye locating and tracking system, with a new eye tracking algorithm for monitoring driver's vigilance, is presented in this paper, which is based on a hardware system using active IR (Infrared) illuminator. The system can work with subjects of different genders, ages, with or without glasses, and work well under different illumination conditions.

关键词： Fatigue computerized monitoring computer vision Lighting Cameras Real time systems Infrared surveillance Face detection Hardware Glass

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Incremental Linear Discriminant Analysis Using Sufficient Spanning Set Approximations

Incremental Linear Discriminant Analysis Using Sufficient Sp...

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Conference on computer vision and pattern recognition (CVPR)

作者： Tae-Kyun Kim Shu-Fai Wong Bjorn Stenger Josef Kittler Roberto Cipolla Department of Engineering University of Cambridge Cambridge UK Computer Vision Group Toshiba Research Europe Limited Cambridge UK CVSSP University of Surrey Guildford UK

This paper presents a new incremental learning solution for linear discriminant analysis (LDA). We apply the concept of the sufficient spanning set approximation in each update step, i.e. for the between-class scatter matrix, the projected data matrix as well as the total scatter matrix. The algorithm yields a more general and efficient solution to incremental LDA than previous methods. It also significantly reduces the computational complexity while providing a solution which closely agrees with the batch LDA result. The proposed algorithm has a time complexity of O(Nd 2 ) and requires O(Nd) space, where d is the reduced subspace dimension and N the data dimension. We show two applications of incremental LDA: First, the method is applied to semi-supervised learning by integrating it into an EM framework. Secondly, we apply it to the task of merging large databases which were collected during MPEG standardization for face image retrieval.

关键词： Linear discriminant analysis Light scattering Image retrieval Computational complexity Standardization MPEG 7 Standard Principal component analysis Europe computer vision Semisupervised learning

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Resolving the Generalized Bas-Relief Ambiguity by Entropy Minimization

Resolving the Generalized Bas-Relief Ambiguity by Entropy Mi...

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Conference on computer vision and pattern recognition (CVPR)

作者： Neil G. Alldrin Satya P. Mallick David J. Kriegman University of California San Diego La Jolla CA USA

It is well known in the photometric stereo literature that uncalibrated photometric stereo, where light source strength and direction are unknown, can recover the surface geometry of a Lambertian object up to a 3-parameter linear transform known as the generalized bas relief (GBR) ambiguity. Many techniques have been proposed for resolving the GBR ambiguity, typically by exploiting prior knowledge of the light sources, the object geometry, or non-Lambertian effects such as specularities. A less celebrated consequence of the GBR transformation is that the albedo at each surface point is transformed along with the geometry. Thus, it should be possible to resolve the GBR ambiguity by exploiting priors on the albedo distribution. To the best of our knowledge, the only time the albedo distribution has been used to resolve the GBR is in the case of uniform albedo. We propose a new prior on the albedo distribution : that the entropy of the distribution should be low. This prior is justified by the fact that many objects in the real-world are composed of a small finite set of albedo values.

关键词： Entropy Light sources Geometry Reflectivity Photometry Surface reconstruction Lighting Snow Image resolution Machine vision

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