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Learning with Cascade for Classification of Non-Convex Manifolds

Learning with Cascade for Classification of Non-Convex Manif...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiangsheng Huang S.Z. Li Yangsheng Wang Institute of Automation Chinese Academy and Sciences Beijing China Microsoft Research Asia Beijing China

Images of a visual object, such as human face, reside in a complicated manifold in the high dimensional image space, when the object is subject to variations in pose, illumination, and other factors. Viola and Jones have successfully tackled difficult nonlinear classification problem for face detection using AdaBoost learning. Moreover, their simple-to-complex cascade of classifiers structure makes the learning and classification even more effective. While training with cascade has been used effectively in many works [4, 5, 6, 7, 2, 3, 8, 9, 10], an understanding of the role of the cascade strategy is still lacking. In this paper, we analyze the problem of classifying non-convex manifolds using AdaBoost learning with and without using cascade. We explain that the divide-and-conquer strategy in cascade learning has a great contribution on learning a complex classifier for non-convex manifolds. We prove that AdaBoost learning with cascade is effective when a complete or over-complete set of features (or weak classifiers) is available. Experiments with both synthesized and real data demonstrate that AdaBoost learning with cascade leads to improved convergence and accuracy.

关键词： Principal component analysis Face detection Automation Humans Lighting pattern recognition computer vision computer errors Asia Convergence

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Midstream Content Access of Visual pattern Coded Imagery

Midstream Content Access of Visual Pattern Coded Imagery

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： G. Schaefer School of Computing and Technology Nottingham Trent University Nottingham UK

Multimedia retrieval is going to play an increasingly important role in the future. Since the availability of digital media seems ever rising, not only the retrieval performance in terms of what the user is presented with as a result but also the retrieval speed proves to be crucial. Images are arguably the most prominent type of media that is to be searched and retrieved, and similar to other media such as videos or sound, images are usually stored in compressed form in order to save storage and bandwidth resources. One way of addressing the speed problem is to perform image retrieval directly in the compressed domain thus saving the time it takes to decode the pictures. In this paper we introduce such a compressed domain image retrieval technique based on the Colour Visual pattern Image Coding (CVPIC) compression method. A combined colour co-occurrence/shape matrix is built and used in retrieval experiments. These experiments show that our approach is not only desirable from an efficiency point of view but that it also clearly outperforms popular image retrieval techniques such as colour histograms, colour coherence vectors and colour correlograms.

关键词： Image coding Image retrieval Image storage Decoding Shape Content based retrieval Information retrieval Image edge detection Bandwidth Histograms

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Object-Based Visual 3D Tracking of Articulated Objects via Kinematic Sets

Object-Based Visual 3D Tracking of Articulated Objects via K...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A.I. Comport E. Marchand F. Chaumette IRISA/INRIA Rennes France

A theoretical framework based on robotics techniques is introduced for visual tracking of parametric non-rigid multi-body objects. It is based on an a-priori model of the object including a general mechanical link description. The objective equation is defined in the object-based coordinate system and non-linear minimization relates to the movement of the object and not the camera. This results in simultaneously estimating all degrees of freedom between the object's last known position relative to its previous position as well as internal articulated parameters. A new kinematic-set formulation takes into account that articulated degrees of freedom are directly observable from the camera and therefore their estimation does not need to pass via a kinematic-chain back to the root. By doing this the tracking techniques are efficient and precise leading to real-time performance and accurate measurements. The system is locally based upon an accurate modeling of a distance criteria. A general method is derived for defining any type of mechanical link and experimental results show prismatic, rotational and helicoidal type links. A statistical M-estimation technique is applied to improve robustness. A monocular camera system was used as a real-time sensor to verify the theory.

关键词： Kinematics computer Society computer vision pattern recognition conferences

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A Unified Framework for Performance Evaluation of 3-D Reconstruction Techniques

A Unified Framework for Performance Evaluation of 3-D Recons...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A. Eid A. Farag Computer Vision and Image Processing (CVIP) Laboratory University of Louisville Louisville KY USA

In this paper we consider the problem of performance evaluation of 3-D reconstruction techniques from calibrated sequence of images. We propose a unified framework for the evaluation process assuming the existence of ground truth data collected by a 3-D laser scanner. Firstly, we propose a new pre-evaluation technique for 3-D data registration based on silhouettes alignment. The performance of this technique is independent of the output quality of the 3-D reconstruction technique under test. This is a major advantage of our technique since the quality issue, which is not guarantee under the evaluation topic, is a challenge for any 3-D registration technique. Secondly, we propose a geometrically based evaluation methodology. The method is based on the geometrical matching of uniformly distributed patches of both ground truth and under-test data sets. The method is insensitive to the relative density of data sets. In addition, it provides a method for localizing errors which is a crucial step for diagnosis and data fusion post-evaluation techniques. Finally, we provide experimental results on the evaluation of stereo and space carving techniques.

关键词： Three dimensional displays Stereo vision Testing computer vision Optical sensors Cameras Image processing Laboratories Stereo image processing Fuses

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Kernel Optimal Component Analysis

Kernel Optimal Component Analysis

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Qiang Zhang Xiuwen Liu Department of Computer Science Florida State University Tallahassee FL USA

Optimal component analysis (OCA) provides a general sub-space formulation that has many applications. Within the framework of linear representations, OCA poses the problem of finding the optimal representations as an optimization one on the underlying manifold such as Grassmann and a stochastic optimization algorithm can then be used to derive optimal representations for recognition and other applications. However, in many applications, as the underlying manifold is intrinsically nonlinear, the effectiveness of linear representations and thus OCA can be limited. To overcome this fundamental limitation, in this paper we propose a kernelized version of optimal component analysis. The basic idea is to (potentially) account the nonlinearity in the feature space through a nonlinear feature mapping so that linear representations in the resulting feature space can be used effectively for nonlinear problems in the given space. The computational complexity associated with the mapping is overcome by performing the mapping implicitly using a property of reproducing kernel Hilbert space. Therefore, kernel optimal component analysis provides a general method to learn application-dependent representations, either linear or nonlinear and a stochastic effective algorithm is presented. Experimental results for recognition show the feasibility and effectiveness of the proposed method.

关键词： Kernel Independent component analysis Stochastic processes Principal component analysis Hilbert space Algorithm design and analysis Object recognition Image recognition computer science Application software

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A Real Time Face Tracking And Animation System

A Real Time Face Tracking And Animation System

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xiaozhou Wei Zhiwei Zhu Lijun Yin Qiang Ji Department of Computer Science SUNY at Binghamton Binghamton NY USA Department of ECSE Rensselaer Polytechnic Institute Troy NY USA

In this paper, a novel system for real time face tracking and animation is presented. The system is composed of two major components: (1) real time infra-red (IR) based active facial feature tracking, and (2) real time facial expression generation based on a 3D face avatar. Twenty-two feature points, head pose orientation and eye close-open status are effectively extracted through a video input. Based on the detected facial features, a 3D face model is animated by a dynamic inference algorithm and a transformation from facial motion parameters to facial animation parameters. The work can be extended to the fields of real time facial expression analysis and synthesis for applications of human-computer interaction, model-based video conferencing and low bit rate avatar communication. The performance of the developed system is evaluated by its real time implementation for facial expression generation.

关键词： Facial animation Real time systems Face detection Facial features Avatars Motion detection Heuristic algorithms Inference algorithms Videoconference Bit rate

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A Generalized Temporal Context Model for Semantic Scene Classification

A Generalized Temporal Context Model for Semantic Scene Clas...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： M. Boutell Jiebo Luo Department of Computer Science University of Rochester USA Research and Development Laboratories Eastman Kodak (Japan) Limited USA

Semantic scene classification is an open problem in computer vision especially when information from only a single image is employed. In applications involving image collections, however, images are clustered sequentially, allowing surrounding images to be used as temporal context. We present a general probabilistic temporal context model in which the first-order Markov property is used to integrate content-based and temporal context cues. The model uses elapsed time-dependent transition probabilities between images to enforce the fact that images captured within a shorter period of time are more likely to be related. This model is generalized in that it allows arbitrary elapsed time between images, making it suitable for classifying image collections. We also derived a variant of this model to use in image collections for which no timestamp information is available, such as film scans. We applied the context models to two problems, achieving significant gains in accuracy in both cases. The two algorithms used to implement inference within the context model, Viterbi and belief propagation, yielded similar results.

关键词： Context modeling Layout Hidden Markov models computer vision Digital cameras computer science Research and development Laboratories Application software Inference algorithms

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High-Resolution, Real-time 3D Shape Acquisition

High-Resolution, Real-time 3D Shape Acquisition

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Song Zhang Peisen Huang Department of Mechanical Engineering State University of New York Stony Brook NY USA

In this paper we describe a high-resolution, real-time 3D shape acquisition system based on structured light techniques. This system uses a color pattern whose RGB channels are coded with either sinusoidal or trapezoidal fringe patterns. When projected by a modified DLP projector (color filters removed), this color pattern results in three grayscale patterns projected sequentially at a frequency of 240 Hz. A high-speed B/W CCD camera synchronized with the projector captures the three images, from which the 3D shape of the object is reconstructed. A color CCD camera is also used to capture images for texture mapping. The maximum 3D shape acquisition speed is 120 Hz (532 × 500 pixels), which is high enough for capturing the 3D shapes of moving objects. Two coding methods, sinusoidal phase-shifting method and trapezoidal phase-shifting method, were tested and results with good accuracy were obtained. The trapezoidal phase-shifting algorithm also makes real-time 3D reconstruction possible.

关键词： Shape Stereo vision Image reconstruction Charge-coupled image sensors Charge coupled devices Real time systems Cameras Stereo image processing Mechanical engineering Filters

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Towards Recovery of 3D Chromosome Structure

Towards Recovery of 3D Chromosome Structure

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： S. Babu Pao-Chuan Liao M.C. Shin L.V. Tsap Department of Computer Science UNC-Charlotte Charlotte NC USA Electronics Engineering Department Lawrence Livemore National Laboratory Livermore CA USA

The objectives of this work include automatic recovery and visualization of a 3D chromosome structure from a sequence of 2D tomographic reconstruction images taken through the nucleus of a cell. Structure is very important for biologists as it affects chromosome functions, behavior of the cell and its state. Chromosome analysis is significant in the detection of diseases and in monitoring environmental gene mutations. The algorithm incorporates thresholding based on a histogram analysis with a polyline splitting algorithm, contour extraction via active contours, and detection of the 3D chromosome structure by establishing corresponding regions throughout the slices. Visualization using point cloud meshing generates a 3D surface. The 3D triangular mesh of the chromosomes provides surface detail and allows a user to interactively analyze chromosomes using visualization software.

关键词： Biological cells Cells (biology) Data visualization Tomography Image reconstruction Diseases Monitoring Genetic mutations Histograms Algorithm design and analysis

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Exploring Face Space

Exploring Face Space

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： T. Sim Sheng Zhang School of Computing National University of Singapore Singapore

Face recognition is a difficult problem, whether using still images or video. A robust solution is still elusive after 30 years of research. The main reason postulated for this is that two people look more alike than images of the same person under different viewing conditions, i.e. the inter-class variability is smaller than the intra-class variability. In this paper, we propose a way to investigate this, and other, phenomenon more quantitatively. This is done by exploring the space of face images. We first synthesize images under different illumination and pose, and then estimate the probability density function (pdf) for each person. The pdfs are then analyzed for their separability, and for where they overlap. Class regions, regions where the Bayes' classifier would correctly classify each person, are also determined. These class regions are subjected to k-means clustering. By examining cluster boundaries, we can determine lighting and pose conditions that make face recognition difficult. Similarly, the cluster centers tell us the viewing conditions most suited for discriminating between the persons. Our paper makes three key contributions: (1) we show how face space may be modeled and explored; (2) we show that the traditional inter-class/intra-class variability is not a good measure of the separability of two classes, and instead propose the use of the Bhattacharyya distance, and (3) we determine the viewing conditions that are best (or worst) for face recognition.

关键词： Space exploration Face recognition Lighting Robustness pattern recognition Probability density function Extraterrestrial measurements Density measurement computer Society computer vision

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