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Ensemble Method for Robust Motion Estimation

Ensemble Method for Robust Motion Estimation

引用

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

作者： Wei Zhang J. Kosecka George Mason University Fairfax VA USA

The core of the traditional RANSAC algorithm and its more recent efficient counterparts is the hypothesis evaluation stage, with the focus on finding the best, outlier free hypothesis. Motivated by a non-parametric ensemble techniques, we demonstrate that it proves advantageous to use the entire set of hypotheses generated in the sampling stage. We show that by studying the residual distribution of each data point with respect to the entire set of hypotheses, the problem of inlier/ outlier identification can be formulated as a classification problem. We present extensive simulations of the approach, which in the presence of a large percentage (> 50%) of outliers, provides a repeatable and, an order of magnitude more efficient method compared to the currently existing techniques. Results on widebaseline matching and fundamental matrix estimation are presented.

关键词： Robustness Motion estimation Sampling methods computer vision Testing Parameter estimation Context modeling Maximum likelihood estimation Iterative algorithms Iterative methods

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Diffusion Distance for Histogram Comparison

Diffusion Distance for Histogram Comparison

引用

conference on computer vision and pattern recognition (CVPR)

作者： Haibin Ling K. Okada Center for Automation Research Computer Science Department University of Maryland College Park MD USA Imaging and Visualization Department Siemens Corporate Research Inc. Princeton NJ USA

In this paper we propose diffusion distance, a new dissimilarity measure between histogram-based descriptors. We define the difference between two histograms to be a temperature field. We then study the relationship between histogram similarity and a diffusion process, showing how diffusion handles deformation as well as quantization effects. As a result, the diffusion distance is derived as the sum of dissimilarities over scales. Being a cross-bin histogram distance, the diffusion distance is robust to deformation, lighting change and noise in histogram-based local descriptors. In addition, it enjoys linear computational complexity which significantly improves previously proposed cross-bin distances with quadratic complexity or higher. We tested the proposed approach on both shape recognition and interest point matching tasks using several multi-dimensional histogram-based descriptors including shape context, SIFT, and spin images. In all experiments, the diffusion distance performs excellently in both accuracy and efficiency in comparison with other state-of-the-art distance measures. In particular, it performs as accurately as the Earth Mover’s Distance with much greater efficiency.

关键词： Histograms Shape Temperature Diffusion processes Quantization Noise robustness Multi-stage noise shaping Computational complexity Testing Image recognition

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Joint Boosting Feature Selection for Robust Face recognition

Joint Boosting Feature Selection for Robust Face Recognition

引用

conference on computer vision and pattern recognition (CVPR)

作者： Rong Xiao Wujun Li Yuandong Tian Xiaoou Tang Microsoft Research Asia Beijing China National Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Engineering Shanghai JiaoTong University Shanghai China

A fundamental challenge in face recognition lies in determining what facial features are important for the identification of faces. In this paper, a novel face recognition framework is proposed to address this problem. In our framework, 3D face models are used to synthesize a huge database of realistic face images which covers wide appearance variations of faces due to various pose, illumination, and expression changes. A novel feature selection algorithm which we call Joint Boosting is developed to extract discriminative face features using this massive database. The major contributions of this paper are: (1) With the help of 3D face models, a massive database of realistic virtual face images is generated to achieve robust feature selection; (2)Because the huge database covers a wide range of face variations, our feature selection procedure only needs to be trained once, and the selected feature set can be generalized to other face database without re-training; (3) We propose a new learning algorithm, Joint Boosting Algorithm, which is effective and efficient in learning directly from a massive database without having to convert face images to intra-personal and extra-personal difference images. This property is important for applying our algorithm to other general pattern recognition problems. Experimental results show that our method significantly improves recognition performance.

关键词： Boosting Robustness Face recognition Image databases Spatial databases Facial features Lighting Feature extraction Image generation Image converters

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Empirical Studies of the Existence of the Biometric Menagerie in the FRGC 2.0 Color Image Corpus

Empirical Studies of the Existence of the Biometric Menageri...

引用

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

作者： M. Wittman P. Davis P.J. Flynn Department of Computer Science and Engineering University of Notre Dame Notre Dame IN USA

The issue of recognizability of subjects in biometric identification is of particular interest to the designers of these systems. We have applied the concept of Doddington’s biometric menagerie to the area of facial recognition. We performed a series of tests for the presence of goats, lambs, and wolves on FRGC 2.0 color image data. The data for the subjects that appeared at the extreme end of these tests was then visually examined. Even a cursory comparison of images showed that for this set of data, some images fell into the defined menagerie categories. Our tests show the statistical existence of these animal classifications within the constraints of this set of FRGC 2.0 data using the baseline matching algorithm. Ultimately, these tests were limited by the image data set and matching algorithm used. For further confirmation of the existence of the menagerie, the analysis must be expanded to include different image sets and matching algorithms..

关键词： Biometrics Color Testing Face recognition Probes Principal component analysis Image analysis Statistics computer science USA Councils

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Gabor Filter Analysis for Texture Segmentation

Gabor Filter Analysis for Texture Segmentation

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

作者： R. Sandler M. Lindenbaum Computer Science Department Technion-Israel Institute of Technology Haifa Israel

Gabor features are a common choice for texture analysis. The particular set of Gabor filters used for extracting the features is usually designed for optimal signal representation. We propose here an alternative criterion for designing the filter set. We consider a set of filters and its response to pairs of harmonic signals. Two signals are considered separable if the corresponding two sets of vector responses are disjoint in at least one of the components. We look for the set of Gabor filters that maximizes the fraction of separable harmonic signal pairs. The resulting filters are significantly different from the traditional ones. We test these maximal harmonic discrimination (MHD) filters using two texture discrimination methods, and describe their advantages over traditional filters.

关键词： Gabor filters Power harmonic filters Image segmentation Signal design Distortion measurement Signal analysis Stability Harmonic analysis computer science Feature extraction

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Finding Minimal Parameterizations of Cylindrical Image Manifolds

Finding Minimal Parameterizations of Cylindrical Image Manif...

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

作者： M. Dixon N. Jacobs R. Pless Department of Computer Science and Engineering Washington University Saint Louis MO USA

Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.

关键词： Topology Manifolds Jacobian matrices Noise reduction Ultrasonic imaging computer science Biomedical engineering Data engineering Application software Image analysis

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Moving Object Localization in Thermal Imagery by Forward-backward MHI

Moving Object Localization in Thermal Imagery by Forward-bac...

引用

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

作者： Zhaozheng Yin R. Collins Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

Detecting moving objects automatically is a key component of an automatic visual surveillance and tracking system. In airborne thermal video, the moving objects may be small, color information is not available, and even intensity appearance may be camouflaged. Previous motionbased moving object detection approaches often use background subtraction, inter-frame difference or three-frame difference. In this paper, we describe a detection and localization method based on forward-backward motion history images (MHI). This method can accurately detect location and shape of moving objects for initializing a tracker. Using long and varied video sequences, we quantify the effectiveness of this method.

关键词： Object detection Shape Motion detection Cameras Surveillance Image reconstruction Thermal engineering computer science History Video sequences

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A Closed Form Solution to Natural Image Matting

A Closed Form Solution to Natural Image Matting

引用

conference on computer vision and pattern recognition (CVPR)

作者： A. Levin D. Lischinski Y. Weiss School of Computer Science and Engineering Hebrew University of Jerusalem Israel

Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper we present a closed form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors, and show that in the resulting expression it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high quality mattes can be obtained on natural images from a surprisingly small amount of user input.

关键词： Closed-form solution Cost function Image color analysis Sparse matrices computer vision Pixel Iterative methods Linear systems Equations Image analysis

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Deformation Modeling for Robust 3D Face Matching

Deformation Modeling for Robust 3D Face Matching

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xiaoguang Lu A.K. Jain Department of Computer Science & Engineering Michigan State University East Lansing MI USA

Human face recognition based on 3D surface matching is promising for overcoming the limitations of current 2D image-based face recognition systems. The 3D shape is invariant to the pose and lighting changes, but not invariant to the non-rigid facial movement, such as expressions. Collecting and storing multiple templates for each subject in a large database (associated with various expressions) is not practical. We present a facial surface modeling and matching scheme to match 2.5D test scans in the presence of both non-rigid deformations and large pose changes (multiview) to a neutral expression 3D face model. A geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A personspecific (3D) deformable model is built for each subject in the gallery w.r.t. the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and large pose changes simultaneously. Experimental results demonstrate that the proposed matching scheme based on deformation modeling improves the matching accuracy.

关键词： Deformable models Robustness Face recognition Humans Shape Testing Mouth computer science Image databases Control system synthesis

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Lensless Imaging with a Controllable Aperture

Lensless Imaging with a Controllable Aperture

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conference on computer vision and pattern recognition (CVPR)

作者： A. Zomet S.K. Nayar Computer Science Department Columbia University New York NY USA

In this paper we propose a novel, highly flexible camera. The camera consists of an image detector and a special aperture, but no lens. The aperture is a set of parallel light attenuating layers whose transmittances are controllable in space and time. By applying different transmittance patterns to this aperture, it is possible to modulate the incoming light in useful ways and capture images that are impossible to capture with conventional lens-based cameras. For example, the camera can pan and tilt its field of view without the use of any moving parts. It can also capture disjoint regions of interest in the scene without having to capture the regions in between them. In addition, the camera can be used as a computational sensor, where the detector measures the end result of computations performed by the attenuating layers on the scene radiance values. These and other imaging functionalities can be implemented with the same physical camera and the functionalities can be switched from one video frame to the next via software. We have built a prototype camera based on this approach using a bare image detector and a liquid crystal modulator for the aperture. We discuss in detail the merits and limitations of lensless imaging using controllable apertures.

关键词： Apertures Cameras Detectors Layout Lenses Lighting control Optical modulation Attenuation measurement Performance evaluation Software prototyping

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