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Direct 3D-rotation estimation from spherical images via a generalized shift theorem

Direct 3D-rotation estimation from spherical images via a ge...

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conference on computer vision and pattern recognition (cvpr)

作者： A. Makadia K. Daniilidis GRASP Laboratory University of Pennsylvania Philadelphia PA USA

Omnidirectional images arising from 3D-motion of a camera contain persistent structures over a large variation of motions because of their large field of view. This persistence made appearance-based methods attractive for robot localization given reference views. Assuming that central omnidirectional images can be mapped to the sphere, the question is what are the underlying mappings of the sphere that can reflect a rotational camera motion. Given such a mapping, we propose a systematic way for finding invariance and the mapping parameters themselves based on the generalization of the Fourier transform. Using results from representation theory, we can generalize the Fourier transform to any homogeneous space with a transitively acting group. Such a case is the sphere with rotation as the acting group. The spherical harmonics of an image pair are related to each other through a shift theorem involving the irreducible representation of the rotation group. We show how to extract Euler angles using this theorem. We study the effect of the number of spherical harmonic coefficients as well as the effect of violation of appearance persistence in real imagery.

关键词： Cameras Fourier transforms Motion estimation Robot vision systems Optical sensors Layout Laboratories Robot localization Image motion analysis Spatiotemporal phenomena

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Face recognition in hyperspectral images

Face recognition in hyperspectral images

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conference on computer vision and pattern recognition (cvpr)

作者： Zhihong Pan G.E. Healey M. Prascad B.J. Tromberg Department of Electrical Engineering and Computer Science University of California Irvine Irvine California USA University of California Irvine Irvine CA US Dept. of Electr. Eng. & Comput. Sci. Univ. of California Irvine CA USA Beckman Laser Institute University of California Irvine Irvine CA USA Beckman Laser Institute University of California Irvine Irvine California

Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter. Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure, which is significantly different from person to person but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression, which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.

关键词： Face recognition Hyperspectral imaging Hyperspectral sensors Humans Image recognition Image databases Charge coupled devices Charge-coupled image sensors Liquid crystals Filters

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Independent component analysis in a facial local residue space

Independent component analysis in a facial local residue spa...

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conference on computer vision and pattern recognition (cvpr)

作者： Tae-Kyun Kim Hyunwoo Kim Wonjun Hwang Seok-Cheol Kee J. Kittler Centre for Vision Speech and Signal Processing University of Surrey UK Human Computer Interaction Laboratory Samsung Advanced Institute of Technology South Korea

In this paper, we propose an ICA (Independent Component Analysis) based face recognition algorithm, which is robust to illumination and pose variation. Generally, it is well known that the first few eigenfaces represent illumination variation rather than identity. Most PCA (Principal Component Analysis)-based methods have overcome illumination variation by discarding the projection to a few leading eigenfaces. The space spanned after removing a few leading eigenfaces is called the "residual face space". We found that ICA in the residual face space provides more efficient encoding in terms of redundancy reduction and robustness to pose variation as well as illumination variation, owing to its ability to represent non-Gaussian statistics. Moreover, a face image is separated into several facial components, local spaces, and each local space is represented by the ICA bases (independent components) of its corresponding residual space. The statistical models of face images in local spaces are relatively simple and facilitate classification by a linear encoding. Various experimental results show that the accuracy of face recognition is significantly improved by the proposed method under large illumination and pose variations.

关键词： Independent component analysis Principal component analysis Face recognition Lighting Statistics Image databases Face detection Space technology Speech analysis Signal processing algorithms

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Face recognition under variable lighting using harmonic image exemplars

Face recognition under variable lighting using harmonic imag...

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conference on computer vision and pattern recognition (cvpr)

作者： Lie Zhang D. Samaras Department of Computer Science SUNY Stony Brook NY USA

We propose a new approach for face recognition under arbitrary illumination conditions, which requires only one training image per subject (if there is no pose variation) and no 3D shape information. Our method is based on the result of Basri and Jacobs (2001), which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. In this paper, we show that we can recover basis images spanning this space from just one image taken under arbitrary illumination conditions. First, using a bootstrap set consisting of 3D face models, we compute a statistical model for each basis image. During training, given a novel face image under arbitrary illumination, we recover a set of images for this face. We prove that these images are the set of basis images with maximum probability. During testing, we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our method achieves comparable levels of accuracy with methods that have much more onerous training data requirements.

关键词： Face recognition Lighting Shape Image recognition Testing computer science Probability Training data Image reconstruction Image texture analysis

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Illumination normalization with time-dependent intrinsic images for video surveillance

Illumination normalization with time-dependent intrinsic ima...

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conference on computer vision and pattern recognition (cvpr)

作者： Y. Matsushita K. Nishino K. Ikeuchi M. Sakauchi Institute of Industrial Science University of Tokyo Tokyo Japan Department of Computer Science Columbia University NY USA

Cast shadows produce troublesome effects for video surveillance systems, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. To robustly eliminate these shadows from image sequences as a preprocessing stage for robust video surveillance, we propose a framework based on the idea of intrinsic images. Unlike previous methods for deriving intrinsic images, we derive time-varying reflectance images and corresponding illumination images from a sequence of images. Using obtained illumination images, we normalize the input image sequence in terms of incident lighting distribution to eliminate shadow effects. We also propose an illumination normalization scheme, which can potentially run in real time, utilizing the illumination eigenspace, which captures the illumination variation due to weather, time of day etc., and a shadow interpolation method based on shadow hulls. This paper describes the theory of the framework with simulation results, and shows its effectiveness with object tracking results on real scene data sets for traffic monitoring.

关键词： Lighting Video surveillance Robustness Image sequences Layout Target tracking Reflectivity Interpolation Buildings Image databases

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Illumination chromaticity estimation using inverse-intensity chromaticity space

Illumination chromaticity estimation using inverse-intensity...

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conference on computer vision and pattern recognition (cvpr)

作者： T.T. Tan K. Nishino K. Ikeuchi Department of Computer Science University of Tokyo Japan Department of Computer Science Columbia University USA

Existing color constancy methods cannot handle both uniform colored surfaces and highly textured surfaces in a single integrated framework. Statistics-based methods require many surface colors, and become error prone when there are only few surface colors. In contrast, dichromatic-based methods can successfully handle uniformly colored surfaces, but cannot be applied to highly textured surfaces since they require precise color segmentation. In this paper, we present a single integrated method to estimate illumination chromaticity from single/multi-colored surfaces. Unlike the existing dichromatic-based methods, the proposed method requires only rough highlight regions, without segmenting the colors inside them. We show that, by analyzing highlights, a direct correlation between illumination chromaticity and image chromaticity can be obtained. This correlation is clearly described in "inverse-intensity chromaticity space", a new two-dimensional space we introduce. In addition, by utilizing the Hough transform and histogram analysis in this space, illumination chromaticity can be estimated robustly, even for a highly textured surface. Experimental results on real images show the effectiveness of the method.

关键词： Lighting Surface texture Rough surfaces Surface roughness Color Colored noise computer science computer errors Image segmentation Image analysis

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Clustering appearances of objects under varying illumination conditions

Clustering appearances of objects under varying illumination...

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conference on computer vision and pattern recognition (cvpr)

作者： J. Ho Ming-Husang Yang Jongwoo Lim Kuang-Chih Lee D. Kriegman Computer Science & Engineering University of California at San Diego La Jolla CA Honda Research Institute Mountain View CA USA Computer Science University of Illinois at Urbana-Champaign Urbana IL USA Computer Science & Engineering University of California at San Diego La Jolla CA USA

We introduce two appearance-based methods for clustering a set of images of 3D (three-dimensional) objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity, which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets.

关键词： Lighting Clustering algorithms computer vision computer science Pixel Image databases Feature extraction Statistics Image converters Cameras

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A perspective on distortions

A perspective on distortions

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conference on computer vision and pattern recognition (cvpr)

作者： R. Swaninathan M.D. Grossberg S.K. Nayar Dept. of Comput. Sci. Columbia Univ. New York NY USA Department of Computer Science Columbia University New York New York USA

A framework for analyzing distortions in non-single viewpoint imaging systems is presented. Such systems possess loci of viewpoints called caustics. In general, perspective (or undistorted) views cannot be computed from images acquired with such systems without knowing scene structure. Views computed without scene structure will exhibit distortions, which we call caustic distortions. We first introduce a taxonomy of distortions based on the geometry of imaging systems. Then, we derive a metric to quantify caustic distortions. We present an algorithm to compute minimally distorted views using simple priors on scene structure. These priors are defined as parameterized primitives such as spheres, planes and cylinders with simple uncertainty models for the parameters. To validate our method, we conducted extensive experiments on rendered and real images. In all cases our method produces nearly undistorted views even though the acquired images were strongly distorted. We also provide an approximation of the above method that warps the entire captured image into a quasi single viewpoint representation that can be used by any "viewer" to compute near-perspective views in real-time.

关键词： Cameras Optical imaging Layout Optical distortion Solid modeling Taxonomy Geometry Lenses computer science Image analysis

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Shadow elimination and occluder light suppression for multi-projector displays

Shadow elimination and occluder light suppression for multi-...

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conference on computer vision and pattern recognition (cvpr)

作者： Tat-Jen Cham J.M. Rehg R. Sukthankar G. Sukthankar School of Computer Engineering Nanyang Technological University Singapore College of Computing Georgia Institute of Technology Atlanta GA USA The Robotics Institute Carnegie Mellon University Pittsburgh PA USA Intel Research Pittsburgh Pittsburgh PA USA

Two related problems of front projection displays, which occur when users obscure a projector, are: (i) undesirable shadows cast on the display by the users, and (ii) projected light falling on and distracting the users. This paper provides a computational framework for solving these two problems based on multiple overlapping projectors and cameras. The overlapping projectors are automatically aligned to display the same dekeystoned image. The system detects when and where shadows are cast by occluders and is able to determine the pixels, which are occluded in different projectors. Through a feedback control loop, the contributions of unoccluded pixels from other projectors are boosted in the shadowed regions, thereby eliminating the shadows. In addition, pixels, which are being occluded, are blanked, thereby preventing the projected light from falling on a user when they occlude the display. This can be accomplished even when the occluders are not visible to the camera. The paper presents results from a number of experiments demonstrating that the system converges rapidly with low steady-state errors.

关键词： Cameras Plasma displays computer displays Photometry Educational institutions Robot kinematics Feedback control Steady-state Surges Virtual environment

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Scene detection in Hollywood movies and TV shows

Scene detection in Hollywood movies and TV shows

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conference on computer vision and pattern recognition (cvpr)

作者： Z. Rasheed M. Shah School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

A scene can be defined as one of the subdivisions of a play in which the setting is fixed, or when it presents continuous action in one place. We propose a novel two-pass algorithm for scene boundary detection, which utilizes the motion content, shot length and color properties of shots as the features. In our approach, shots are first clustered by computing Backward Shot Coherence (BSC) - a shot color similarity measure that detects Potential Scene Boundaries (PSBs) in the videos. In the second pass we compute Scene Dynamics (SD), a function of shot length and the motion content in the potential scenes. In this pass, a scene merging criteria has been developed to remove weak PSBs in order to reduce over segmentation. We also propose a method to describe the content of each scene by selecting one representative image. The segmentation of video data into number of scenes facilitates an improved browsing of videos in electronic form, such as video on demand, digital libraries, Internet. The proposed algorithm has been tested on a variety of videos that include five Hollywood movies, one sitcom, and one interview program and promising results have been obtained.

关键词： Layout Motion pictures TV Gunshot detection systems Image segmentation Clustering algorithms Motion detection Merging Video on demand Software libraries

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