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Space-time behavior based correlation

Space-time behavior based correlation

引用

conference on computer vision and pattern recognition (cvpr)

作者： E. Shechtman M. Irani Department of Computer Science and Applied Math Weizmann Institute of Science Rehovot Israel

We introduce a behavior-based similarity measure which tells us whether two different space-time intensity patterns of two different video segments could have resulted from a similar underlying motion field. This is done directly from the intensity information, without explicitly computing the underlying motions. Such a measure allows us to detect similarity between video segments of differently dressed people performing the same type of activity. It requires no foreground/background segmentation, no prior learning of activities, and no motion estimation or tracking. Using this behavior-based similarity measure, we extend the notion of 2-dimensional image correlation into the 3-dimensional space-time volume, thus allowing to correlate dynamic behaviors and actions. Small space-time video segments (small video clips) are "correlated" against entire video sequences in all three dimensions (x,y, and t). Peak correlation values correspond to video locations with similar dynamic behaviors. Our approach can detect very complex behaviors in video sequences (e.g., ballet movements, pool dives, running water), even when multiple complex activities occur simultaneously within the field-of-view of the camera.

关键词： Motion estimation Video sequences Motion measurement Image segmentation Videoconference Performance evaluation Tracking Volume measurement Optical filters Apertures

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Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model

Face synthesis and recognition from a single image under arb...

引用

conference on computer vision and pattern recognition (cvpr)

作者： L. Zhang S. Wang D. Samaras Computer Science Department State University of New York Stony Brook NY USA

Understanding and modifying the effects of arbitrary illumination on human faces in a realistic manner is a challenging problem both for face synthesis and recognition. Recent research demonstrates 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 using spherical harmonics representation. Morphable models are statistical ensembles of facial properties such as shape and texture. In this paper, we integrate spherical harmonics into the morphable model framework, by proposing a 3D spherical harmonic basis morphable model (SHBMM) and demonstrate that any face under arbitrary unknown lighting can be simply represented by three low-dimensional vectors: shape parameters, spherical harmonic basis parameters and illumination coefficients. We show that, with our SHBMM, given one single image under arbitrary unknown lighting, we can remove the illumination effects from the image (face "delighting") and synthesize new images under different illumination conditions (face "re-lighting"). Furthermore, we demonstrate that cast shadows can be detected and subsequently removed by using the image error between the input image and the corresponding rendered image. We also propose two illumination invariant face recognition methods based on the recovered SHBMM parameters and the de-lit images respectively. Experimental results show that using only a single image of a face under unknown lighting, we can achieve high recognition rates and generate photorealistic images of the face under a wide range of illumination conditions, including multiple sources of illumination.

关键词： Face recognition Image recognition Lighting Humans Face detection Image generation Shape Rendering (computer graphics) Image databases computer science

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Facial Action Unit Detection using Probabilistic Actively Learned Support Vector Machines on Tracked Facial Point Data

Facial Action Unit Detection using Probabilistic Actively Le...

引用

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

作者： M.F. Valstar I. Patras M. Pantic EEMCS/Mediamatics Department Delft University of Technnology Netherlands

A system that could enable fast and robust facial expression recognition would have many applications in behavioral science, medicine, security and human-machine interaction. While working toward that goal, we do not attempt to recognize prototypic facial expressions of emotions but analyze subtle changes in facial behavior by recognizing facial muscle action units (AUs, i.e., atomic facial signals) instead. By detecting AUs we can analyse many more facial communicative signals than emotional expressions alone. This paper proposes AU detection by classifying features calculated from tracked ?ducial facial points. We use a Particle Filtering tracking scheme using factorized likelihoods and a novel observation model that combines a rigid and a morphologic model. The AUs displayed in a video are classi?ed using Probabilistic Actively Learned Support VectorMachines (PAL-SVM).When tested on 167 videos from the MMI web-based facial expression database, the proposed method achieved very high recognition rates for 16 different AUs. To ascertain data independency we also performed a validation using another benchmark database. When trained on the MMI-Facial expression database and tested on the Cohn-Kanade database, the proposed method achieved a recognition rate of 84% when detecting 9 AUs occurring alone or in combination in input image sequences.

关键词： Face detection Support vector machines Face recognition Image databases Emotion recognition Signal analysis Testing Robustness Behavioral science Biomedical imaging

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Joint nonparametric alignment for analyzing spatial gene expression patterns in Drosophila imaginal discs

Joint nonparametric alignment for analyzing spatial gene exp...

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conference on computer vision and pattern recognition (cvpr)

作者： P. Ahammad C.L. Harmon A. Hammonds S. Shankar Sastry G.M. Rubin Department of Electrical Engineering and Computer Science University of California Berkeley CA USA Department of Molecular and Cell Biology University of California Berkeley CA USA Howard Hughes Medical Institute and Department of Molecular and Cell Biology University of California Berkeley CA USA

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of 'congealing' (E. G. Miller et al., 2000), while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

关键词： Image analysis pattern analysis Gene expression Shape Embryo Time measurement Image segmentation Biological cells Organisms Insects

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Automatic 3D to 2D registration for the photorealistic rendering of urban scenes

Automatic 3D to 2D registration for the photorealistic rende...

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conference on computer vision and pattern recognition (cvpr)

作者： L. Liu I. Stamos Department of Computer Science Graduate Center and Hunter CollegeCity College and Graduate Center City University of New York New York NY USA

This paper presents a novel and efficient algorithm for the 3D range to 2D image registration problem in urban scene settings. Our input is a set of unregistered 3D range scans and a set of unregistered and uncalibrated 2D images of the scene. The 3D range scans and 2D images capture real scenes in extremely high detail. A new automated algorithm calibrates each 2D image and computes an optimized transformation between the 2D images and 3D range scans. This transformation is based on a match of 3D with 2D features that maximizes an overlap criterion. Our algorithm attacks the hard 3D range to 2D image registration problem in a systematic, efficient, and automatic way. Images captured by a high-resolution 2D camera, that moves and adjusts freely, are mapped on a centimeter-accurate 3D model of the scene providing photorealistic renderings of high quality. We present results from experiments in three different urban settings.

关键词： Layout Cameras Image registration Solid modeling Rendering (computer graphics) Image sensors Sensor systems computer science Educational institutions Calibration

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Human Learning of Contextual Priors for Object Search: Where does the time go?

Human Learning of Contextual Priors for Object Search: Where...

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

作者： B. Hidalgo-Sotelo A. Oliva A. Torralba Department of Brain and Cognitive Sciences MIT Cambridge MA USA Computer Science and Artificial Intelligence Laboratory MIT Cambridge MA USA

Attention allocation in visual search is known to be influenced by low-level image features, visual scene context and top down task constraints. Here, we investigate the role of Contextual priors in guiding visual search by monitoring eye movements as participants search very familiar scenes for a target object. The goal of the study is to identify which stage of the visual search benefits from contextual priors. Two groups of participants differed in the expectation of target presence associated with a scene. Stronger priors are established when a scene exemplar is always associated with the presence of the target than when the scene is periodically observed with and without the target. In both cases, overall search performance improves over repeated presentations of scenes. An analytic decomposition of the time course of the effect of contextual priors shows a time benefit to the exploration stage of search (scan time) and a decrease in gaze duration on the target. The strength of the contextual relationship modulates the magnitude of gaze duration gain, while the scan time gain constitutes one half of the overall search performance benefit regardless of the probability (50% or 100%) of target presence. These data are discussed in terms of the implications of contextdependent scene processing and its putative role in various stages of visual search.

关键词： Humans Layout Performance gain Learning computer science Artificial intelligence Laboratories computerized monitoring Cellular phones Lamps

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Unsupervised Segmentation of Multi-Modal Images by a Precise Approximation of Individual Modes with Linear Combinations of Discrete Gaussians

Unsupervised Segmentation of Multi-Modal Images by a Precise...

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

作者： A. El-Baz R.M. Mohamed A.A. Farag G. Gimel'farb CVIP Laboratory Department of Electrical and Computer Engineering University of Louisville Louisville KY USA Department of Computer Science Tamaki Campus University of Auckland Auckland New Zealand

Many important applications of image analysis deal with multi-modal images such that each object of interest relates to an individual mode in the marginal signal distribution collected over the image. Segmentation of such seemingly simple images is nonetheless a challenging problem because each meaningful boundary between the objects is rarely formed by easily detectable signal differences (or "edges"). Most commonly, the signals have very close values across the boundary and relate to intersecting tails of distributions describing individual objects. To accurately segment such images, not only the main body but also the tails of each such distribution have to be precisely recovered from the available mixture. We present a *** version of our novel EM-based algorithm for accurate unsupervised segmentation of multi-modal grayscale images. It has a considerably improved convergence to a local maximum of the image likelihood and provides a very close approximation of each distribution related to the mode with a linear combination of sign-alternate discrete Gaussian kernels. Experiments with medical images show the proposed segmentation is more accurate than several other known alternatives.

关键词： Image segmentation Gaussian approximation Image edge detection Probability distribution Object detection Signal detection Gray-scale Convergence Gaussian distribution Kernel

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The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras

The radial trifocal tensor: a tool for calibrating the radia...

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Thirthala M. Pollefeys Department of Computer Science UNC Chapel Hill USA

We present a technique to linearly estimate the radial distortion of a wide-angle lens given three views of a real-world plane. The approach can also be used with pure rotation as in this case all points appear as lying on a plane. The three views can even be recorded using three different cameras as long as the deviation from the pin-hole model for each camera is distortion along radial lines. We introduce the 1D radial camera which projects scene points onto radial lines and the radial trifocal tensor which encodes the multi-view relations between radial lines. Given at least seven triplets of corresponding points the radial trifocal tensor can be computed linearly. This allows recovery of the radial cameras and the projective reconstruction of the plane up to a two fold ambiguity. This 2D reconstruction is unaffected by radial distortion and can be used in different ways to compute the radial distortion parameters. We propose to use the division model as in this case we obtain a linear algorithm that computes the radial distortion coefficients and the 3 remaining degrees of freedom of the homography relating the reconstructed 2D plane to the undistorted image. Each feature point that has at least one corresponding point yields one linear constraint on those unknowns. Our method is validated on real-world images. We successfully calibrate several wide-angle cameras.

关键词： Tensile stress Cameras Nonlinear distortion Calibration Lenses Layout Image reconstruction computer vision computer science Mirrors

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A Combinational Approach to the Fusion, De-noising and Enhancement of Dual-Energy X-Ray Luggage Images

A Combinational Approach to the Fusion, De-noising and Enhan...

引用

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

作者： Zhiyu Chen Yue Zheng B.R. Abidi D.L. Page M.A. Abidi Imaging Robotics and Intelligent Systems Laboratory Electrical and Computer Engineering Department University of Tennessee Knoxville TN USA

X-ray luggage inspection systems play an important role in ensuring air travelers’ security. However, the false alarm rate of commercial systems can be as high as 20% due to less than perfect image processing algorithms. In an effort to reduce the false alarm rate, this paper proposes a combinational scheme to fuse, de-noise and enhance dual-energy X-ray images for better object classification and threat detection. The fusion step is based on the wavelet transform. Fused images generally reveal more detail information; however, background noise often gets amplified during the fusion process. This paper applies a backgroundsubtraction- based noise reduction technique which is very efficient in removing background noise from fused X-ray images. The de-noised image is then processed using a new enhancement technique to reconstruct the final image. The final image not only contains complementary information from both source images, but is also background-noise-free and contrastenhanced, therefore easier to segment automatically or be interpreted by screeners, thus reducing the false alarm rate in X-ray luggage inspection.

关键词： Noise reduction X-ray imaging Inspection Background noise Security Image processing Fuses Object detection X-ray detection X-ray detectors

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3D Measurements in Cargo Inspection with a Gamma-Ray Linear Pushbroom Stereo System

3D Measurements in Cargo Inspection with a Gamma-Ray Linear ...

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

作者： Zhigang Zhu Li Zhao Jiayan Lei Department of Computer Science The City College City University of New York NY USA

In this paper we present a practical approach for 3D measurements in gamma-ray (or X-ray) cargo inspection. The linear pushbroom sensor model is used for such a gamma-ray scanning system. Thanks to the constraints of the real scanning system, we model the system by using a linear pushbroom model with only one rotation angle instead of three. This greatly simplifies the calibration procedure and increases the robustness of the parameter estimation. Using only the knowledge of the dimensions of a cargo container, we automatically calibrate the sensor and find all the sensor parameters, including the image center, the focal length, the 3D sensor starting location, the viewing direction, and the scanning speed. Then, a semi-automated stereo reconstruction approach is proposed to obtain 3D measurements of objects inside the cargo by using two such scanning systems with different scanning angles to construct a pushbroom stereo system. Experimental results of 3D measurements and visualization of a 3D cargo container and the objects inside are presented. With both the interactive matching procedure and the 3D visualization interface, the 3D measurements could add more value to today’s cargo inspection systems.

关键词： Inspection Image sensors Containers Stereo image processing Visualization Sensor systems Gamma ray detectors Calibration Robustness Parameter estimation

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