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Guided sampling via weak motion models and outlier sample generation for epipolar geometry estimation

Guided sampling via weak motion models and outlier sample ge...

引用

conference on computer vision and pattern recognition (cvpr)

作者： L. Goshen I. Shimshoni Faculty of Industrial Engineering & Management Technion-Israel Institute of Technology Haifa Israel

The problem of automatic robust estimation of the epipolar geometry in cases where the correspondences are contaminated with a high percentage of outliers is addressed. This situation often occurs when the images have undergone a significant deformation, either due to large rotation or wide baseline of the cameras. An accelerated algorithm for the identification of the false matches between the views is presented. The algorithm generates a set of weak motion models (WMMs). Each WMM roughly approximates the motion of correspondences from one image to the other. The algorithm represents the distribution of the median of the geometric distances of a correspondence to the WMMs as a mixture model of outlier correspondences and inlier correspondences. The algorithm generates an outlier correspondence sample from the data. This sample is used to estimate the outlier rate and to estimate the outlier pdf. Using these two pdfs the probability that each correspondence is an inlier is estimated. These probabilities enable to guide the sampling. In the RANSAC process this guided sampling accelerates the search process. The resulting algorithm when tested on real images achieves a speedup of between one or two orders of magnitude.

关键词： Sampling methods Solid modeling Motion estimation computer vision Robustness Acceleration Image sampling Computational geometry Industrial engineering Technology management

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A sparse support vector machine approach to region-based image categorization

A sparse support vector machine approach to region-based ima...

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conference on computer vision and pattern recognition (cvpr)

作者： Jinbo Bi Yixin Chen J.Z. Wang Computer Aided Diagnosis and Therapy Solutions Siemens Medical Solutions Inc. Malvern PA USA Department of Computer Science University of New Orleans New Orleans LA USA School of Information Sciences and Technology The Pennsylvania State University State College PA

Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.

关键词： Support vector machines computer vision Image segmentation Support vector machine classification Image classification Histograms Classification algorithms Cities and towns Statistics Bismuth

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Capturing 2 ½ D Depth and Texture of Time-Varying Scenes Using Structured Infrared Light

Capturing 2 ½ D Depth and Texture of Time-Varying Scenes Us...

引用

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

作者： C. Frueh A. Zakhor Department of Computer Science and Electrical Engineering University of California Berkeley USA

In this paper, we describe an approach to simultaneously capture visual appearance and depth of a time-varying scene. Our approach is based on projecting structured infrared (IR) light. Specifically, we project a combination of (a) a static vertical IR stripe pattern, and (b) a horizontal IR laser line sweeping up and down the scene; at the same time, the scene is captured with an IR-sensitive camera. Since IR light is invisible to the human eye, it does not disturb human subjects or interfere with human activities in the scene; in addition, it does not affect the scene’s visual appearance as recorded by a color video camera. Vertical lines in the IR frames are identified using the horizontal line, intra-frame tracking, and inter-frame tracking; depth along these lines is reconstructed via triangulation. Interpolating these sparse depth lines within the foreground silhouette of the recorded video sequence, we obtain a dense depth map for every frame in the video sequence. Experimental results corresponding to a dynamic scene with a human subject in motion are presented to demonstrate the effectiveness of our proposed approach.

关键词： Layout Humans Cameras Image reconstruction Video sequences Motion analysis Image motion analysis Geometry Streaming media Visualization

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Integration of motion fields through shape

Integration of motion fields through shape

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conference on computer vision and pattern recognition (cvpr)

作者： H. Ji C. Fermuller Center for Automation Research University of Maryland College Park MD USA

Structure from motion from single flow fields has been studied intensively, but the integration of information from multiple flow fields has not received much attention. Here we address this problem by enforcing constraints on the shape (surface normals) of the scene in view, as opposed to constraints on the structure (depth). The advantage of integrating shape is two-fold. First, we do not need to estimate feature correspondences over multiple frames, but we only need to match patches. Second, the shape vectors in the different views are related only by rotation. This constraint on shape can be combined easily with motion estimation, thus formulating motion and structure estimation from multiple views as a practical constrained minimization problem using a rank-3 constraint. Based on this constraint, we develop a 3D motion technique, which locates through color and motion segmentation, planar patches in the scene, matches patches over multiple frames, and estimates the motion between multiple frames and the shape of the selected scene patches using the image gradients. Experiments evaluate the accuracy of the 3D motion estimation and demonstrate the motion and shape estimation of the technique by super-resolving an image sequence.

关键词： Shape Motion estimation Layout Cameras Parameter estimation Automation Educational institutions Motion segmentation computer vision Image resolution

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A Local Region-based Approach to Gender *** From Face Images

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

作者： C. BenAbdelkader P. Griffin Department of Computer Science American University of Beirut Beirut Lebanon Identix Inc.orporated Jersey NJ USA

We present a novel appearance-based method for gender classification from face images. To circumvent the problem of local variations in appearance that may be caused by pose, expression, or illumination variability, we use local region analysis of the face to extract the gender classi?cation features. Given a new face image, a normalized feature vector is formed by matching N local regions of the face against some fixed set of M face images using the FaceIt algorithm, then applying the Karhunen-Loeve transform to reduce the dimensionality of this MN-dimensional vector. For the purpose of comparison, we have also implemented a holistic feature extraction method based on the well-known Eigenfaces. Gender classification is performed in a compact feature space via two standard binary classifiers; SVM and FLD. The classifier is tested via cross-validation on a database of approximately 13,000 frontal and nearly frontal face images, and the best performance of 94.2% is achieved with the local region-based feature extraction and SVM classification methods.

关键词： Psychology computer vision Face detection Feature extraction Face recognition Support vector machines Support vector machine classification Testing Image databases Spatial databases

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Multimodal face recognition: combination of geometry with physiological information

Multimodal face recognition: combination of geometry with ph...

引用

conference on computer vision and pattern recognition (cvpr)

作者： I.A. Kakadiaris G. Passalis T. Theoharis G. Toderici I. Konstantinidis N. Murtuza Visual Computing Lab Department of Computer Science University of Houston Clear Lake Houston TX USA

It is becoming increasingly important to be able to credential and identify authorized personnel at key points of entry. Such identity management systems commonly employ biometric identifiers. In this paper, we present a novel multimodal facial recognition approach that employs data from both visible spectrum and thermal infrared sensors. Data from multiple cameras is used to construct a three-dimensional mesh representing the face and a facial thermal texture map. An annotated face model with explicit two-dimensional parameterization (UV) is then fitted to this data to construct: 1) a three-channel UV deformation image encoding geometry, and 2) a one-channel UV vasculature image encoding facial vasculature. recognition is accomplished by comparing: 1) the parametric deformation images, 2) the parametric vasculature images, and 3) the visible spectrum texture maps. The novelty of our work lies in the use of deformation images and physiological information as means for comparison. We have performed extensive tests on the Face recognition Grand Challenge v1.0 dataset and on our own multimodal database with very encouraging results.

关键词： Face recognition Information geometry Image coding Personnel Identity management systems Biometrics Infrared sensors Cameras Solid modeling Deformable models

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Full-frame video stabilization

Full-frame video stabilization

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conference on computer vision and pattern recognition (cvpr)

作者： Yasuyuki Matsushita Eyal Ofek Xiaoou Tang Heung-Yeung Shum Beijing Sigma Center Microsoft Research Asia Beijing China

Video stabilization is an important video enhancement technology which aims at removing annoying shaky motion from videos. We propose a practical and robust approach of video stabilization that produces full-frame stabilized videos with good visual quality. While most previous methods end up with producing low resolution stabilized videos, our completion method can produce full-frame videos by naturally filling in missing image parts by locally aligning image data of neighboring frames. To achieve this, motion inpainting is proposed to enforce spatial and temporal consistency of the completion in both static and dynamic image areas. In addition, image quality in the stabilized video is enhanced with a new practical deblurring algorithm. Instead of estimating point spread functions, our method transfers and interpolates sharper image pixels of neighbouring frames to increase the sharpness of the frame. The proposed video completion and deblurring methods enabled us to develop a complete video stabilizer which can naturally keep the original image quality in the stabilized videos. The effectiveness of our method is confirmed by extensive experiments over a wide variety of videos.

关键词： Layout Videoconference Robustness Filling Image quality Video sequences Sampling methods computer vision Asia Spatial resolution

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Hand tracking with Flocks of Features

Hand tracking with Flocks of Features

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conference on computer vision and pattern recognition (cvpr)

作者： M. Kolsch M. Turk Computer Science Department Naval Postgraduate School Monterrey CA USA Computer Science Department University of California Santa Barbara CA USA

Tracking hands in live video is a challenging task: the hand appearance can change too rapidly for appearance-based trackers to work, and color-based trackers (that do not rely on geometry) have to make limiting assumptions about the background color. This article shows the results of hand tracking with "Flocks of Features", a tracking method that combines motion cues and a learned foreground color distribution to achieve fast and robust 2D tracking of highly articulated objects. Many independent image artifacts are tracked from one frame to the next, adhering only to local constraints. This concept is borrowed from nature since these tracks mimic the flight of flocking birds - exhibiting local individualism and variability while maintaining a clustered entirety. Hand tracking has important applications for interaction with wearable computers, for intuitive manipulation of virtual objects, for detection of activity signatures, and much more. Tracking with Flocks of Features is not limited to hands - any articulated or appearance-changing object can benefit from this multi-cue tracking method.

关键词： Target tracking computer science Computational geometry Birds Colored noise Color Noise robustness Background noise Cameras Testing

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Estimation of 2D motion trajectories from video object planes and its application in hand gesture recognition

引用

1st International conference on pattern recognition and Machine Intelligence

作者： Bhuyan, MK Ghosh, D Bora, PK Indian Inst Technol Dept Elect & Commun Engn Gauhati 781039 India

ISBN: (纸本)3540305068

Hand gesture recognition from visual images finds applications in areas like human computer interaction, machine vision, virtual reality and so on. vision-based hand gesture recognition involves visual analysis of hand shape, position and/or movement. In this paper, we present a model-based method for tracking hand motion in a complex scene, thereby estimating the hand motion trajectory. In our proposed technique, we first segment the frames into video object planes (VOPs) with the hand as the video object. This is followed by hand tracking using Hausdorff tracker. In the next step, the centroids of all VOPs are calculated using moments as well as motion information. Finally, the hand trajectory is estimated by joining the VOP centroids. In our experiment, the proposed trajectory estimation algorithm gives about 99% accuracy A in finding the actual trajectory.

关键词： Motion pictures

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3D geometric and optical modeling of warped document images from scanners

3D geometric and optical modeling of warped document images ...

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conference on computer vision and pattern recognition (cvpr)

作者： Li Zhang Zheng Zhang Chew Lim Tan Tao Xia School of Computing National University of Singapore Singapore Department of Mathematics National University of Singapore Singapore

ISBN: (纸本)0769523722

When one scans a document page from a thick bound volume, the curvature of the page to be scanned results in two kinds of distortion in the scanned document images: i) shade along the 'spine' of the book; and ii) warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. We first build practical models namely a 3D geometric model and a 3D optical model for the practical scanning conditions to reconstruct the 3D shape of book surface. We next restore the scanned document image using this shape based on de-shading and de-warping models. Finally, we evaluate the restoration results by comparing the OCR (optical character recognition) performance on the original and restored document images. The experiments show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.

关键词： Geometrical optics Solid modeling Optical character recognition software Optical distortion Image restoration Optical variables control Books Shape Surface reconstruction Image reconstruction

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