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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

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

作者： De Zhang Yunhong Wang Bir Bhanu Center for Research in Intelligent Systems University of California Riverside CA USA Intelligent Recognition and Image Processing Laboratory Beihang University Beijing China

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy Image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x; y; view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs the best.

关键词： Cameras Tensile stress Biometrics Surveillance Image analysis Image recognition Rendering (computer graphics) Principal component analysis Feature extraction Voting

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Deep Convolutional Sparse Coding Networks for Interpretable Image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

引用

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

作者： Zixiang Zhao Jiangshe Zhang Haowen Bai Yicheng Wang Yukun Cui Lilun Deng Kai Sun Chunxia Zhang Junmin Liu Shuang Xu Xi’an Jiaotong University Computer Vision Lab ETH Zurich The University of Melbourne Research and Development Institute of Northwestern Polytechnical University in Shenzhen Northwestern Polytechnical University

Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection.

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Efficient Multi-Lens Bokeh Effect Rendering and Transformation

Efficient Multi-Lens Bokeh Effect Rendering and Transformati...

引用

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

作者： Tim Seizinger Marcos V. Conde Manuel Kolmet Tom E. Bishop Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany Glass Imaging Inc. CA

Many advancements of mobile cameras aim to reach the visual quality of professional DSLR cameras. Great progress was shown over the last years in optimizing the sharp regions of an image and in creating virtual portrait effects with artificially blurred backgrounds. Bokeh is the aesthetic quality of the blur in out-of-focus areas of an image. This is a popular technique among professional photographers, and for this reason, a new goal in computational photography is to optimize the Bokeh effect *** paper introduces EBokehNet, a efficient state-of-the-art solution for Bokeh effect transformation and rendering. Our method can render Bokeh from an all-in-focus image, or transform the Bokeh of one lens to the effect of another lens without harming the sharp foreground regions in the image. Moreover we can control the shape and strength of the effect by feeding the lens properties i.e. type (Sony or Canon) and aperture, into the neural network as an additional input. Our method is a winning solution at the NTIRE 2023 Lens-to-Lens Bokeh Effect Transformation Challenge, and state-of-the-art at the EBB benchmark.

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Conformal Rectification of Omnidirectional Stereo Pairs

Conformal Rectification of Omnidirectional Stereo Pairs

引用

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

作者： Christopher Geyer Kostas Daniilidis GRASP Laboratory University of Pennsylvania Philadelphia PA USA Department of EECS University of California Berkeley Berkeley CA USA

A pair of stereo images are said to be rectified if corresponding image points have the same y-coordinate in their respective images. In this paper we consider the rectification of two omnidirectional cameras, specifically two parabolic catadioptric cameras. Such systems consist of a parabolic mirror and an orthographically projecting lens. We show that if the image coordinates are represented as a point z in the complex plane, then the rectification is specified by coth -1z. This rectification is shown to be conformal, in that it is locally distortionless, and furthermore, it is unique up to scale and transformation. We show an experiment in which two real images have been rectified and a stereo matching performed.

关键词： Cameras Geometry Equations computer vision pattern recognition conferences Stereo vision

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Visual Tracking With Group Motion Approach

Visual Tracking With Group Motion Approach

引用

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

作者： A.E. Arslan M. Demirekler Middle East Technical University Ankara Turkey

An algorithm for tracking a single visual target is presented where visual features of target are tracked in a group tracking method. A single visual target is regarded as a group of multiple targets. The model developed allows some variations on the relative positions of the features which corresponds to some sort of non-rigidity. The approach is a combination of PDA and JPDA algorithms and using the advantages of group motion model this approach enables accurate tracking of single targets.

关键词： Target tracking computer vision Bayesian methods Position measurement Personal digital assistants Kalman filters Filtering Vehicle dynamics Vehicles Measurement standards

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Data and model-driven selection using closely-spaced parallel-line groups

Data and model-driven selection using closely-spaced paralle...

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

作者： Syeda-Mahmood Xerox Webster Research Center Webster NY USA

Selecting regions in an image likely to come from a single object is important for reducing the amount of searching involved in object recognition. Such selections can be purely based on image data (data-driven), or based on the knowledge of the model object (model-driven). In this paper, we present methods for data- and model-driven selection by grouping closely-spaced parallel lines in images. Data-driven selection is achieved by selecting salient line groups that emphasize the likelihood of the groups coming from single objects. Model-driven selection is achieved by selectively generating image line groups that are likely to be the projections of the model groups, taking into account the effect of occlusions, illumination changes and imaging errors. We also present results that indicate a vast improvement in the search performance of a recognition system that is integrated with parallel fine group-based selection.< >

关键词： Object recognition Lighting Search methods Image region analysis Image line-pattern analysis

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3D face econstruction from a single 2D face image

3D face econstruction from a single 2D face image

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

作者： Sung Won Park Jingu Heo Marios Savvides Electrical and Computer Engineering Department Carnegie Mellon University Pittsburgh PA USA

T3D face reconstruction from a single 2D image is mathematically ill-posed. However, to solve ill-posed problems in the area of computer vision, a variety of methods has been proposed; some of the solutions are to estimate latent information or to apply model based approaches. In this paper, we propose a novel method to reconstruct a 3D face from a single 2D face image based on pose estimation and a deformable model of 3D face shape. For 3D face reconstruction from a single 2D face image, it is the first task to estimate the depth lost by 2D projection of 3D faces. Applying the EM algorithm to facial landmarks in a 2D image, we propose a pose estimation algorithm to infer the pose parameters of rotation, scaling, and translation. After estimating the pose, much denser points are interpolated between the landmark points by a 3D deformable model and barycentric coordinates. As opposed to previous literature, our method can locate facial feature points automatically in a 2D facial image. Moreover, we also show that the proposed method for pose estimation can be successfully applied to 3D face reconstruction. Experiments demonstrate that our approach can produce reliable results for reconstructing photorealistic 3D faces.

关键词： Face detection Image reconstruction computer vision Deformable models Parameter estimation Shape Facial features Probability Detectors Interference

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Towards Automated Understanding of Student-Tutor Interactions Using Visual Deictic Gestures

Towards Automated Understanding of Student-Tutor Interaction...

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

作者： Suchitra Sathayanarayana Ravi Kumar Satzoda Amber Carini Monique Lee Linda Salamanca Judy Reilly Deborah Forster Marian Bartlett Gwen Littlewort University of California San Diego La Jolla CA US University of California San Diego San Diego State University

In this paper, we present techniques for automated understanding of tutor-student behavior through detecting visual deictic gestures, in the context of one-to-one mathematics tutoring. To the best knowledge of the authors, this is the first work in the area of intelligent tutoring systems, which focuses on spatial localization of deictic gestural activity, i.e. where the deictic gesture is pointing on the workspace. A new dataset called SDMATH is first introduced. The motivation for detecting deictic gestures and their spatial properties is established, followed by techniques for automatic localization of deictic gestures in a workspace. The techniques employ computer vision and machine learning steps such as GBVS saliency, binary morphology and HOG-SVM classification. It is shown that the method localizes the deictic tip with an accuracy of over 85 % accuracy for a cut off distance of 12 pixels. Furthermore, a detailed discussion using examples from the proposed dataset is presented on high-level inferences about the student-tutor interactions that can be derived from the integration of spatial and temporal localization of the deictic gestural activity using the proposed techniques.

关键词： Speech Visualization Cameras Support vector machines Mathematics Bismuth computer vision

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Low Bitrate Image Compression with Discretized Gaussian Mixture Likelihoods

Low Bitrate Image Compression with Discretized Gaussian Mixt...

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

作者： Zhengxue Cheng Heming Sun Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Waseda Research Institute for Science and Engineering Tokyo Japan

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In this paper, we provide a detailed description on our submitted method Kattolab to Workshop and Challenge on Learned Image Compression (CLIC) 2020. Our method mainly incorporates discretized Gaussian Mixture Likeli-hoods to previous state-of-the-art learned compression algorithms. Besides, we also describes the acceleration strategies and bit optimization with the low-rate constraint. Experimental results have demonstrated that our approach Kattolab achieves 0.9761 in terms of MS-SSIM at the rate constraint of 0.15 bpp during the validation phase.

关键词： Image coding Convolution Entropy Training Decoding pattern recognition Bit rate

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Using specular highlights as pose invariant features for 2D-3D pose estimation

Using specular highlights as pose invariant features for 2D-...

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作者： Netz, Aaron Osadchy, Margarita Computer Science University of Haifa Carmel Haifa 31905 Israel

ISBN: (纸本)9781457703942

We address the problem of 2D-3D pose estimation in difficult viewing conditions, such as low illumination, cluttered background, and large highlights and shadows that appear on the object of interest. In such challenging conditions conventional features used for establishing correspondence are unreliable. We show that under the assumption of a dominant light source, specular highlights produced by a known object can be used to establish correspondence between its image and the 3D model, and to verify the hypothesized pose. These ideas are incorporated in an efficient method for pose estimation from a monocular image of an object using only highlights produced by the object as its input. The proposed method uses no knowledge of lighting direction and no calibration object for estimating the lighting in the scene. The evaluation of the method shows good accuracy on numerous synthetic images and good robustness on real images of complex, shiny objects, with shadows and difficult backgrounds1. © 2011 ieee.

关键词： Light sources

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