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recognition of walking humans in 3D: Initial results

Recognition of walking humans in 3D: Initial results

引用

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

作者： Koichiro Yamauchi Bir Bhanu Hideo Saito Center for Research in Intelligent Systems University of California Riverside CA USA Keio University Yokohama Japan

It has been challenging to recognize walking humans at arbitrary poses from a single or small number of video cameras. Attempts have been made mostly using a 2D image/silhouette-based representation and a limited use of 3D kinematic model-based approaches. In this paper, the problem of recognizing walking humans at arbitrary poses is addressed. Unlike all the previous work in computer vision and pattern recognition the models of walking humans are built using the sensed 3D range data at selected poses without any markers. An instance of a walking individual at a different pose is recognized using the 3D range data at that pose. Both modeling and recognition of an individual are done using the dense 3D range data. The proposed approach first measures 3D human body data that consists of the representative poses during a gait cycle. Next, a 3D human body model is fitted to the body data using an approach that overcomes the inherent gaps in the data and estimates the body pose with high accuracy. A gait sequence is synthesized by interpolation of joint positions and their movements from the fitted body models. Both dynamic and static gait features are obtained which are used to define a similarity measure for an individual recognition in the database. The experimental results show high recognition rates using our range based 3D gait database.

关键词： Legged locomotion Humans Biological system modeling Spatial databases Cameras Kinematics computer vision pattern recognition Anthropometry Interpolation

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A projector-camera system for creating a display with water drops

A projector-camera system for creating a display with water ...

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

作者： Peter Barnum Srinivasa Narasimhan Takeo Kanade Carnegie Mellon Univ. Pittsburgh PA USA

Various non-traditional media, such as water drops, mist, and fire, have been used to create vibrant two and three dimensional displays. Usually such displays require a great deal of design and engineering. In this work, we show a computer vision based approach to easily calibrate and learn the properties of a three-dimensional water drop display, using a few pieces of off-the-shelf hardware. Our setup consists of a camera, projector, laser plane, and water drop generator. Based on the geometric calibration between the hardware, a user can “paint” the drops from the point of view of the camera, causing the projector to illuminate them with the correct color at the correct time. We first demonstrate an algorithm for the case where no drop occludes another from the point of view of either camera or projector. If there is no occlusion, the system can be trained once, and the projector plays a precomputed movie. We then show our work toward a display with real rain. In real time, our system tracks and predicts the future location of hundreds of drops per second, then projects rays to hit or miss each drop.

关键词： computer displays Cameras Three dimensional displays Hardware Fires Design engineering computer vision Water Calibration Color

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Shape Google: a computer vision approach to isometry invariant shape retrieval

Shape Google: a computer vision approach to isometry invaria...

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International conference on computer vision workshops (ICCV workshops)

作者： Maks Ovsjanikov Alexander M. Bronstein Michael M. Bronstein Leonidas J. Guibas ICME University of Stanford USA Department of Computer Science Technion-Israel Institute of Technology Israel Department of Computer Science University of Stanford USA

Feature-based methods have recently gained popularity in computer vision and pattern recognition communities, in applications such as object recognition and image retrieval. In this paper, we explore analogous approac... 详细信息

关键词： Shape computer vision Image retrieval Robustness computer science Information retrieval Image databases Spatial databases pattern recognition Object recognition

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Mining discriminative adjectives and prepositions for natural scene recognition

Mining discriminative adjectives and prepositions for natura...

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

作者： Bangpeng Yao Juan Carlos Niebles Li Fei-Fei Department of Computer Science Princeton University NJ USA Robotics and Intelligent Systems Group Universidad del Norte Colombia Department of Electrical Engineering Princeton University NJ USA

This paper presents a method that considers not only patch appearances, but also patch relationships in the form of adjectives and prepositions for natural scene recognition. Most of the existing scene categorization approaches only use patch appearances or co-occurrence of patch appearances to determine the scene categories, but the relationships among patches remain ignored. Those relationships are, however, critical for recognition and understanding. For example, a `beach' scene can be characterized by a `sky' region above `sand', and a `water' region between `sky' and `sand'. We believe that exploiting such relations between image regions can improve scene recognition. In our approach, each image is represented as a spatial pyramid, from which we obtain a collection of patch appearances with spatial layout information. We apply a feature mining approach to get discriminative patch combinations. The mined patch combinations can be interpreted as adjectives or prepositions, which are used for scene understanding and recognition. Experimental results on a fifteen class scene dataset show that our approach achieves competitive state-of-the-art recognition accuracy, while providing a rich description of the scene classes in terms of the mined adjectives and prepositions.

关键词： Layout Road transportation Image representation Intelligent robots Image recognition Data mining computer science Intelligent systems Histograms

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Human Action recognition with Extremities as Semantic Posture Representation

Human Action Recognition with Extremities as Semantic Postur...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Elden Yu J. K. Aggarwal Computer and Vision Research Center The University of Texas at Austin

ISBN: (纸本)9781424439942

In this paper, we present an approach for human action recognition with extremities as a compact semantic posture representation. First, we develop a variable star skeleton representation (VSS) in order to accurately find human extremities from contours. Earlier, Fujiyoshi and Upton [7] proposed an image skeletonization technique with the center of mass as a single star for rapid motion analysis. Yu and Aggarwal [18] used the highest contour point as the second star in their application for fence climbing detection. We implement VSS and earlier algorithms from [7, 18], and compare their performance over a set of 1000 frames from 50 sequences of persons climbing fences to analyze the characteristic of each representation. Our results show that VSS performs the best. Second, we build feature vectors out of detected extremities for Hidden Markov Model (HMM) based human action recognition. On the data set of human climbing fences, we achieved excellent classification accuracy. On the publicly available Blank et al. [3] data set, our approach showed that using only extremities is sufficient to obtain comparable classification accuracy against other state-of-the-art performance. The advantage of our approach lies in the less time complexity with comparable classification accuracy.

关键词： Humans Extremities Skeleton Variable structure systems Cameras computer vision Motion analysis Hidden Markov models Videos Performance analysis

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Evaluation of spatio-temporal regional features For 3D face analysis

Evaluation of spatio-temporal regional features For 3D face ...

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

作者： Yi Sun Lijun Yin Department of Computer Science State University of New York Binghamton Binghamton NY USA

3D facial representations have been widely used for face recognition and facial expression recognition. Both local and global features can be extracted from either static or dynamic models in both spatial and temporal domains. 3D local features are referred to the features in regional facial areas while 3D global features are referred to the features from the entire facial region. In this paper, we address the issue of performance assessment of facial analysis in terms of global features versus local features, static models versus dynamic models, and spatial domain versus temporal domain. Based on the existing work of using 3D spatio-temporal HMM for facial analysis, we propose to extend it to a local-temporal HMM in order to provide an explicit comparison of global features and local features. A dynamic 3D facial expression database and a static facial expression database are used for experiments. The performance for six prototypic facial expression classification and face identification is analyzed and reported.

关键词： Hidden Markov models Face recognition Performance analysis Spatial databases Image databases Prototypes Labeling Vectors Sun computer science

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3D Stochastic Completion Fields for Fiber Tractography

3D Stochastic Completion Fields for Fiber Tractography

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Parya Momayyez Kaleem Siddiqi School of Computer Science Centre for Intelligent Machines McGill University

ISBN: (纸本)9781424439942

We approach the problem of fiber tractography from the viewpoint that a computational theory should relate to the underlying quantity that is being measured-the diffusion of water molecules. We characterize the brownian motion of water by a 3D random walk described by a stochastic nonlinear differential equation. We show that the maximum-likelihood trajectories are 3D elastica, or curves of least energy. We illustrate the model with Monte-Carlo (sequential) simulations and then develop a more efficient (local, parallelizable) implementation, based on the Fokker-Planck equation. The final algorithm allows us to efficiently compute stochastic completion fields to connect a source region to a sink region, while taking into account the underlying diffusion MRI data. We demonstrate promising tractography results using high angular resolution diffusion data as input.

关键词： Stochastic processes Image segmentation Brain Image registration Neuroscience Acceleration Deformable models Optimization methods Collision mitigation Application software

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Remote audio/video acquisition for human signature detection

Remote audio/video acquisition for human signature detection

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

作者： Yufu Qu Tao Wang Zhigang Zhu Department of Computer Science City College of New York New York NY USA Department of Computer Science City University of New York New York NY USA

To address the challenges of noncooperative, large-distance human signature detection, we present a novel multimodal remote audio/video acquisition system. The system mainly consists of a laser Doppler virbometer (LDV) and a pan-tilt-zoom (PTZ) camera. The LDV is a unique remote hearing sensor that uses the principle of laser interferometry. However, it needs an appropriate surface to modulate the speech of a human subject and reflect the laser beam to the LDV receiver. The manual operation to turn the laser beam onto a target is very difficult at a distance of more than 20 meters. Therefore, the PTZ camera is used to capture the video of the human subject, track the subject when he/she moves, and analyze the image to get a good reflection surface for LDV measurements in real-time. Experiments show that the integration of those two sensory components is ideal for multimodal human signature detection at a large distance.

关键词： Humans Cameras Laser beams Auditory system Interferometry Surface emitting lasers Optical modulation Speech Manuals Target tracking

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Bicycle Chain Shape Models

Bicycle Chain Shape Models

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Stefan Sommer Aditya Tatu Chen Chen Dan R. Jorgensen Marleen de Bruijne Marco Loog Mads Nielsen Francois Lauze Department of Computer Science University of Copenhagen Denmark Department of Computer Science University of Copenhagen Denmark Nordic Bioscience A/S Herlev Denmark Department of Computer Science University of Copenhagen Department of Computer Science University of Copenhagen Denmark ICT Group Delft University of Technology The Netherlands

ISBN: (纸本)9781424439942

In this paper we introduce landmark-based pre-shapes which allow mixing of anatomical landmarks and pseudo-landmarks, constraining consecutive pseudo-landmarks to satisfy planar equidistance relations. This defines naturally a structure of Riemannian manifold on these preshapes, with a natural action of the group of planar rotations. Orbits define the shapes. We develop a Geodesic Generalized Procrustes Analysis procedure for a sample set on such a preshape spaces and use it to compute Principal Geodesic Analysis. We demonstrate it on an elementary synthetic example as well on a dataset of manually annotated vertebra shapes from X-ray. We re-landmark them consistently and show that PGA captures the variability of the dataset better than its linear counterpart, PCA.

关键词： Bicycles Shape Image analysis Biomedical imaging X-ray imaging Spine Orbits Geophysics computing Electronics packaging Principal component analysis

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Background Subtraction in Varying Illuminations Using an Ensemble Based on an Enlarged Feature Set

Background Subtraction in Varying Illuminations Using an Ens...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Brendan Klare Sudeep Sarkar Dept of Computer Science and Engineering Michigan State University Dept of Computer Science and Engineering University of South Florida

ISBN: (纸本)9781424439942

Image sequences with dynamic backgrounds often cause false classification of pixels. In particular, varying illuminations cause significant changes in the representation of a scene in different color spaces, which in turn results in the high levels of failure in such conditions. Because mapping to alternate color spaces has largely failed to solve this problem, a solution of using alternate image features is proposed in this paper. In particular, the use of gradient and texture features along with the original color intensities are used in an ensemble of Mixture of Gaussians background classifiers. A clear improvement is shown when using this method compared to the Mixture of Gaussians algorithm using only color intensities. In addition, this work shows that performing background subtraction using only gradient magnitude as an image feature performs at a much higher rate in varying illuminations then using color intensities. Results are generated on three separate datasets, each with unique, dynamic, illumination conditions.

关键词： Lighting Gaussian distribution Gaussian processes Image segmentation computer science Image sequences Layout Iterative algorithms Target tracking Equations

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