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Towards Automatic Retrieval of Blink-Based Lexicon for Persons Suffered from Brain-Stem Injury using Video Cameras

Towards Automatic Retrieval of Blink-Based Lexicon for Perso...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： D.O. Gorodnichy Institute for Information Technology (IIT-ITI) National Research Council Canada Ottawa Canada

Directly connected to the brain, the eyes are the last part of our body we lose control of. For some persons, such as those suffered from a brain-stem stroke, the eyes provide the only means of communication with the world. The eye blinks for such persons are used to make their lexicon and the goal of many rehabilitation centers worldwide is to build tools that would allow automatic detection of the eye blink based lexicon. The tools designed so far are very cumbersome and still do not show the desired performance. At the same time, recent advances in computer hardware and computer vision, in particular, in motion and change detection, offered practitioners a new way for detecting blinks based on video observations of the person's face. This paper overviews different techniques to the problem and describes a vision-based system which is presently being tested in one of the rehabilitation centres. We show how to reliably detect a two-eye blink with a help of an off-the-shelf web-camera and present an approach to the detection a single-eye blink (wink) - this type of blinks is much harder to detect due the lack of spacial constrains, it is however the only type of movement some patients can exhibit.

关键词： Injuries Cameras Face detection Eyes computer vision Motion detection Automatic control Communication system control Hardware System testing

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Semantic-level Understanding of Human Actions and Interactions using Event Hierarchy

Semantic-level Understanding of Human Actions and Interactio...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sangho Park J.K. Aggarwal Electrical and Computer Engineering University of Technology Austin TX USA

Understanding human behavior in video data is essential in numerous applications including surveillance, video annotation/retrieval, and human-computer interfaces. This paper describes a framework for recognizing human actions and interactions in video by using three levels of abstraction. At low level, the poses of individual body parts including head, torso, arms and legs are recognized using individual Bayesian networks (BNs), which are then integrated to obtain an overall body pose. At mid level, the actions of a single person are modeled using a dynamic Bayesian network (DBN) with temporal links between identical states of the Bayesian network at time t and t+1. At high level, the results of mid-level descriptions for each person are juxtaposed along a common time line to identify an interaction between two persons. The linguistic 'verb argument structure' is used to represent human action in terms of triplets. Spatial and temporal constraints are used for a decision tree to recognize specific interactions. A meaningful semantic description in terms of subject-verb-object is obtained. Our method provides a user-friendly natural-language description of several human interactions, and correctly describes positive, neutral, and negative interactions occurring between two persons. Example sequences of real persons are presented to illustrate the paradigm.

关键词： Humans Surveillance Bayesian methods Natural languages Layout pattern recognition computer interfaces Data engineering Application software Information retrieval

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A New Joint Clustering and Diffeomorphism Estimation Algorithm for Non-Rigid Shape Matching

A New Joint Clustering and Diffeomorphism Estimation Algorit...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hongyu Guo A. Rangarajan S.C. Joshi L. Younes Department of CISE University of Florida USA Departments of Radiation Oncology and Biomedical Engineering North Carolina State University Chapel Hill USA Department of Applied Mathematics and Statistics Johns Hopkins University USA

Matching shapes parameterized as unlabeled point-sets is a challenging problem since we have to solve for point correspondences in a non-rigid setting. Previous work on this problem such as modal matching, linear assignment, shape contexts etc. has focused more on the correspondence aspect and not on the non-rigid deformations. The principal motivation for the present work is to establish a distance measure between shapes on a shape manifold. A pre-requisite for achieving this goal is the diffeomorphic matching of point-sets. We show that a joint clustering and diffeomorphism estimation strategy is capable of simultaneously estimating correspondences and a diffeomorphism between unlabeled point-sets. Cluster centers for the two point-sets having the same label are always in correspondence. Essentially, as the cluster centers evolve during the iterations of an incremental EM algorithm, we estimate a diffeomorphism between the two sets of cluster centers. We apply our algorithm to 2D corpus callosum shapes.

关键词： Clustering algorithms Shape measurement Image analysis computer vision Probability distribution Oncology Biomedical engineering Mathematics Statistics Biomedical measurements

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Modeling Individual and Group Actions in Meetings: A Two-Layer HMM Framework

Modeling Individual and Group Actions in Meetings: A Two-Lay...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dong Zhang D. Gatica-Perez S. Bengio I. McCowan G. Lathoud IDIAP Research Institute Martigny Switzerland

We address the problem of recognizing sequences of human interaction patterns in meetings, with the goal of structuring them in semantic terms. The investigated patterns are inherently group-based (defined by the individual activities of meeting participants, and their interplay), and multimodal (as captured by cameras and microphones). By defining a proper set of individual actions, group actions can be modeled as a two-layer process, one that models basic individual activities from low-level audio-visual features, and another one that models the interactions. We propose a two-layer Hidden Markov Model (HMM) framework that implements such concept in a principled manner, and that has advantages over previous works. First, by decomposing the problem hierarchically, learning is performed on low-dimensional observation spaces, which results in simpler models. Second, our framework is easier to interpret, as both individual and group actions have a clear meaning, and thus easier to improve. Third, different HMM models can be used in each layer, to better reflect the nature of each subproblem. Our framework is general and extensible, and we illustrate it with a set of eight group actions, using a public five-hour meeting corpus. Experiments and comparison with a single-layer HMM baseline system show its validity.

关键词： Hidden Markov models pattern recognition Humans Speech recognition Cameras Microphones Sensor systems Psychology Teamwork Context modeling

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Face recognition in the Thermal Infrared Spectrum

Face Recognition in the Thermal Infrared Spectrum

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： P. Buddharaju I. Pavlidis I. Kakadiaris Computer Science Department University of Houston Houston TX USA

We present a two-stage face recognition method based on infrared imaging and statistical modeling. In the first stage we reduce the search space by finding highly likely candidates before arriving at a singular conclusion during the second stage. Previous work has shown that Bessel forms model accurately the marginal densities of filtered components and can be used to find likely matches but not a unique solution. We present an enhancement to this approach by applying Bessel modeling on the facial region only rather than the entire image and by pipelining a classification algorithm to produce a unique solution. The detailed steps of our method are as follows: First, the faces are separated from the background using adaptive fuzzy connectedness segmentation. Second, Gabor filtering is used as a spectral analysis tool. Third, the derivative filtered images are modeled using two-parameter Bessel forms. Fourth, high probability subjects are short-listed by applying the L^2 -norm on the Bessel models. Finally, the resulting set of highly likely matches is fed to a Bayesian classifier to find the exact match. We show experimentally that segmentation of the facial regions results in better hypothesis pruning and classification performance. We also present comparative experimental results with an eigenface approach to highlight the potential of our method.

关键词： Face recognition Infrared spectra Image segmentation Gabor filters Infrared imaging Matched filters Pipeline processing Classification algorithms Filtering Spectral analysis

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Mosaicing with Parallax using Time Warping

Mosaicing with Parallax using Time Warping

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A. Rav-Acha Y. Shor S. Peleg School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel

2D image alignment methods are applied successfully for mosaicing aerial images, but fail when the camera moves in a 3D scene. Such methods can not handle 3D parallax, resulting in distorted mosaicing. General egomotion methods are slow, and do not have the robustness of 2D alignment methods. We propose to use the x-y-t space-time volume as a tool for depth invariant mosaicing. When the camera moves on a straight line in the x direction, a y-t slice of the space-time volume is a panoramic mosaic, while a x-t slice is an EPI plane. Time warping, which is a resampling of the t axix, is used to form straight feature lines in the EPI planes. This process will simultaneously give best panoramic mosaic in the y-t slices. This approach is as robust as 2D alignment methods, while giving depth invariant motion ("ego motion"). Extensions for two dimensional camera motion on a plane are also described, with applications for 2D mosaicing, and for image based rendering such as "light field".

关键词： Cameras Layout Robustness Rendering (computer graphics) Photography computer science Cities and towns Inspection Contracts Motion control

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Epipolar Contrained User Pushbutton Selection in Projected Interfaces

Epipolar Contrained User Pushbutton Selection in Projected I...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： A. Kale K. Kwan C. Jaynes UK Center of Visualization and Virtual Reality Lexington KY USA

An almost ubiquitous user interaction in most HCI applications is the task of selecting one of out of a given list of options. For example, in common desktop environments, the user moves the mouse pointer to the desired option and clicks it. The analog of this action in projector-camera HCI environments involves the user raising her finger to touch one of the different virtual buttons projected on a display surface. In this paper, we discuss some of the challenges involved in tracking and recognizing this task in an projected immersive environment and present a hierarchical vision based approach to detect intuitive gesture-based "mouse clicks" in a front-projected virtual interface. Given the difficulty of tracking user gestures directly in a projected environment, our approach first tracks shadows cast on the display by the user and exploits the multi-view geometry of the camera-projector pair to constrain a subsequent search for the users hand position in the scene. The method only requires a simple setup step in which the projector's epipole in the camera’s frame is estimated. We demonstrate how this approach is capable of detecting a contact event as a user interacts with a virtual pushbutton display. Results demonstrate that camera-based monitoring of user gesture is feasible even under difficult conditions in which the user is illluminated by changing and saturated colors.

关键词： Human computer interaction computer displays Event detection Application software computer vision Visualization Virtual reality Mice Fingers Geometry

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Learning in Region-Based Image Retrieval with Generalized Support Vector Machines

Learning in Region-Based Image Retrieval with Generalized Su...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： I. Gondra D.R. Heisterkamp Computer Science Department Oklahoma State University Stillwater OK USA

Relevance feedback approaches based on support vector machine (SVM) learning have been applied to significantly improve retrieval performance in content-based image retrieval (CBIR). Those approaches require the use of fixed-length image representations because SVM kernels represent an inner product in a feature space that is a non-linear transformation of the input space. Many region-based CBIR approaches create a variable length image representation and define a similarity measure between two variable length representations. The standard SVM approach cannot be applied to this approach because it violates the requirements that SVM places on the kernel. Fortunately, a generalized SVM (GSVM) has been developed that allows the use of an arbitrary kernel. In this paper, we present an initial investigation into utilizing a GSVM-based relevance feedback learning algorithm. Since GSVM does not place restrictions on the kernel, any image similarity measure can be used. In particular, the proposed approach uses an image similarity measure developed for region-based, variable length representations. Experimental results over real world images demonstrate the efficacy of the proposed method.

关键词： Machine learning Image retrieval Support vector machines Image segmentation Content based retrieval Kernel Feedback computer science Image representation Length measurement

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Automatic Single View Building Reconstruction by Integrating Segmentation

Automatic Single View Building Reconstruction by Integrating...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Feng Han Song-Chun Zhu Departments of Computer Science and Statistics University of California Los Angeles Los Angeles CA USA

In this paper, we propose a stochastic algorithm using Markov chain Monte Carlo (MCMC) to automatically reconstruct buildings from a single image of architectural scenes by integrating segmentation and reconstruction. Buildings are modelled by two families of generative models: One is parameterized geometric primitives (e.g. boxes, prisms) for 3D structures of buildings. The other is image models for appearances of building surfaces. All the other objects except buildings in the scene are modelled as a 3D background plane with some appearance. Regarding one image of architectural scenes as the 2D projection of the appearances of all the component primitives in the buildings and the background plane to the image plane, we reconstruct buildings under the Bayesian statistical framework by inferring the 3D structure of its component primitives and image models of visible surfaces, which follow some spatial relation prior and reproduce the given image under some projection matrix. The aspect hierarchy is used to generate proposals in primitive space, which can greatly speed up the Markov chain search.

关键词： Buildings Image reconstruction Layout Image segmentation Solid modeling Surface reconstruction Stochastic processes Monte Carlo methods Bayesian methods Proposals

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Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories

Learning Generative Visual Models from Few Training Examples...

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IEEE computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Li Fei-Fei R. Fergus P. Perona California Institute of Technology USA Oxford University UK

Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior information into the learning process. In addition, no algorithm presented in the literature has been tested on more than a handful of object categories. We present an method for learning object categories from just a few training images. It is quick and it uses prior information in a principled way. We test it on a dataset composed of images of objects belonging to 101 widely varied categories. Our proposed method is based on making use of prior information, assembled from (unrelated) object categories which were previously learnt. A generative probabilistic model is used, which represents the shape and appearance of a constellation of features belonging to the object. The parameters of the model are learnt incrementally in a Bayesian manner. Our incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood. The incremental and batch versions have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Both Bayesian methods outperform maximum likelihood on small training sets.

关键词： Bayesian methods Testing Humans Maximum likelihood estimation Assembly Shape Machine vision Image recognition Parameter estimation Image databases

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