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Three-Dimensional Shape and Motion Reconstruction for the Analysis of American Sign Language

Three-Dimensional Shape and Motion Reconstruction for the An...

引用

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

作者： Liya Ding A.M. Martinez Department of Electrical and Computer Engineering Ohio State Uinversity USA

The design of systems that can analyze and recognize American Sign Language (ASL) sentences, require that motion patterns and handshapes be independently represented from one another. In this paper, we define an algorithm that first obtains the 3D shape of each of the linguistically significant fingers (known as "selected" fingers in the linguistic literature), and then represents the motion of the hand as a three-dimensional trajectory in the coordinate system of the camera. In doing so, we address the problems caused by self-occlusions and localization errors. The 3D shape is obtained using an affine structure from motion algorithm that is based on the linear fitting of matrices with missing data. To recover the 3D motion of the hand, we define a robust algorithm that selects the most stable solution from the pool of all the solutions given by the three point resection problem. To be robust to localization errors (of the hand feature points tracked) we define a simple algorithm that searches for that combination of coordinates that gives the largest number of stable solutions in our system of equations. To validate our theory, we provide a set of experimental results using video sequences of ASL signs.

关键词： Shape Motion analysis Handicapped aids Fingers Robustness pattern analysis pattern recognition Cameras Equations Video sequences

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Integrating Spatial and Discriminant Strength for Feature Selection and Linear Dimensionality Reduction

Integrating Spatial and Discriminant Strength for Feature Se...

引用

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

作者： Qi Li C. Kambhamettu Jieping Ye Video/Image Modeling and Synthesis Laboratory Computer Information & Sciences University of Delaware USA Computer Science & Engineering Arizona State University USA

Interest strength assignment to image points is important for selecting good features. Strength assignments using spatial information aim to detect interest points repeatable across different image/illumination transformations, and have been widely adopted in many interest point detectors. Recently, strength assignment schemes using discriminant information received attention, and studies showed the superiority of discriminant strength. In this paper, we introduce a strength assignment scheme integrating spatial and discriminant information, with the motivation that strong spatial information can be helpful in improving the robustness of the discriminant strength estimation, e.g., in undersampled training scenario. Our integrated strength uses a new discriminant strength assignment, so-called locality oriented Fisher criterion score. The integrated strength leads to new methods for feature selection and weighted linear dimensionality reduction. Experimental results in two case studies (embryo developmental stage classification and face recognition) show the favorable performance of the proposed methods.

关键词： Linear discriminant analysis Detectors Robustness Lighting Autocorrelation computer science Embryo Face recognition Object detection Object recognition

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Towards Robust and Physically Plausible Shaded Stereoscopic Segmentation

Towards Robust and Physically Plausible Shaded Stereoscopic ...

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

作者： Dejun Wang S. Soatto E. Prados Vision Lab Computer Science Department University of California Los Angeles CA USA Perception Laboratory INRIA Rhone-Alpes Montbonnot France

We address the multi-view shape from shading problem, that is the recovery of 3-D shape, lighting configuration and surface albedo from multiple calibrated views. Previous approaches to this problem relied on physically impossible illumination models (negative light) and only work on constant albedo and resulted in biased estimates of shape and lighting positions. Furthermore, since the solution involves infinite-dimensional optimization, existing approaches were quite slow. We develop a new model that explicitly enforces positivity in the light sources with the assumption that the object is Lambertian and its albedo is piecewise constant and show that the new model significantly improves the accuracy and robustness relative to existing approaches. Furthermore, we show that the most computationally expensive step in the optimization can actually be solved in closed form. This significantly improves speed of convergence over existing schemes. We illustrate the behavior of our algorithm directly on the same data used by previous authors, so direct comparison is possible.

关键词： Robustness Shape Lighting Light sources Layout Stereo image processing Image reconstruction Stereo vision computer science Convergence

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TRICam - An Embedded Platform for Remote Traffic Surveillance

TRICam - An Embedded Platform for Remote Traffic Surveillanc...

引用

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

作者： C. Arth H. Bischof C. Leistner Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Technology Center GRAZ FREQUENTIS GmbH Graz Austria

In this paper we present a novel embedded platform, dedicated especially to the surveillance of remote locations under harsh environmental conditions, featuring various video and audio compression algorithms as well as support for local systems and devices. The presented solution follows a radically decentralized approach and is able to act as an autonomous video server. Using up to three Texas InstrumentsTM TMS320C6414 DSPs, it is possible to use high-level computer vision algorithms in real-time in order to extract the information from the video stream which is relevant to the surveillance task. The focus of this paper is on the task of vehicle detection and tracking in images. In particular, we discuss the issues specific for embedded systems, and we describe how they influenced our work. We give a detailed description of several algorithms and justify their use in our implementation. The power of our approach is shown on two real-world applications, namely vehicle detection on highways and license plate detection on urban traffic videos.

关键词： Surveillance Vehicle detection Video compression Audio compression Instruments Digital signal processing computer vision Data mining Streaming media Focusing

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Tree Trunks as Landmarks for Outdoor vision SLAM

Tree Trunks as Landmarks for Outdoor Vision SLAM

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

作者： D.C. Asmar J.S. Zelek S.M. Abdallah Systems Design Engineering University of Waterloo Waterloo ONT Canada Mechanical Engineering Department American University of Beirut Beirut Lebanon

Simultaneous Localization and Mapping (SLAM) of robots is the process of building a map of the robot milieu, while simultaneously localizing the robot inside that map. Cameras have been recently proposed, as a replacement for laser range finders, for the purpose of detecting and localizing landmarks around the navigating robot. vision SLAM is either Interest Point (IP) based, where landmarks are images saliencies, or object-based where real objects are used as landmarks. The contribution of this paper is two prong: first, it details an approach based on Perceptual Organization (PO) to detect and track trees in a sequence of images, thereby promoting the use of a camera as a viable exteroceptive sensor for object-based SLAM; second,it demonstrates the superiority of the suggested PO system over two appearance-based algorithms in segmenting trees from difficult settings. Experiments conducted on a database of 873 images containing approximately 2008 tree trunks, show that the proposed system correctly classifies trees at 81 % with a false positive rate of 30%.

关键词： Simultaneous localization and mapping Sensor systems Cameras Machine vision Navigation Global Positioning System Design engineering Systems engineering and theory Robot vision systems Robot sensing systems

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Evaluation of Intensity and Color Corner Detectors for Affine Invariant Salient Regions

Evaluation of Intensity and Color Corner Detectors for Affin...

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

作者： N. Sebe T. Gevers S. Dijkstra J. van de Weije Faculty of Science University of Amsterdam Netherlands

Global features are commonly used to describe the image content. The problem with this approach is that these features cannot capture all parts of the image having different characteristics. Therefore, local computation of image information is necessary. By using salient points to represent local information, more discriminative features can be computed. This research is based on an existing affine invariant local feature detector, in which the features are assumed to be intensity corners. First, the existing algorithm is extended with the intensity based SUSAN corner detector which fundamentally differs from the original Harris corner detector. Second, the algorithm is extended to incorporate color information into the detection process. This results in a comparison between three different detection algorithms: the intensity based algorithm using the Harris or SUSAN detector and a color based algorithm that uses two color extended Harris detectors. The different algorithms are compared in terms of invariance and distinctiveness of the regions and computational complexity.

关键词： Detectors Colored noise Color Layout computer vision Detection algorithms Computational complexity Retina Performance analysis Abstracts

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Model-Based Face De-Identification

Model-Based Face De-Identification

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

作者： R. Gross L. Sweeney F. de la Torre S. Baker Data Privacy Lab School of Computer Science Carnegie Mellon University USA Robotics Institute Carnegie Mellon University USA

Advances in camera and computing equipment hardware in recent years have made it increasingly simple to capture and store extensive amounts of video data. This, among other things, creates ample opportunities for the sharing of video sequences. In order to protect the privacy of subjects visible in the scene, automated methods to de-identify the images, particularly the face region, are necessary. So far the majority of privacy protection schemes currently used in practice rely on ad-hoc methods such as pixelation or blurring of the face. In this paper we show in extensive experiments that pixelation and blurring offers very poor privacy protection while significantly distorting the data. We then introduce a novel framework for de-identifying facial images. Our algorithm combines a model-based face image parameterization with a formal privacy protection model. In experiments on two large-scale data sets we demonstrate privacy protection and preservation of data utility.

关键词： Protection Data privacy Cameras Video sharing Layout Biomedical monitoring Biomedical imaging Senior citizens computer science Robots

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Open Hand Detection in a Cluttered Single Image using Finger Primitives

Open Hand Detection in a Cluttered Single Image using Finger...

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

作者： M.B. Caglar N. Lobo University of Central Florida Orlando FL USA

Hand Detection plays an important role in human computer interaction (HCI) applications, as well as surveillance. We propose a hand detection technique that is robust to different skin color, illumination and shadow irregularities by exploiting the geometric properties of the hand. We first obtain the responses from two detectors that operate independently on the test image to identify parallel finger edges and curved fingertips. These responses are then grouped by using two decision trees trained on each primitive class, yielding two separate collections of groups. The final merging algorithm returns candidate hands in a given single image by comparing groups across each collection and merging those that satisfy a scoring function. The proposed system is robust to the size and the orientation of the hand, with the single requirement that one or more fingers are visible. The system is the first to successfully detect hands in an uncontrolled environment, without training on the skin color within a single image or using motion information.

关键词： Fingers Human computer interaction Robustness Skin Image edge detection Merging Application software Surveillance Lighting Detectors

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Example Based 3D Reconstruction from Single 2D Images

Example Based 3D Reconstruction from Single 2D Images

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

作者： T. Hassner R. Basri Weizmann Institute of Science Rehovot Israel

We present a novel solution to the problem of depth reconstruction from a single image. Single view 3D reconstruction is an ill-posed problem. We address this problem by using an example-based synthesis approach. Our method uses a database of objects from a single class (e.g. hands, human figures) containing example patches of feasible mappings from the appearance to the depth of each object. Given an image of a novel object, we combine the known depths of patches from similar objects to produce a plausible depth estimate. This is achieved by optimizing a global target function representing the likelihood of the candidate depth. We demonstrate how the variability of 3D shapes and their poses can be handled by updating the example database on-the-fly. In addition, we show how we can employ our method for the novel task of recovering an estimate for the occluded backside of the imaged objects. Finally, we present results on a variety of object classes and a range of imaging conditions.

关键词： Image reconstruction Shape Image databases Humans Marine animals Rendering (computer graphics) pattern matching Graphical models Batteries Degradation

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3D Surveillance A Distributed Network of Smart Cameras for Real-Time Tracking and its Visualization in 3D

3D Surveillance A Distributed Network of Smart Cameras for R...

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

作者： S. Fleck F. Busch P. Biber W. Straber WSI/GRIS University of Tübingen Tübingen Germany

The demand for surveillance systems has increased extremely over recent times. We present a system consisting of a distributed network of cameras that allows for tracking and handover of multiple persons in real time. The intercamera tracking results are embedded as live textures in an integrated 3D world model which is available ubiquitously and can be viewed from arbitrary perspectives independent of the persons’ movements. We mainly concentrate on our implementation of embedded camera nodes in the form of smart cameras and discuss the benefits of such a distributed surveillance network compared to a host centralized approach. We also briefly describe our way of hassle free 3D model acquisition to cover the complete system from setup to operation and finally show some results of both an indoor and an outdoor system in operation.

关键词： Surveillance Smart cameras Visualization Intelligent networks Switches Real time systems Tracking Streaming media computer displays Security

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