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A Smart Sensor with Hyper spectral/Range Fovea and Panoramic Peripheral View

A Smart Sensor with Hyper spectral/Range Fovea and Panoramic...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Tao Wang Zhigang Zhu Harvey Rhody Department of Computer Science Department of Computer Science The CUNY Graduate Center Center for Imaging Science Rochester Institute of Technology

ISBN: (纸本)9781424439942

We propose an adaptive and effective multimodal peripheral-fovea sensor design for real-time targets tracking. This design is inspired by the biological vision systems for achieving real-time target detection and recognition with a hyperspectral/range fovea and panoramic peripheral view. A realistic scene simulation approach is used to evaluate our sensor design and the related data exploitation algorithms before a real sensor is made. The goal is to reduce development time and system cost while achieving optimal results through an iterative process that incorporates simulation, sensing, processing and evaluation. Important issues such as multimodal sensory component integration, region of interest extraction, target tracking, hyperspectral image analysis and target signature identification are discussed.

关键词： Hyperspectral imaging Panoramic vision bio-inspired sensing Target tracking

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SUSurE: Speeded Up Surround Extrema Feature Detector and Descriptor for Real-time Applications

SUSurE: Speeded Up Surround Extrema Feature Detector and Des...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Mosalam Ebrahimi Walterio W. Mayol-Cuevas Department of Computer Science University of Bristol Bristol United Kingdom

ISBN: (纸本)9781424439942

There has been significant research into the development of visual feature detectors and descriptors that are robust to a number of image deformations. Some of these methods have emphasized the need to improve on computational speed and compact representations so that they can enable a range of real-time applications with reduced computational requirements. In this paper we present modified detectors and descriptors based on the recently introduced CenSurE [l], and show experimental results that aim to highlight the computational savings that can be made with limited reduction in performance. The developed methods are based on exploiting the concept of sparse sampling which may be of interest to a range of other existing approaches.

关键词： computer vision Detectors Filters Robustness Information services Web sites Internet Application software Kernel Image databases

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Gibbs likelihoods for Bayesian tracking

Gibbs likelihoods for Bayesian tracking

引用

conference on computer vision and pattern recognition (cvpr)

作者： S. Roth L. Sigal M.J. Black Department of Computer Science Brown University Providence RI USA

Bayesian methods for visual tracking model the likelihood of image measurements conditioned on a tracking hypothesis. Image measurements may, for example, correspond to various filter responses at multiple scales and orientations. Most tracking approaches exploit ad hoc likelihood models while those that exploit more rigorous, learned, models often make unrealistic assumptions about the underlying probabilistic model. Such assumptions cause problems for Bayesian inference when an unsound likelihood is combined with an a priori probability distribution. Errors in modeling the likelihood can lead to brittle tracking results, particularly when using non-parametric inference techniques such as particle filtering. We show how assumptions of conditional independence of filter responses are violated in common tracking scenarios, lead to incorrect likelihood models, and cause problems for Bayesian inference. We address the problem of modeling more principled likelihoods using Gibbs learning. The learned models are compared with naive Bayes methods which assume conditional independence of the filter responses. We show how these Gibbs models can be used as an effective image likelihood, and demonstrate them in the context of particle filter-based human tracking.

关键词： Bayesian methods computer society computer vision pattern recognition

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conference on computer vision and pattern recognition (cvpr)

作者： Feng Kang Rong Jin R. Sukthankar Michigan State University USA Robotics Institute Carnegie Mellon University USA Intel Research Pittsburgh USA

Many computer vision applications, such as scene analysis and medical image interpretation, are ill-suited for traditional classification where each image can only be associated with a single class. This has stimulated recent work in multi-label learning where a given image can be tagged with multiple class labels. A serious problem with existing approaches is that they are unable to exploit correlations between class labels. This paper presents a novel framework for multi-label learning termed Correlated Label Propagation (CLP) that explicitly models interactions between labels in an efficient manner. As in standard label propagation, labels attached to training data points are propagated to test data points; however, unlike standard algorithms that treat each label independently, CLP simultaneously co-propagates multiple labels. Existing work eschews such an approach since naive algorithms for label co-propagation are intractable. We present an algorithm based on properties of submodular functions that efficiently finds an optimal solution. Our experiments demonstrate that CLP leads to significant gains in precision/recall against standard techniques on two real-world computer vision tasks involving several hundred labels.

关键词： Testing computer vision Application software Medical robotics Robot vision systems Image analysis Biomedical imaging Training data Frequency computer society

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Separating style and content on a nonlinear manifold

Separating style and content on a nonlinear manifold

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conference on computer vision and pattern recognition (cvpr)

作者： A. Elgammal Chan-Su Lee Department of Computer Science Rutgers University New Brunswick NJ USA

Bilinear and multi-linear models have been successful in decomposing static image ensembles into perceptually orthogonal sources of variations, e.g., separation of style and content. If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. The question that we address in this paper is how to separate style and content on manifolds representing dynamic objects. In this paper we learn a decomposable generative model that explicitly decomposes the intrinsic body configuration (content) as a function of time from the appearance (style) of the person performing the action as time-invariant parameter. The framework we present in this paper is based on decomposing the style parameters in the space of nonlinear functions which map between a learned unified nonlinear embedding of multiple content manifolds and the visual input space.

关键词： Legged locomotion Tensile stress Shape Humans Principal component analysis Head Image analysis computer science Biological system modeling Face recognition

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Detecting text in natural scenes with stroke width transform

Detecting text in natural scenes with stroke width transform

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conference on computer vision and pattern recognition (cvpr)

作者： Boris Epshtein Eyal Ofek Yonatan Wexler Microsoft Corporation USA

We present a novel image operator that seeks to find the value of stroke width for each image pixel, and demonstrate its use on the task of text detection in natural images. The suggested operator is local and data dependent, which makes it fast and robust enough to eliminate the need for multi-scale computation or scanning windows. Extensive testing shows that the suggested scheme outperforms the latest published algorithms. Its simplicity allows the algorithm to detect texts in many fonts and languages.

关键词： Layout Optical character recognition software Pixel computer vision Filter bank Engines Image segmentation Colored noise Geometry Robustness

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Exploratory active vision: theory

Exploratory active vision: theory

引用

conference on computer vision and pattern recognition (cvpr)

作者： J.-Y. Herve Y. Aloimonos Computer Vision Laboratory Center for Automation Research Department of Computer Science Institute for Advanced Computer Studies University of Maryland College Park MD USA

An active approach to the integration of shape from x modules-here shape from shading and shape from texture-is proposed. The question of what constitutes a good motion for the active observer is addressed. Generally, the role of the visual system is to provide depth information to an autonomous robot; a trajectory module will then interpret it to determine a motion for the robot, which in turn will affect the visual information received. It is suggested that the motion can also be chosen so as to improve the performance of the visual system.< >

关键词： Layout Motion analysis Active shape model Cameras computer vision Robots Visual system Laboratories Automation computer science

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Attention control for robot vision

Attention control for robot vision

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conference on computer vision and pattern recognition (cvpr)

作者： C.-F. Westin C.-J. Westelius H. Knutsson G. Granlund Computer Vision Laboratory Department of Electrical Engineering Linkoping University Linkoping Sweden Brigham and Womens Hospital Harvard Medical School Surgical Planning Laboratory Boston MA USA

Focus of attention mechanisms for robot vision are discussed. A new method for neglecting low level filter responses from already modelled structures is presented. The method is based on a filtering technique termed normalized convolution. In one experiment, the robot is continuously moving its arm in the scene while tracking other objects. It is shown how the arm can be made "invisible" so that only the moving object of interest is detected. This makes tracking of objects much simpler. In another experiment, the attention of the system is shifted between objects by simply cancelling the mask of the object to be attended to. With this strategy the low level processes do not need to know the difference between a new object entering the scene and a mask being cancelled, and thus a complex communication structure between high and low levels is avoided.

关键词： Robot control Robot vision systems Layout Laboratories Focusing Robot sensing systems Feature extraction Context modeling Computational modeling Filtering

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Flexible spatial models for grouping local image features

Flexible spatial models for grouping local image features

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conference on computer vision and pattern recognition (cvpr)

作者： G. Carneiro A.D. Jepson Department of Computer Science University of Toronto Toronto ONT Canada

A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping far long range motion problems.

关键词： Robustness Image databases Spatial databases Image recognition Object recognition Gabor filters Object detection computer science Image matching Shape

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Efficient search of faces from complex line drawings

Efficient search of faces from complex line drawings

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conference on computer vision and pattern recognition (cvpr)

作者： Jianzhuang Liu Xiaoou Tang Department of Information Engineering Chinese University of Hong Kong Hong Kong China

Single 2D line drawing is a straightforward method to illustrate 3D objects. The faces of an object depicted by a line drawing give very useful information for the reconstruction of its 3D geometry. Each of the two recently proposed methods for face identification from line drawings involves two combinatorial problems. When dealing with complex objects having more faces, the combinatorial explosion prevents these methods from practical uses. This paper proposes a new approach to tackling the face identification problem by a variable-length genetic algorithm (GA) with geometric constraints and a novel heuristic incorporated for local search. The hybrid GA solves the two combinatorial problems simultaneously. Experimental results show that our algorithm can find the faces of a line drawing having more than 30 faces much more efficiently.

关键词： Circuits Face detection Engineering drawings Explosions Genetic algorithms computer vision Information geometry Computational geometry Application software Design automation

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