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Fitting Superellipses to Incomplete Contours

Fitting Superellipses to Incomplete Contours

引用

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

作者： M. Osian T. Tuytelaars L. Van Gool ESAT/PSI Katholieke Universiteit Leuven Belgium K. U. Leuven and ETH Zurich Belgium

Affine invariant regions have proved a powerful feature for object recognition and categorization. These features heavily rely on object textures rather than shapes, however. Typically, their shapes have been fixed to ellipses or parallelograms. The paper proposes a novel affine invariant region type, that is built up from a combination of fitted superellipses. These novel features have the advantage of offering a much wider range of shapes through the addition of a very limited number of shape parameters, with the traditional ellipses and parallelograms as subsets. The paper offers a solution for the robust fitting of superellipses to partial contours, which is a crucial step towards the implementation of the novel features.

关键词： Shape computer vision Object recognition Robustness Radio access networks Equations computer Society pattern recognition conferences

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Threshold Selection as a Function of Region Count Stability

Threshold Selection as a Function of Region Count Stability

引用

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

作者： Li Yu A. Hoover Electrical & Computer Engineering Department Clemson University Clemson SC USA

In this paper we present a novel method for threshold selection. The idea is based upon quantifying the stability of the number of regions segmented as the threshold is varied. We capture this idea using a scale-space formulation, and detect "stable" segmentations as local minima in the scale-space. This work was originally motivated by the problem of detecting some types of common lesions in retinal images (many lesions appear as abnormally bright areas), on which we show some results. We also compare our method against an approach based on saliency.

关键词： Stability Lesions Image segmentation Retina Shape Humans Cotton Wool Merging Amorphous materials

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Representation and recognition of Events in Surveillance Video Using Petri Nets

Representation and Recognition of Events in Surveillance Vid...

引用

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

作者： N. Ghanem D. DeMenthon D. Doermann L. Davis Department of Computer Science University of Maryland College Park MD USA

Detection of events is an essential task in surveillance applications. This task requires finding a general event representation method and developing efficient recognition algorithms dealing with this representation. In this paper, we describe an interactive system for querying surveillance video about events. The queries may not be known in advance and have to be composed from primitive events and previously defined queries. We propose using Petri nets as both representation and recognition methods. The Petri net representation for users' queries is derived automatically from simpler event nets. recognition is then performed by tokens moving through the Petri nets.

关键词： Surveillance Petri nets Event detection Interactive systems Stochastic processes computer vision Ontologies Mathematical model computer science Educational institutions

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Efficient Computation of Closed Contours using Modified Baum-Welch Updating

Efficient Computation of Closed Contours using Modified Baum...

引用

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

作者： L.A. Johnston J.H. Elder Centre for Vision Research York University Toronto Canada

We address the problem of computing closed contours delineating the boundaries of objects of interest from image tangent maps. The Bayesian formulation of this problem incorporates the notion of foreground and background models in inferring the optimal image organization. Due to vital non-local constraints, the optimal MAP solution is intractable. We present a novel efficient search algorithm for the construction of highly probable closed contours, based on the Baum-Welch algorithm for updating hidden Markov model parameters.

关键词： Bayesian methods Hidden Markov models Clustering algorithms Heuristic algorithms computer vision Polynomials Costs Dynamic programming Jacobian matrices Greedy algorithms

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Constraints for Heterogeneous Sensor Auto-Calibration

Constraints for Heterogeneous Sensor Auto-Calibration

引用

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

作者： Qilong Zhang R. Pless Department of Computer Science and Engineering Washington University of Saint Louis Saint Louis MO USA

This paper describes a framework for calibrating motion sensitive sensors attached to an autonomous vehicle. Based on camera auto-calibration techniques, we derive constraints relating sensor measurements to the relative position and orientation of different sensors. For the case of a camera and laser range finder, we present an auto-calibration algorithm for discrete motions. Auto-calibration tools are vital for real world use of vision algorithms, and the framework presented here is important to merge image sensors with GPS, inertial, infrared and ultra-sonic sensors.

关键词： Cameras Sensor systems Calibration Magnetic sensors Mobile robots Infrared image sensors Robot sensing systems Robot kinematics Robot vision systems Optical sensors

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Learning with the Optimized Data-Dependent Kernel

Learning with the Optimized Data-Dependent Kernel

引用

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

作者： Huilin Xiong M.N.S. Swamy M.O. Ahmad Department of Electrical and Computer Engineering Concordia University Montreal QUE Canada

This paper presents a method of optimizing a data-dependent kernel by maximizing a measure of class separability in the empirical feature space, an Euclidean space in which the training data are embedded in such a way that the geometrical structure of the data in the feature space is preserved. An effective algorithm is derived to perform the optimization. The optimized kernel show more adaptive to the data and leads to a substantial improvement in the performance of the kernel machines. Simulations are carried out to demonstrate this improvement.

关键词： Kernel Training data Machine learning Matrix decomposition Symmetric matrices Machine learning algorithms Support vector machines Spatial resolution Embedded computing Data engineering

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Fusion of Visual and Ultrasonic Information for Environmental Modelling

Fusion of Visual and Ultrasonic Information for Environmenta...

引用

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

作者： C. Town Computer Laboratory University of Cambridge Cambridge UK

Information obtained from calibrated cameras by means of computer vision is integrated with location events from an ultrasonic tracking system deployed in an indoor office. This results in improved estimates of state and location which are used to augment the environmental model maintained by a sentient computing system. Fusion of the different sources of information takes place at a high level using Bayesian networks to model dependencies and reliabilities of the multi-modal variables. Context is represented using a world model of both the static and dynamic environment. The world model serves both as an ontology of prior information for multi-modal integration and as a source of context for applications.

关键词： computer vision Context modeling Pulse measurements Cameras Maintenance Bayesian methods Ontologies Context-aware services Sensor systems Machine vision

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Silhouette Lookup for Automatic Pose Tracking

Silhouette Lookup for Automatic Pose Tracking

引用

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

作者： N.R. Howe Computer Science Smith College MA USA

computers should be able to detect and track the articulated 3-D pose of a human being moving through a video sequence. Current tracking methods often prove slow and unreliable, and many must be initialized by a human operator before they can track a sequence. This paper introduces a simple yet effective algorithm for tracking articulated pose, based upon looking up observed silhouettes in a collection of known poses. The new algorithm runs quickly, can initialize itself without human intervention, and can automatically recover from critical tracking errors made while tracking previous frames in a video sequence.

关键词： Humans Cameras Video sequences Application software Particle tracking Smoothing methods Data mining Infrared heating computer vision computer science

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From Stills to Video: Face recognition Using a Probabilistic Approach

From Stills to Video: Face Recognition Using a Probabilistic...

引用

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

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

While it is generally possible to do recognition from video sequences, the training process is usually done over static images. This is due to the fact that, in many applications (e.g., homeland security), one does not have large video sequences which can be used for training. For example, law enforcement agencies generally have a frontal and a profile view of wanted individuals, but do not usually keep video sequences in file. Nonetheless, in these applications, it is still possible to analyze the information of video sequences for subsequent recognition tasks. This paper presents a probabilistic algorithm that learns from small sets of static images and then recognizes faces from video sequences. The proposed algorithm is robust to partial occlusions, different orientations and expression changes and does not require of precise face localizations. Our preliminary results with a small database show that the proposed method is more robust to such changes than static-to-static recognition of faces.

关键词： Face recognition Video sequences Image recognition Robustness Cameras pattern recognition Law enforcement Image databases Face detection computer vision

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Moving Humans Detection Based on Multi-Modal Sensor Fusion

Moving Humans Detection Based on Multi-Modal Sensor Fusion

引用

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

作者： B. Bhanu Xiaotao Zou Center for Research in Intelligent Systems University of California Riverside CA USA

Moving object detection plays an important role in automated surveillance systems. However, it is challenging to detect moving objects robustly in a cluttered environment. In this paper, we propose an approach for detecting humans using multi-modal measurements. The approach is based on using Time-Delay Neural Network (TDNN) to fuse the audio and video data at the feature level for detecting the walker with multiple persons in the scene. The main contribution of this paper is the introduction of Time-Delay Neural Network in learning the relation between visual motion and step sounds of the walking person. Experimental results are presented.

关键词： Humans Multimodal sensors Object detection Neural networks Surveillance Robustness Anthropometry Fuses computer vision Layout

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