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On-Vehicle and Aerial Texture Analysis for vision-Based Desert Road Following

On-Vehicle and Aerial Texture Analysis for Vision-Based Dese...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： C. Rasmussen T. Korah Department of Computer & Information Sciences University of Delaware USA

We present two components of a vision-based approach to autonomous driving on and near rural and desert roads. The first component comprises fast processing of live vehicle camera video to robustly extract linear direction and midline estimates of marginal roads. The second uses satellite imagery immediately surrounding the vehicle’s GPS position to trace the road ahead for curve and corner anticipation, and to inform the vehicle planner of a nearby road when traveling cross-conalysis: on-board, they are employed to ?nd ruts and trauntry. The algorithms are built upon Gabor wavelet filters for texture acks from which the road vanishing point can be inferred via Houghstyle voting, and aerially, they localize the edges of low-contrast road contours. Mechanisms for both modules to determine whether the vehicle is currently on- or off-road are also explained. Our system’s efficacy is illustrated for several difficult datasets, including a log from one vehicle’s run during the 2004 DARPA Grand Challenge.

关键词： Road vehicles computer vision Global Positioning System Voting Information analysis Remotely operated vehicles Cameras Robustness Data mining Satellites

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Face Identification by a Cascade of Rejection Classifiers

Face Identification by a Cascade of Rejection Classifiers

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Quan Yuan A. Thangali S. Sclaroff Computer Science Boston University Boston MA USA

Nearest neighbor search is commonly employed in face recognition but it does not scale well to large dataset sizes. A strategy to combine rejection classifiers into a cascade for face identification is proposed in this paper. A rejection classifier for a pair of classes is defined to reject at least one of the classes with high confidence. These rejection classifiers are able to share discriminants in feature space and at the same time have high confidence in the rejection decision. In the face identification problem, it is possible that a pair of known individual faces are very dissimilar. It is very unlikely that both of them are close to an unknown face in the feature space. Hence, only one of them needs to be considered. Using a cascade structure of rejection classifiers, the scope of nearest neighbor search can be reduced significantly. Experiments on Face recognition Grand Challenge (FRGC) version 1 data demonstrate that the proposed method achieves significant speed up and an accuracy comparable with the brute force Nearest Neighbor method. In addition, a graph cut based clustering technique is employed to demonstrate that the pairwise separability of these rejection classifiers is capable of semantic grouping.

关键词： Nearest neighbor searches Face recognition Watches Testing computer science Surveillance Access control Robustness Lighting Hair

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Improving the Speed of Virtual Rear Projection: A GPU-Centric Architecture

Improving the Speed of Virtual Rear Projection: A GPU-Centri...

引用

ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： M. Flagg J. Summet J.M. Rehg GVU Center College of Computing Georgia Institute of Technology Atlanta GA USA

Projection is the only viable way to produce very large displays. Rear projection of large-scale upright displays is often preferred over front projection because of the lack of shadows that occlude the projected image. However, rear projection is not always a feasible option for space and cost reasons. Recent research suggests that many of the desirable features of rear projection, in particular lack of shadows, can be reproduced using active virtual rear projection (VRP). We present a new approach to shadow detection that addresses limitations with previous work. Furthermore, we demonstrate how to exploit the image processing capabilities of a GPU to shift the main performance bottleneck from image processing to camera capture and projector display rates. The improvements presented in this paper enable a speed increase in image processing from 15Hz to 110Hz in our new active VRP prototype.

关键词： computer displays Space technology Cameras Costs Image processing Foot computer architecture Educational institutions Large-scale systems Prototypes

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A Measure for Objective Evaluation of Image Segmentation Algorithms

A Measure for Objective Evaluation of Image Segmentation Alg...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： R. Unnikrishnan C. Pantofaru M. Hebert The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Despite significant advances in image segmentation techniques, evaluation of these techniques thus far has been largely subjective. Typically, the effectiveness of a new algorithm is demonstrated only by the presentation of a few segmented images and is otherwise left to subjective evaluation by the reader. Little effort has been spent on the design of perceptually correct measures to compare an automatic segmentation of an image to a set of hand-segmented examples of the same image. This paper demonstrates how a modification of the Rand index, the Normalized Probabilistic Rand (NPR) index, meets the requirements of largescale performance evaluation of image segmentation. We show that the measure has a clear probabilistic interpretation as the maximum likelihood estimator of an underlying Gibbs model, can be correctly normalized to account for the inherent similarity in a set of ground truth images, and can be computed efficiently for large datasets. Results are presented on images from the publicly available Berkeley Segmentation dataset.

关键词： Image segmentation Humans Robots Maximum likelihood detection Maximum likelihood estimation Machine vision Target tracking Shape Clustering algorithms Image databases

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Semi-supervised adapted HMMs for unusual event detection

Semi-supervised adapted HMMs for unusual event detection

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conference on computer vision and pattern recognition (cvpr)

作者： Dong Zhang D. Gatica-Perez S. Bengio I. McCowan Swiss Federal Institute of Technology Lausanne Switzerland IDIAP Research Institute Martigny Switzerland

We address the problem of temporal unusual event detection. Unusual events are characterized by a number of features (rarity, unexpectedness, and relevance) that limit the application of traditional supervised model-based approaches. We propose a semi-supervised adapted hidden Markov model (HMM) framework, in which usual event models are first learned from a large amount of (commonly available) training data, while unusual event models are learned by Bayesian adaptation in an unsupervised manner. The proposed framework has an iterative structure, which adapts a new unusual event model at each iteration. We show that such a framework can address problems due to the scarcity of training data and the difficulty in pre-defining unusual events. Experiments on audio, visual, and audiovisual data streams illustrate its effectiveness, compared with both supervised and unsupervised baseline methods.

关键词： Hidden Markov models Event detection Training data Bayesian methods Streaming media computer vision Surveillance Data mining Information management Ambient intelligence

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Task-Driven Learning of Spatial Combinations of Visual Features

Task-Driven Learning of Spatial Combinations of Visual Featu...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： S. Jodogne F. Scalzo J.H. Piater Institut Montefiore (B28) Universitè de Limoges Liege Belgium

Solving a visual, interactive task can often be thought of as building a mapping from visual stimuli to appropriate actions. Clearly, the extracted visual characteristics that index into the repertoire of actions must be sufficiently rich to distinguish situations that demand distinct actions. Spatial combinations of local features permit, in principle, the construction of features at various levels of discriminative power. We present an algorithm for selecting relevant spatial combinations of visual features by exercising a given task in a closed-loop learning process based on Reinforcement Learning. The algorithm operates by progressively splitting the perceptual space into distinct regions. Whenever the agent detects perceptual aliasing of distinct world states, it constructs a spatial combination of visual features that disambiguates the aliased states. We demonstrate the efficacy of our algorithm on a version of the classical "Car on the Hill" control problem where position and velocity are presented to the agent visually, in a way that the task is unsolvable using individual point features.

关键词： Partitioning algorithms Buildings Velocity control Humans Data mining Supervised learning Unsupervised learning Focusing Control systems computer society

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Face modeling and analysis in Stony Brook University

Face modeling and analysis in Stony Brook University

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conference on computer vision and pattern recognition (cvpr)

作者： D. Samaras Y. Wang L. Zhang S. Wang M. Gupta Computer Science Stony Brook University NY USA

In this paper, we present our latest work on facial expression analysis, synthesis and face recognition. The advent of new technologies that allow the capture of massive amounts of high resolution, high frame rate face data, leads us to propose data-driven face models that accurately describe the appearance of faces under unknown pose and illumination conditions as well as to track subtle geometry changes that occur during expressions. In this paper, we also demonstrate our results for expression transfer among different subjects. We reduce the dimensionality of our data onto a lower dimensional space manifold and then decompose it into style and content parameters. This allows us to transfer subtle expression information (in the form of a style vector) between individuals to synthesize new expressions, as well as smoothly morph geometry and motion. Finally, we demonstrate the accuracy of our face modeling methods through an integrated example of image-driven re-targeting and relighting of facial expressions, where transfer of expression and illumination information between different individuals is possible.

关键词： Lighting Face recognition Geometry computer graphics computer science Solid modeling Clouds Tracking Topology Head

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Redundant Class-Dependence Feature Analysis Based on Correlation Filters Using FRGC2.0 Data

Redundant Class-Dependence Feature Analysis Based on Correla...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Chunyan Xie M. Savvides B.V.K.V. Kumar Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA

In this paper we propose a new method called redundant class-dependence feature analysis (CFA) based on the advanced correlation filters to perform robust face recognition on the Face recognition Grand Challenge (FRGC) data set. The FRGC contains a large corpus of data and a set of challenge problems. The data is divided into training and validation partitions, with the standard still-image training partition consisting of 12,800 images, and the validation partition consisting of 16,028 controlled still images, 8,014 uncontrolled stills, and 4,007 3D scans. We have tested the proposed CFA method and compared it with the PCA and LDA methods in a recognition scenario on the FRGC2.0 data. The preliminary results show that the CFA outperforms the other two compared methods in our experiments. We also show the improved performance of the CFA method on the FRGC experiments #1 and #4.

关键词： Face recognition Filter bank Robustness Principal component analysis Linear discriminant analysis Lighting Data mining Performance analysis Testing Humans

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Shape matching and object recognition using low distortion correspondences

Shape matching and object recognition using low distortion c...

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conference on computer vision and pattern recognition (cvpr)

作者： A.C. Berg T.L. Berg J. Malik Department of Electrical Engineering and Computer Science University of California Berkeley USA

We approach recognition in the framework of deformable shape matching, relying on a new algorithm for finding correspondences between feature points. This algorithm sets up correspondence as an integer quadratic programming problem, where the cost function has terms based on similarity of corresponding geometric blur point descriptors as well as the geometric distortion between pairs of corresponding feature points. The algorithm handles outliers, and thus enables matching of exemplars to query images in the presence of occlusion and clutter. Given the correspondences, we estimate an aligning transform, typically a regularized thin plate spline, resulting in a dense correspondence between the two shapes. Object recognition is then handled in a nearest neighbor framework where the distance between exemplar and query is the matching cost between corresponding points. We show results on two datasets. One is the Caltech 101 dataset (Fei-Fei, Fergus and Perona), an extremely challenging dataset with large intraclass variation. Our approach yields a 48% correct classification rate, compared to Fei-Fei et al 's 16%. We also show results for localizing frontal and profile faces that are comparable to special purpose approaches tuned to faces.

关键词： Shape Object recognition computer vision Algorithms Deformable models Quadratic programming Cost function Spline Nearest neighbor searches Face detection

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Probabilistic modeling based vessel enhancement in thoracic CT scans

Probabilistic modeling based vessel enhancement in thoracic ...

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conference on computer vision and pattern recognition (cvpr)

作者： G. Agam C. Wu Department of Computer Science Illinois Institute of Technology Chicago IL USA

Vessel enhancement in volumetric data is a necessary prerequisite in various medical imaging applications with particular importance for automated nodule detection. Ideally, vessel enhancement filters should enhance vessels and vessel junctions while suppressing nodules and other non-vessel elements. A distinction between vessels and nodules is normally obtained through eigenvalue analysis of the curvature tensor which is a second order differential quantity and so is sensitive to noise. Furthermore, by relying on principal curvatures alone, existing vessel enhancement filters are incapable of distinguishing between nodules and vessel junctions. In this paper we propose probabilistic vessel models from which novel vessel enhancement filters capable of enhancing junctions while suppressing nodules are derived. The proposed filters are based on eigenvalue analysis of the structure tensor which is a first order differential quantity and so are less sensitive to noise. The proposed filters are evaluated and compared to known techniques based on actual clinical data.

关键词： Computed tomography Nonlinear filters Biomedical imaging Eigenvalues and eigenfunctions Tensile stress Blood vessels Lungs Image segmentation computer science Application software

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