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A Proposed Pipelined-Architecture for FPGA-Based Affine-Invariant Feature Detectors

A Proposed Pipelined-Architecture for FPGA-Based Affine-Inva...

引用

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

作者： C. Cabani W.J. MacLean Department of Electrical & Computer Engineering University of Toronto Toronto ONT Canada

This paper describes a hardware architecture for an FPGAbased implementation of affine-invariant image feature detectors, following the algorithm of Mikolajczyk & Schmid. The architecture mimics the structure of the algorithm by implementing a multi-scale Harris corner detector which feeds candidate points into an iterative procedure to determine the local affine shape of the feature’s neighbourhood (up to an undetermined rotation). Since the algorithm is iterative, and since we desire a high throughput rate, the iterations are "unrolled" into a sequence of identical computation blocks arranged in a pipeline architecture. The modularity of the resulting architecture allows for scaling the implementation to devices of different resource capacity, as well as partitioning the algorithm over several devices. The final implementation, when completed, will be part of a smart-camera system which outputs features at the same time as the associated images.

关键词： computer vision Iterative algorithms Detectors Partitioning algorithms computer architecture Hardware Feeds Shape Throughput Pipelines

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Geometric Hashing with Local Affine Frames

Geometric Hashing with Local Affine Frames

引用

conference on computer vision and pattern recognition (CVPR)

作者： O. Chum J. Matas Center for Machine Perception Czech Technical University Czech Republic CVSSP University of Surrey Guildford UK

We propose a novel representation of local image structure and a matching scheme that are insensitive to a wide range of appearance changes. The representation is a collection of local affine frames that are constructed on outer boundaries of maximally stable extremal regions (MSERS) in an affine-covariant way. Each local affine frame is described by a relative location of other local affine frames in its neighborhood. The image is thus represented by quantities that depend only on the location of the boundaries of MSERs. Inter-image correspondences between local affine frames are formed in constant time by geometric hashing. Direct detection of local afine frames removes the requirement of a point-based hashing to establish reference frames in a combinatorial way, which has in the case of affine transform complexily that is cubic in the number of points. Local affine frames, which are also the quantities represented in the hash table, occupy a 6 0 space and hence data collisions are less likely compared with 2 0 point hashing. Experimentally, the robustness of the method and its insensitiviq to photometric changes is demonstrated on images from different spectral bands of satellite sensor; on images of a transparent object and on images of an object taken during day and night.

关键词： Image recognition Photometry Image retrieval Object recognition computer vision Information retrieval Robustness Satellites Application software Real time systems

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Tracking With Sobolev Active Contours

Tracking With Sobolev Active Contours

引用

conference on computer vision and pattern recognition (CVPR)

作者： G. Sundaramoorthi J.D. Jackson A. Yezzi A.C. Mennucci School of Electrical and Computer Engineering Georgia Institute of Technology USA Scuola Normale Superiore Italy

Recently proposed Sobolev active contours introduced a new paradigm for minimizing energies defined on curves by changing the traditional cost of perturbing a curve and thereby redefining their gradients. Sobolev active contours evolve more globally and are less attracted to certain intermediate local minima than traditional active contours. In this paper we analyze Sobolev active contours in the Fourier domain in order to understand their evolution across different scales. This analysis shows an important and useful behavior of Sobolev contours, namely, that they move successively from coarse to increasingly finer scale motions in a continuous manner. Along with other properties, the previous observation reveals that Sobolev active contours are ideally suited for tracking problems that use active contours. Our purpose in this work is to show how a variety of active contour based tracking methods can be significantly improved merely by evolving the active contours according to the Sobolev method.

关键词： Active contours Shape Costs Motion analysis Video sequences Image edge detection Robustness Deformable models

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Shape Guided Object Segmentation

Shape Guided Object Segmentation

引用

conference on computer vision and pattern recognition (CVPR)

作者： E. Borenstein J. Malik Division of Applied Mathematics Brown University USA Department of Electrical Engineering and Computer Science University of California Berkeley USA

We construct a Bayesian model that integrates topdown with bottom-up criteria, capitalizing on their relative merits to obtain figure-ground segmentation that is shape-specific and texture invariant. A hierarchy of bottom-up segments in multiple scales is used to construct a prior on all possible figure-ground segmentations of the image. This prior is used by our top-down part to query and detect object parts in the image using stored shape templates. The detected parts are integrated to produce a global approximation for the object’s shape, which is then used by an inference algorithm to produce the final segmentation. Experiments with a large sample of horse and runner images demonstrate strong figure-ground segmentation despite high object and background variability. The segmentations are robust to changes in appearance since the matching component depends on shape criteria alone. The model may be useful for additional visual tasks requiring labeling, such as the segmentation of multiple scene objects.

关键词： Shape Object segmentation Image segmentation Bayesian methods Object detection Approximation algorithms Inference algorithms Horses Robustness Labeling

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Reconstructing Occluded Surfaces Using Synthetic Apertures: Stereo, Focus and Robust Measures

Reconstructing Occluded Surfaces Using Synthetic Apertures: ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： V. Vaish M. Levoy R. Szeliski C.L. Zitnick Sing Bing Kang Computer Science Department University of Stanford CA USA Microsoft Research Limited Redmond WA USA

Most algorithms for 3D reconstruction from images use cost functions based on SSD, which assume that the surfaces being reconstructed are visible to all cameras. This makes it difficult to reconstruct objects which are partially occluded. Recently, researchers working with large camera arrays have shown it is possible to "see through" occlusions using a technique called synthetic aperture focusing. This suggests that we can design alternative cost functions that are robust to occlusions using synthetic apertures. Our paper explores this design space. We compare classical shape from stereo with shape from synthetic aperture focus. We also describe two variants of multi-view stereo based on color medians and entropy that increase robustness to occlusions. We present an experimental comparison of these cost functions on complex light fields, measuring their accuracy against the amount of occlusion.

关键词： Surface reconstruction Robustness Image reconstruction Focusing Cost function Cameras Space exploration Shape Stereo image processing Entropy

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Discriminative Patch Selection using Combinatorial and Statistical Models for Patch-Based Object recognition

Discriminative Patch Selection using Combinatorial and Stati...

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

作者： A. Vashist Zhipeng Zhao A. Elgammal I. Muchnik C. Kulikowski Department of Computer Science State University of New Jersey Rutgers Piscataway NJ USA

In an object recognition task where an image is represented as a constellation of image patches, often many patches correspond to the cluttered background. If such patches are used for object class recognition, they will adversely affect the recognition rate. In this paper, we present a two stage method for selecting image patches which characterize the target object class and are capable of discriminating between the positive images containing the target objects and the complementary negative images. The first stage selection is done using a novel combinatorial optimization formulation on a weighted multipartite graph representing similarities between images patches across different instances of the target object. The following stage is a statistical method for selecting those images patches from the positive images which, when used individually, have the power of discriminating between the positive and negative images in the evaluation data. The individual methods have a performance competitive with the state of the art methods on a popular benchmark data set and their sequential combination consistently outperforms the individual methods and most of the other known methods while approaching the best known results.

关键词： Object recognition Solid modeling Feature extraction Object detection computer vision Image recognition Face detection pattern recognition computer science Statistical analysis

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Robust AAM Fitting by Fusion of Images and Disparity Data

Robust AAM Fitting by Fusion of Images and Disparity Data

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conference on computer vision and pattern recognition (CVPR)

作者： J. Liebelt Jing Xiao Jie Yang Interactive Systems Laboratories Universitaet Karlsruhe Karlsruhe Germany Epson Palo Alto Laboratory Palo Alto CA USA School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Active Appearance Models (AAMs) have been popularly used to represent the appearance and shape variations of human faces. Fitting an AAM to images recovers the face pose as well as its deformable shape and varying appearance. Successful fitting requires that the AAM is sufficiently generic such that it covers all possible facial appearances and shapes in the images. Such a generic AAM is often difficult to be obtained in practice, especially when the image quality is low or when occlusion occurs. To achieve robust AAM fitting under such circumstances, this paper proposes to incorporate the disparity data obtained from a stereo camera with the image fitting process. We develop an iterative multi-level algorithm that combines efficient AAM fitting to 2D images and robust 3D shape alignment to disparity data. Experiments on tracking faces in low-resolution images captured from meeting scenarios show that the proposed method achieves better performance than the original 2D AAM fitting algorithm. We also demonstrate an application of the proposed method to a facial expression recognition task.

关键词： Robustness Active appearance model Shape Humans Face Iterative algorithms pattern recognition Interactive systems Laboratories Drives

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A Robust Video Object Tracking by Using Active Contours

A Robust Video Object Tracking by Using Active Contours

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

作者： M.S. Allili D. Ziou Département d'informatique Faculté des Sciences Université de Sherbrook Sherbrooke QUE Canada

In this paper, we propose a novel object tracking algorithm in video sequences. The formulation of our tracking model is based on variational calculus, where region and boundary information cooperate for object boundary localization by using active contours. In the approach, only the segmentation of the objects in the first frame is required for initialization. The evolution of the object contours on a current frame aims to find the boundary of the objects by minimizing the Kullback-Leibler distance of the region features distribution in the vicinity of the contour to the objects versus the background respectively. We show the effectiveness of the approach on examples of object tracking performed on real video sequences.

关键词： Robustness Active contours Video sequences Level set computer vision Object detection Tracking Calculus Application software Video surveillance

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Robust Visual Tracking Using Case-Based Reasoning with Confidence

Robust Visual Tracking Using Case-Based Reasoning with Confi...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zhiwei Zhu Wenhui Liao Qiang Ji Department of ECSE Rensselaer Polytechnic Institute Troy NY USA Sarnoff Corporation USA

The paper describes a simple but robust framework for visual object tracking in a video sequence. Compared with the existing tracking techniques, our proposed tracking technique has two significant contributions. First, a Case- Based Reasoning (CBR) paradigm is introduced to track the non-rigid object robustly under significant appearance changes without drifting away. Second, it can provide an accurate confidence measurement for each tracked object so that the tracking failures can be identified successfully. Specifically, under this framework, the appearance changes of the object being tracked can be adapted dynamically during tracking via an adaption mechanism of CBR. Hence, an accurate 2D tracking model can be maintained online for each image frame during tracking. Therefore, the proposed tracking technique possesses a self-recovery capability so that the object can be tracked robustly under significant appearance changes without error accumulation. Application was focused on the development of a real-time face tracking system. Via the proposed framework, the built real-time face tracker can track the human face robustly at 26 frames per second under various face orientations, significant facial expression and external illumination changes.

关键词： Robustness Video sequences Face Cameras Geometry Real time systems Humans Lighting computer society computer vision

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Boundary Extraction in Natural Images Using Ultrametric Contour Maps

Boundary Extraction in Natural Images Using Ultrametric Cont...

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

作者： P. Arbelaez CEREMADE UMR CNRS 7534 Université Paris Dauphine Paris France

This paper presents a low-level system for boundary extraction and segmentation of natural images and the evaluation of its performance. We study the problem in the framework of hierarchical classification, where the geometric structure of an image can be represented by an ultrametric contour map, the soft boundary image associated to a family of nested segmentations. We define generic ultrametric distances by integrating local contour cues along the regions boundaries and combining this information with region attributes. Then, we evaluate quantitatively our results with respect to ground-truth segmentation data, proving that our system outperforms significantly two widely used hierarchical segmentation techniques, as well as the state of the art in local edge detection.

关键词： Image segmentation Humans Image edge detection computer vision Object recognition Layout Classification tree analysis Visual perception Data mining Geometry

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