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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013"

共 654 条记录，以下是11-20 订阅

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Improving the Visual Comprehension of Point Sets

Improving the Visual Comprehension of Point Sets

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Katz, Sagi Tal, Ayellet Technion Israel Inst Technol IL-32000 Haifa Israel

ISBN: (纸本)9780769549897

Point sets are the standard output of many 3D scanning systems and depth cameras. Presenting the set of points as is, might "hide" the prominent features of the object from which the points are sampled. Our goal is to reduce the number of points in a point set, for improving the visual comprehension from a given viewpoint. this is done by controlling the density of the reduced point set, so as to create bright regions (low density) and dark regions (high density), producing an effect of shading. this data reduction is achieved by leveraging a limitation of a solution to the classical problem of determining visibility from a viewpoint. In addition, we introduce a new dual problem, for determining visibility of a point from infinity, and show how a limitation of its solution can be leveraged in a similar way.

关键词： computer graphics data reduction

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Optimized Pedestrian Detection for Multiple and Occluded People

Optimized Pedestrian Detection for Multiple and Occluded Peo...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Rujikietgumjorn, Sitapa Collins, Robert T. Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9780769549897

We present a quadratic unconstrained binary optimization (QUBO) framework for reasoning about multiple object detections with spatial overlaps. the method maximizes an objective function composed of unary detection confidence scores and pairwise overlap constraints to determine which overlapping detections should be suppressed, and which should be kept. the framework is flexible enough to handle the problem of detecting objects as a shape covering of a foreground mask, and to handle the problem of filtering confidence weighted detections produced by a traditional sliding window object detector. In our experiments, we show that our method outperforms two existing state-of-the-art pedestrian detectors.

关键词： filtering theory object detection optimisation pedestrians shape recognition

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Joint Detection, Tracking and Mapping by Semantic Bundle Adjustment

Joint Detection, Tracking and Mapping by Semantic Bundle Adj...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Fioraio, Nicola Di Stefano, Luigi Univ Bologna Dept Comp Sci & Engn CVLab I-40135 Bologna Italy

ISBN: (纸本)9780769549897

In this paper we propose a novel Semantic Bundle Adjustment framework whereby known rigid stationary objects are detected while tracking the camera and mapping the environment. the system builds on established tracking and mapping techniques to exploit incremental 3D reconstruction in order to validate hypotheses on the presence and pose of sought objects. then, detected objects are explicitly taken into account for a global semantic optimization of both camera and object poses. thus, unlike all systems proposed so far, our approach allows for solving jointly the detection and SLAM problems, so as to achieve object detection together with improved SLAM accuracy.

关键词： SLAM (robots) computer graphics image reconstruction object detection

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Detecting and Naming Actors in Movies using Generative Appearance Models

Detecting and Naming Actors in Movies using Generative Appea...

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Gandhi, Vineet Ronfard, Remi INRIA LJK Grenoble France

ISBN: (纸本)9780769549897

We introduce a generative model for learning person and costume specific detectors from labeled examples. We demonstrate the model on the task of localizing and naming actors in long video sequences. More specifically, the actor's head and shoulders are each represented as a constellation of optional color regions. Detection can proceed despite changes in view-point and partial occlusions. We explain how to learn the models from a small number of labeled keyframes or video tracks, and how to detect novel appearances of the actors in a maximum likelihood framework. We present results on a challenging movie example, with 81% recall in actor detection (coverage) and 89% precision in actor identification (naming).

关键词： computer graphics image colour analysis image sequences maximum likelihood detection

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A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzles

A Genetic Algorithm-Based Solver for Very Large Jigsaw Puzzl...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Sholomon, Dror David, Omid Netanyahu, Nathan S. Bar Ilan Univ Dept Comp Sci IL-52900 Ramat Gan Israel

ISBN: (纸本)9780769549897

In this paper we propose the first effective automated, genetic algorithm (GA)-based jigsaw puzzle solver. We introduce a novel procedure of merging two "parent" solutions to an improved "child" solution by detecting, extracting, and combining correctly assembled puzzle segments. the solver proposed exhibits state-of-the-art performance solving previously attempted puzzles faster and far more accurately, and also puzzles of size never before attempted. Other contributions include the creation of a benchmark of large images, previously unavailable. We share the data sets and all of our results for future testing and comparative evaluation of jigsaw puzzle solvers.

关键词： benchmark testing computer games feature extraction genetic algorithms performance evaluation

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GRASP Recurring patterns from a Single View

<i>GRASP</i> Recurring Patterns from a Single View

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jingchen Liu, Yanxi Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9780769549897

We propose a novel unsupervised method for discovering recurring patterns from a single view. A key contribution of our approach is the formulation and validation of a joint assignment optimization problem where multiple visual words and object instances of a potential recurring pattern are considered simultaneously. the optimization is achieved by a greedy randomized adaptive search procedure (GRASP) with moves specifically designed for fast convergence. We have quantified systematically the performance of our approach under stressed conditions of the input (missing features, geometric distortions). We demonstrate that our proposed algorithm outperforms state of the art methods for recurring pattern discovery on a diverse set of 400+ real world and synthesized test images.

关键词： optimisation pattern recognition search problems

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Better exploiting motion for better action recognition

Better exploiting motion for better action recognition

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Jain, Mihir Jegou, Herve Bouthemy, Patrick INRIA Rennes France

ISBN: (纸本)9780769549897

Several recent works on action recognition have attested the importance of explicitly integrating motion characteristics in the video description. this paper establishes that adequately decomposing visual motion into dominant and residual motions, both in the extraction of the space-time trajectories and for the computation of descriptors, significantly improves action recognition algorithms. then, we design a new motion descriptor, the DCS descriptor, based on differential motion scalar quantities, divergence, curl and shear features. It captures additional information on the local motion patterns enhancing results. Finally, applying the recent VLAD coding technique proposed in image retrieval provides a substantial improvement for action recognition. Our three contributions are complementary and lead to outperform all reported results by a significant margin on three challenging datasets, namely Hollywood 2, HMDB51 and Olympic Sports.

关键词： feature extraction image enhancement image motion analysis image recognition video coding video retrieval

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SWIGS: A Swift Guided Sampling Method

SWIGS: A Swift Guided Sampling Method

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Fragoso, Victor Turk, Matthew Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9780769549897

We present SWIGS, a Swift and efficient Guided Sampling method for robust model estimation from image feature correspondences. Our method leverages the accuracy of our new confidence measure (MR-Rayleigh), which assigns a correctness-confidence to a putative correspondence in an online fashion. MR-Rayleigh is inspired by Meta-recognition (MR), an algorithm that aims to predict when a classifier's outcome is correct. We demonstrate that by using a Rayleigh distribution, the prediction accuracy of MR can be improved considerably. Our experiments show that MR-Rayleigh tends to predict better than the often-used Lowe's ratio, Brown's ratio, and the standard MR under a range of imaging conditions. Furthermore, our homography estimation experiment demonstrates that SWIGS performs similarly or better than other guided sampling methods while requiring fewer iterations, leading to fast and accurate model estimates.

关键词： computer vision feature extraction image recognition

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A Max-Margin Riffled Independence Model for Image Tag Ranking

A Max-Margin Riffled Independence Model for Image Tag Rankin...

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26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Lan, Tian Mori, Greg Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780769549897

We propose Max-Margin Riffled Independence Model (MMRIM), a new method for image tag ranking modeling the structured preferences among tags. the goal is to predict a ranked tag list for a given image, where tags are ordered by their importance or relevance to the image content. Our model integrates the max-margin formalism with riffled independence factorizations proposed in [10], which naturally allows for structured learning and efficient ranking. Experimental results on the SUN Attribute and LabelMe datasets demonstrate the superior performance of the proposed model compared with baseline tag ranking methods. We also apply the predicted rank list of tags to several higher-level computer vision applications in image understanding and retrieval, and demonstrate that MMRIM significantly improves the accuracy of these applications.

关键词： computer vision image retrieval

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Learning a Manifold as an Atlas

Learning a Manifold as an Atlas

引用

26th ieee conference on computer vision and pattern recognition (cvpr)

作者： Pitelis, Nikolaos Russell, Chris Agapito, Lourdes Univ London Sch EECS London WC1E 7HU England

ISBN: (纸本)9780769549897

In this work, we return to the underlying mathematical definition of a manifold and directly characterise learning a manifold as finding an atlas, or a set of overlapping charts, that accurately describe local structure. We formulate the problem of learning the manifold as an optimisation that simultaneously refines the continuous parameters defining the charts, and the discrete assignment of points to charts. In contrast to existing methods, this direct formulation of a manifold does not require "unwrapping" the manifold into a lower dimensional space and allows us to learn closed manifolds of interest to vision, such as those corresponding to gait cycles or camera pose. We report state-of-the-art results for manifold based nearest neighbour classification on vision datasets, and show how the same techniques can be applied to the 3D reconstruction of human motion from a single image.

关键词： computer vision image motion analysis image reconstruction learning (artificial intelligence) optimisation

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