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

共 656 条记录，以下是91-100 订阅

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Ensemble Learning for Confidence Measures in Stereo vision

Ensemble Learning for Confidence Measures in Stereo Vision

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

作者： Haeusler, Ralf Nair, Rahul Kondermann, Daniel Univ Auckland Dept Comp Sci Auckland 1 New Zealand Heidelberg Univ Heidelberg Collaboratory Image Proc D-69115 Heidelberg Germany

ISBN: (纸本)9780769549897

With the aim to improve accuracy of stereo confidence measures, we apply the random decision forest framework to a large set of diverse stereo confidence measures. Learning and testing sets were drawn from the recently introduced KITTI dataset, which currently poses higher challenges to stereo solvers than other benchmarks with ground truth for stereo evaluation. We experiment with semi global matching stereo (SGM) and a census dataterm, which is the best performing real-time capable stereo method known to date. On KITTI images, SGM still produces a significant amount of error. We obtain consistently improved area under curve values of sparsification measures in comparison to best performing single stereo confidence measures where numbers of stereo errors are large. More specifically, our method performs best in all but one out of 194 frames of the KITTI dataset.

关键词： decision trees image matching learning (artificial intelligence) stereo image processing

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Generation of Ground Truth for Object Detection While Playing an Online Game: Productive Gaming or Recreational Working?

Generation of Ground Truth for Object Detection While Playin...

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

作者： Kavasidis, Isaak Spampinato, Concetto Giordano, Daniela Univ Catania Catania Italy

ISBN: (纸本)9780769549903

In this paper we present a flash game that aims at generating easily ground truth for testing object detection algorithms. Flash the Fish is an online game where the user is shown videos from underwater environments and has to take photos of fish by clicking on them. the initial ground truth is provided by object detection algorithms and, subsequent, cluster analysis and user evaluation techniques, allow for the generation of ground truth based on the weighted combination of these "photos". Evaluation of the platform and comparison of the obtained results against a hand drawn ground truth confirmed that reliable ground truth generation is not necessarily a cumbersome task both in terms of effort and time needed.

关键词： computer games object detection

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Attribute-Based Detection of Unfamiliar Classes with Humans in the Loop

Attribute-Based Detection of Unfamiliar Classes with Humans ...

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

作者： Wah, Catherine Belongie, Serge Univ Calif San Diego Dept Comp Sci & Engn San Diego CA 92103 USA

ISBN: (纸本)9780769549897

Recent work in computer vision has addressed zero-shot learning or unseen class detection, which involves categorizing objects without observing any training examples. However, these problems assume that attributes or defining characteristics of these unobserved classes are known, leveraging this information at test time to detect an unseen class. We address the more realistic problem of detecting categories that do not appear in the dataset in any form. We denote such a category as an unfamiliar class;it is neither observed at train time, nor do we possess any knowledge regarding its relationships to attributes. this problem is one that has received limited attention within the computer vision community. In this work, we propose a novel approach to the unfamiliar class detection task that builds on attribute-based classification methods, and we empirically demonstrate how classification accuracy is impacted by attribute noise and dataset "difficulty," as quantified by the separation of classes in the attribute space. We also present a method for incorporating human users to overcome deficiencies in attribute detection. We demonstrate results superior to existing methods on the challenging CUB-200-2011 dataset.

关键词： computer vision image classification learning (artificial intelligence) object detection

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Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object recognition

Efficient 2D-to-3D Correspondence Filtering for Scalable 3D ...

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

作者： Hao, Qiang Cai, Rui Li, Zhiwei Zhang, Lei Pang, Yanwei Wu, Feng Rui, Yong Tianjin Univ Tianjin 300072 Peoples R China Microsoft Res Asia Beijing 100080 Peoples R China

ISBN: (纸本)9780769549897

3D model-based object recognition has been a noticeable research trend in recent years. Common methods find 2D-to-3D correspondences and make recognition decisions by pose estimation, whose efficiency usually suffers from noisy correspondences caused by the increasing number of target objects. To overcome this scalability bottleneck, we propose an efficient 2D-to-3D correspondence filtering approach, which combines a light-weight neighborhood-based step with a finer-grained pairwise step to remove spurious correspondences based on 2D/3D geometric cues. On a dataset of 300 3D objects, our solution achieves similar to 10 times speed improvement over the baseline, with a comparable recognition accuracy. A parallel implementation on a quad-core CPU can run at similar to 3fps for 1280 x 720 images.

关键词： filtering theory geometry object recognition pose estimation target tracking

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Decoding, Calibration and Rectification for Lenselet-Based Plenoptic Cameras

Decoding, Calibration and Rectification for Lenselet-Based P...

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

作者： Dansereau, Donald G. Pizarro, Oscar Williams, Stefan B. Univ Sydney Australian Ctr Field Robot Sch Aerosp Mech & Mechatron Engn Sydney NSW 2006 Australia

ISBN: (纸本)9780769549897

Plenoptic cameras are gaining attention for their unique light gathering and post-capture processing capabilities. We describe a decoding, calibration and rectification procedure for lenselet-based plenoptic cameras appropriate for a range of computer vision applications. We derive a novel physically based 4D intrinsic matrix relating each recorded pixel to its corresponding ray in 3D space. We further propose a radial distortion model and a practical objective function based on ray reprojection. Our 15-parameter camera model is of much lower dimensionality than camera array models, and more closely represents the physics of lenselet-based cameras. Results include calibration of a commercially available camera using three calibration grid sizes over five datasets. Typical RMS ray reprojection errors are 0.0628, 0.105 and 0.363 mm for 3.61, 7.22 and 35.1 mm calibration grids, respectively. Rectification examples include calibration targets and real-world imagery.

关键词： computer vision image sensors matrix algebra

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Recovering Line-networks in Images by Junction-Point Processes

Recovering Line-networks in Images by Junction-Point Process...

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

作者： Chai, Dengfeng Foerstner, Wolfgang Lafarge, Florent Zhejiang Univ Hangzhou Zhejiang Peoples R China Univ Bonn Bonn Germany INRIA Sophia Antipolis France

ISBN: (纸本)9780769549897

the automatic extraction of line-networks from images is a well-known computer vision issue. Appearance and shape considerations have been deeply explored in the literature to improve accuracy in presence of occlusions, shadows, and a wide variety of irrelevant objects. However most existing works have ignored the structural aspect of the problem. We present an original method which provides structurally-coherent solutions. Contrary to the pixel-based and object-based methods, our result is a graph in which each node represents either a connection or an ending in the line-network. Based on stochastic geometry, we develop a new family of point processes consisting in sampling junction-points in the input image by using a Monte Carlo mechanism. the quality of a configuration is measured by a probability density which takes into account both image consistency and shape priors. Our experiments on a variety of problems illustrate the potential of our approach in terms of accuracy, flexibility and efficiency.

关键词： Monte Carlo methods computer vision graph theory image sampling probability

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Tracking People and their Objects

Tracking People and Their Objects

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

作者： Baumgartner, Tobias Mitzel, Dennis Leibe, Bastian Rhein Westfal TH Aachen Comp Vis Grp Aachen Germany

ISBN: (纸本)9780769549897

Current pedestrian tracking approaches ignore important aspects of human behavior. Humans are not moving independently, but they closely interact with their environment, which includes not only other persons, but also different scene objects. Typical everyday scenarios include people moving in groups, pushing child strollers, or pulling luggage. In this paper, we propose a probabilistic approach for classifying such person-object interactions, associating objects to persons, and predicting how the interaction will most likely continue. Our approach relies on stereo depth information in order to track all scene objects in 3D, while simultaneously building up their 3D shape models. these models and their relative spatial arrangement are then fed into a probabilistic graphical model which jointly infers pairwise interactions and object classes. the inferred interactions can then be used to support tracking by recovering lost object tracks. We evaluate our approach on a novel dataset containing more than 15,000 frames of person-object interactions in 325 video sequences and demonstrate good performance in challenging real-world scenarios.

关键词： image classification image motion analysis image sequences object tracking pedestrians probability solid modelling stereo image processing

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Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem

Rolling Riemannian Manifolds to Solve the Multi-class Classi...

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

作者： Caseiro, Rui Martins, Pedro Henriques, Joao F. Leite, Fatima Silva Batista, Jorge Univ Coimbra Inst Syst & Robot P-3000 Coimbra Portugal

ISBN: (纸本)9780769549897

In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [,]. A popular framework, valid over any Riemannian manifold, was proposed in [] for binary classification. Once moving from binary to multi-class classification this paradigm is not valid anymore, due to the spread of multiple positive classes on the manifold []. It is then natural to ask whether the multi-class paradigm could be extended to operate on a large class of Riemannian manifolds. We propose a mathematically well-founded classification paradigm that allows to extend the work in [] to multi-class models, taking into account the structure of the space. the idea is to project all the data from the manifold onto an affine tangent space at a particular point. To mitigate the distortion induced by local diffeomorphisms, we introduce for the first time in the computer vision community a well-founded mathematical concept, so-called Rolling map [,] the novelty in this alternate school of thought is that the manifold will be firstly rolled (without slipping or twisting) as a rigid body, then the given data is unwrapped onto the affine tangent space, where the classification is performed.

关键词： computer vision image classification learning (artificial intelligence)

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Optimizing 1-Nearest Prototype Classifiers

Optimizing 1-Nearest Prototype Classifiers

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

作者： Wohlhart, Paul Koestinger, Martin Donoser, Michael Roth, Peter M. Bischof, Horst Graz Univ Technol Inst Comp Vis & Graph A-8010 Graz Austria

ISBN: (纸本)9780769549897

the development of complex, powerful classifiers and their constant improvement have contributed much to the progress in many fields of computer vision. However, the trend towards large scale datasets revived the interest in simpler classifiers to reduce runtime. Simple nearest neighbor classifiers have several beneficial properties, such as low complexity and inherent multi-class handling, however, they have a runtime linear in the size of the database. Recent related work represents data samples by assigning them to a set of prototypes that partition the input feature space and afterwards applies linear classifiers on top of this representation to approximate decision boundaries locally linear. In this paper, we go a step beyond these approaches and purely focus on 1-nearest prototype classification, where we propose a novel algorithm for deriving optimal prototypes in a discriminative manner from the training samples. Our method is implicitly multi-class capable, parameter free, avoids noise overfitting and, since during testing only comparisons to the derived prototypes are required, highly efficient. Experiments demonstrate that we are able to outperform related locally linear methods, while even getting close to the results of more complex classifiers.

关键词： computational complexity computer vision image classification optimisation

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Efficient object detection and segmentation for fine-grained recognition

Efficient object detection and segmentation for fine-grained...

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

作者： Angelova, Anelia Zhu, Shenghuo NEC Labs Amer Cupertino CA 95014 USA

ISBN: (纸本)9780769549897

We propose a detection and segmentation algorithm for the purposes of fine-grained recognition. the algorithm first detects low-level regions that could potentially belong to the object and then performs a full-object segmentation through propagation. Apart from segmenting the object, we can also 'zoom in' on the object, i.e. center it, normalize it for scale, and thus discount the effects of the background. We then show that combining this with a state-of-the-art classification algorithm leads to significant improvements in performance especially for datasets which are considered particularly hard for recognition, e. g. birds species. the proposed algorithm is much more efficient than other known methods in similar scenarios [ 4, 21]. Our method is also simpler and we apply it here to different classes of objects, e. g. birds, flowers, cats and dogs. We tested the algorithm on a number of benchmark datasets for fine-grained categorization. It outperforms all the known state-of-the-art methods on these datasets, sometimes by as much as 11%. It improves the performance of our baseline algorithm by 3-4%, consistently on all datasets. We also observed more than a 4% improvement in the recognition performance on a challenging large-scale flower dataset, containing 578 species of flowers and 250,000 images.

关键词： image classification image segmentation object detection object recognition

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