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

共 1569 条记录，以下是121-130 订阅

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Adherent Raindrop Detection and Removal in Video

Adherent Raindrop Detection and Removal in Video

引用

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

作者： You, Shaodi Tan, Robby T. Kawakami, Rei Ikeuchi, Katsushi Univ Tokyo Tokyo 1138654 Japan Univ Utrecht NL-3508 TC Utrecht Netherlands

ISBN: (纸本)9780769549897

Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. the core idea is to exploit the local spatio-temporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method.

关键词： computer vision drops image restoration natural scenes spatiotemporal phenomena video retrieval video signal processing

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Spatio-Temporal Depth Cuboid Similarity Feature for Activity recognition Using Depth Camera

Spatio-Temporal Depth Cuboid Similarity Feature for Activity...

引用

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

作者： Xia, Lu Aggarwal, J. K. Univ Texas Austin Comp & Vis Res Ctr Dept ECE Austin TX 78712 USA

ISBN: (纸本)9780769549897

Local spatio-temporal interest points (STIPs) and the resulting features from RGB videos have been proven successful at activity recognition that can handle cluttered backgrounds and partial occlusions. In this paper, we propose its counterpart in depth video and show its efficacy on activity recognition. We present a filtering method to extract STIPs from depth videos (called DSTIP) that effectively suppress the noisy measurements. Further, we build a novel depth cuboid similarity feature (DCSF) to describe the local 3D depth cuboid around the DSTIPs with an adaptable supporting size. We test this feature on activity recognition application using the public MSRAction3D, MSRDailyActivity3D datasets and our own dataset. Experimental evaluation shows that the proposed approach outperforms state-of-the-art activity recognition algorithms on depth videos, and the framework is more widely applicable than existing approaches. We also give detailed comparisons with other features and analysis of choice of parameters as a guidance for applications.

关键词： cameras feature extraction filtering theory image denoising image recognition video signal processing

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An iterated l₁ Algorithm for Non-smooth Non-convex Optimization in computer vision

An iterated <i>l</i><sub>1</sub> Algorithm for Non-smooth No...

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

作者： Ochs, Peter Dosovitskiy, Alexey Brox, thomas Pock, thomas Univ Freiburg Freiburg Germany Graz Univ Technol A-8010 Graz Austria

ISBN: (纸本)9780769549897

Natural image statistics indicate that we should use non-convex norms for most regularization tasks in image processing and computer vision. Still, they are rarely used in practice due to the challenge to optimize them. Recently, iteratively reweighed l(1) minimization has been proposed as a way to tackle a class of non-convex functions by solving a sequence of convex l(2)-l(1) problems. Here we extend the problem class to linearly constrained optimization of a Lipschitz continuous function, which is the sum of a convex function and a function being concave and increasing on the non-negative orthant (possibly non-convex and non-concave on the whole space). this allows to apply the algorithm to many computer vision tasks. We show the effect of non-convex regularizers on image denoising, deconvolution, optical flow, and depth map fusion. Non-convexity is particularly interesting in combination with total generalized variation and learned image priors. Efficient optimization is made possible by some important properties that are shown to hold.

关键词： computer vision concave programming convex programming deconvolution image denoising image sequences iterative methods minimisation

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Layer Depth Denoising and Completion for Structured-Light RGB-D Cameras

Layer Depth Denoising and Completion for Structured-Light RG...

引用

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

作者： Shen, Ju Cheung, Sen-ching S. Univ Kentucky Ctr Visualizat & Virtual Environm Lexington KY 40506 USA

ISBN: (纸本)9780769549897

the recent popularity of structured-light depth sensors has enabled many new applications from gesture-based user interface to 3D reconstructions. the quality of the depth measurements of these systems, however, is far from perfect. Some depth values can have significant errors, while others can be missing altogether. the uncertainty in depth measurements among these sensors can significantly degrade the performance of any subsequent vision processing. In this paper, we propose a novel probabilistic model to capture various types of uncertainties in the depth measurement process among structured-light systems. the key to our model is the use of depth layers to account for the differences between foreground objects and background scene, the missing depth value phenomenon, and the correlation between color and depth channels. the depth layer labeling is solved as a maximum a-posteriori estimation problem, and a Markov Random Field attuned to the uncertainty in measurements is used to spatially smooth the labeling process. Using the depth-layer labels, we propose a depth correction and completion algorithm that outperforms other techniques in the literature.

关键词： Markov processes cameras computer vision image colour analysis image denoising image reconstruction maximum likelihood estimation probability random processes smoothing methods stereo image processing

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Learning Locally-Adaptive Decision Functions for Person Verification

Learning Locally-Adaptive Decision Functions for Person Veri...

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

作者： Li, Zhen Chang, Shiyu Liang, Feng Huang, thomas S. Cao, Liangliang Smith, John R. UIUC Urbana IL 61801 USA IBM Res Corp Armonk NY 10504 USA

ISBN: (纸本)9780769549897

this paper considers the person verification problem in modern surveillance and video retrieval systems. the problem is to identify whether a pair of face or human body images is about the same person, even if the person is not seen before. Traditional methods usually look for a distance (or similarity) measure between images (e.g., by metric learning algorithms), and make decisions based on a fixed threshold. We show that this is nevertheless insufficient and sub-optimal for the verification problem. this paper proposes to learn a decision function for verification that can be viewed as a joint model of a distance metric and a locally adaptive thresholding rule. We further formulate the inference on our decision function as a second-order large-margin regularization problem, and provide an efficient algorithm in its dual from. We evaluate our algorithm on both human body verification and face verification problems. Our method outperforms not only the classical metric learning algorithm including LMNN and ITML, but also the state-of-the-art in the computer vision community.

关键词： face recognition learning (artificial intelligence) video retrieval

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Hybrid body representation for integrated pose recognition, localization and segmentation

Hybrid body representation for integrated pose recognition, ...

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26th ieee conference on computer vision and pattern recognition, cvpr

作者： Chen, G. Fan, Guoliang School of Electrical and Computer Engineering Oklahoma State University Stillwater OK 74078 United States

ISBN: (纸本)9781424422432

We propose a hybrid body representation that represents each typical pose by both template-like view information and part-based structural information. Specifically, each body part as well as the whole body are represented by an off-line learned shape model where both region-based and edge-based priors are combined in a coupled shape representation. Part-based spatial priors are represented by a "star" graphical model. this hybrid body representation can synergistically integrate pose recognition, localization and segmentation into one computational flow. Moreover, as an important step for feature extraction and model inference, segmentation is involved in the low-level, mid-level and high-level vision stages, where top-down prior knowledge and bottom-up data processing is well integrated via the proposed hybrid body representation. ©2008 ieee.

关键词： Gesture recognition

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Perceptual Organization and recognition of Indoor Scenes from RGB-D Images

Perceptual Organization and Recognition of Indoor Scenes fro...

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

作者： Gupta, Saurabh Arbelaez, Pablo Malik, Jitendra Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9780769549897

We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach on the recently introduced NYU-Depth V2 (NYUD2) dataset [27]. We propose algorithms for object boundary detection and hierarchical segmentation that generalize the gPb - ucm approach of [2] by making effective use of depth information. We show that our system can label each contour with its type (depth, normal or albedo). We also propose a generic method for long-range amodal completion of surfaces and show its effectiveness in grouping. We then turn to the problem of semantic segmentation and propose a simple approach that classifies superpixels into the 40 dominant object categories in NYUD2. We use both generic and class-specific features to encode the appearance and geometry of objects. We also show how our approach can be used for scene classification, and how this contextual information in turn improves object recognition. In all of these tasks, we report significant improvements over the state-of-the-art.

关键词： feature extraction geometry image classification image coding image colour analysis image resolution image segmentation object detection object recognition

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iCub World: Friendly Robots Help Building Good vision Data-Sets

iCub World: Friendly Robots Help Building Good Vision Data-S...

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

作者： Fanello, Sean Ryan Ciliberto, Carlo Santoro, Matteo Natale, Lorenzo Metta, Giorgio Rosasco, Lorenzo Odone, Francesca Ist Italiano Tecnol iCub Facil Genoa Italy Univ Genoa DIBRIS Genoa Italy Italian Inst Technol MIT LCSL I-16163 Genoa Italy

ISBN: (纸本)9780769549903

In this paper we present and start analyzing the iCub World data-set, an object recognition data-set, we acquired using a Human-Robot Interaction (HRI) scheme and the iCub humanoid robot platform. Our set up allows for rapid acquisition and annotation of data with corresponding ground truth. While more constrained in its scopes - the iCub world is essentially a robotics research lab - we demonstrate how the proposed data-set poses challenges to current recognition systems. the iCubWorld data-set is publicly available (1).

关键词： human-robot interaction humanoid robots object recognition robot vision

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Axially Symmetric 3D Pots Configuration System using Axis of Symmetry and Break Curve

Axially Symmetric 3D Pots Configuration System using Axis of...

引用

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

作者： Son, Kilho Almeida, Eduardo B. Cooper, David B. Brown Univ Providence RI 02912 USA

ISBN: (纸本)9780769549897

this paper introduces a novel approach for reassembling pot sherds found at archaeological excavation sites, for the purpose of reconstructing clay pots that had been made on a wheel. these pots and the sherds into which they have broken are axially symmetric. the reassembly process can be viewed as 3D puzzle solving or generalized cylinder learning from broken fragments. the estimation exploits both local and semi-global geometric structure, thus making it a fundamental problem of geometry estimation from noisy fragments in computer vision and pattern recognition. the data used are densely digitized 3D laser scans of each fragment's outer surface. the proposed reassembly system is automatic and functions when the pile of available fragments is from one or multiple pots, and even when pieces are missing from any pot. the geometric structure used are curves on the pot along which the surface had broken and the silhouette of a pot with respect to an axis, called axis-profile curve (APC). For reassembling multiple pots with or without missing pieces, our algorithm estimates the APC from each fragment, then reassembles into configurations the ones having distinctive APC. Further growth of configurations is based on adding remaining fragments such that their APC and break curves are consistent with those of a configuration. the method is novel, more robust and handles the largest numbers of fragments to date.

关键词： computer vision image reconstruction

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Cross-View Action recognition via a Continuous Virtual Path

Cross-View Action Recognition via a Continuous Virtual Path

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

作者： Zhang, Zhong Wang, Chunheng Xiao, Baihua Zhou, Wen Liu, Shuang Shi, Cunzhao CASIA State Key Lab Management & Control Complex Syst Beijing Peoples R China

ISBN: (纸本)9780769549897

In this paper, we propose a novel method for cross-view action recognition via a continuous virtual path which connects the source view and the target view. Each point on this virtual path is a virtual view which is obtained by a linear transformation of the action descriptor. All the virtual views are concatenated into an infinite-dimensional feature to characterize continuous changes from the source to the target view. However, these infinite-dimensional features cannot be used directly. thus, we propose a virtual view kernel to compute the value of similarity between two infinite-dimensional features, which can be readily used to construct any kernelized classifiers. In addition, there are a lot of unlabeled samples from the target view, which can be utilized to improve the performance of classifiers. thus, we present a constraint strategy to explore the information contained in the unlabeled samples. the rationality behind the constraint is that any action video belongs to only one class. Our method is verified on the IXMAS dataset, and the experimental results demonstrate that our method achieves better performance than the state-of-the-art methods.

关键词： feature extraction image classification image motion analysis video signal processing

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