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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991-1000 订阅

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Learning without Human Scores for Blind Image Quality Assessment

Learning without Human Scores for Blind Image Quality Assess...

引用

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

作者： Xue, Wufeng Zhang, Lei Mou, Xuanqin Xi An Jiao Tong Univ Inst Im Pro & Pat Rec Xian Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9780769549897

General purpose blind image quality assessment (BIQA) has been recently attracting significant attention in the fields of image processing, vision and machine learning. State-of-the-art BIQA methods usually learn to evaluate the image quality by regression from human subjective scores of the training samples. However, these methods need a large number of human scored images for training, and lack an explicit explanation of how the image quality is affected by image local features. An interesting question is then: can we learn for effective BIQA without using human scored images? this paper makes a good effort to answer this question. We partition the distorted images into overlapped patches, and use a percentile pooling strategy to estimate the local quality of each patch. then a quality-aware clustering (QAC) method is proposed to learn a set of centroids on each quality level. these centroids are then used as a codebook to infer the quality of each patch in a given image, and subsequently a perceptual quality score of the whole image can be obtained. the proposed QAC based BIQA method is simple yet effective. It not only has comparable accuracy to those methods using human scored images in learning, but also has merits such as high linearity to human perception of image quality, real-time implementation and availability of image local quality map.

关键词： image processing learning (artificial intelligence) pattern clustering regression analysis

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An Embedded vision Services Framework for Heterogeneous Accelerators

An Embedded Vision Services Framework for Heterogeneous Acce...

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

作者： Gudis, Eduardo Lu, Pullan Berends, David Kaighn, Kevin van der Wal, Gooitzen Buchanan, Gregory Chai, Sek Piacentino, Michael SRI Int Princeton NJ 08543 USA

ISBN: (纸本)9780769549903

this paper describes an architecture framework using heterogeneous hardware accelerators for embedded vision applications. this approach leverages the recent single-chip heterogeneous FPGAs that combine powerful multicore processors with extensive programmable gate array fabric on the same die. We present a framework using an extensive library of pipelined real time vision hardware accelerators and a service-based software architecture. this field-proven system design approach provides embedded vision developers with a powerful software abstraction layer for rapidly and efficiently integrating any of hardware accelerators for applications such as image stabilization, moving target indication, contrast normalization enhancement, and others. the framework allows the service-based software to take advantage of the hardware acceleration blocks available and perform the remainder of the processing in software. As performance requirements increase, more hardware acceleration can be added to the FPGA fabric, thus offloading the main processor.

关键词： computer vision field programmable gate arrays programmable circuits software architecture

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Overhead-based image and video geo-localization framework

Overhead-based image and video geo-localization framework

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

作者： Hammoud, Riad I. Kuzdeba, Scott A. Berard, Brian Tom, Victor Ivey, Richard Bostwick, Renu HandUber, Jason Vinciguerra, Lori Shnidman, Nathan Smiley, Byron BAE Syst Burlington MA 01803 USA

ISBN: (纸本)9780769549903

this paper presents a geo-localization framework of street-level outdoor images using multiple sources of overhead reference imagery including LIDAR, Digital Elevation Maps and Multi-Spectral Land Cover/Use imagery. We describe five different matchers and an adaptive linear fusion process which combines individual matchers' probability maps into a single map. these matchers exploit mountain elevation profiles, rendered camera views, landmarks, landuse classes and building heights. We successfully validated our framework on 100 queries with geographic truth in two world regions (each of 10, 000km(2)) in the USA.

关键词： digital elevation models geographic information systems geophysical image processing optical radar probability rendering (computer graphics) terrain mapping video signal processing

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Probabilistic Elastic Matching for Pose Variant Face Verification

Probabilistic Elastic Matching for Pose Variant Face Verific...

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

作者： Li, Haoxiang Hua, Gang Lin, Zhe Brandt, Jonathan Yang, Jianchao Stevens Inst Technol Hoboken NJ 07030 USA Adobe Syst Inc San Jose CA 95110 USA

ISBN: (纸本)9780769549897

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic matching method. We take a part based representation by extracting local features (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each feature with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of all face images in the training corpus. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms. Each Gaussian component builds correspondence of a pair of features to be matched between two faces/face tracks. For face verification, we train an SVM on the vector concatenating the difference vectors of all the feature pairs to decide if a pair of faces/face tracks is matched or not. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that our method outperforms the state-of-the-art in the most restricted protocol on Labeled Face in the Wild (LFW) and the YouTube video face database by a significant margin.

关键词： Gaussian processes belief networks face recognition feature extraction image matching probability support vector machines

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Shape and Texture Based Countermeasure to Protect Face recognition Systems Against Mask Attacks

Shape and Texture Based Countermeasure to Protect Face Recog...

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

作者： Kose, Neslihan Dugelay, Jean-Luc EURECOM Multimedia Dept Sophia Antipolis France

ISBN: (纸本)9780769549903

Photographs, videos or masks can be used to spoof face recognition systems. In this paper, a countermeasure is proposed to protect face recognition systems against 3D mask attacks. the reason for the lack of studies on countermeasures against mask attacks is mainly due to the unavailability of public databases dedicated to mask attack. In this study, a 2D+3D mask attacks database is used that is prepared for a research project in which the authors are all involved. the proposed countermeasure is based on the fusion of the information extracted from both the texture and the depth images in the mask database, and provides satisfactory results to protect recognition systems against mask attacks. Another contribution of this study is that the countermeasure is integrated to the selected baseline systems for 2D and 3D face recognition, which provides to analyze the performances of the systems with/without attacks and with/without the countermeasure.

关键词： face recognition image fusion image texture visual databases

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Deep Convolutional Network Cascade for Facial Point Detection

Deep Convolutional Network Cascade for Facial Point Detectio...

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

作者： Sun, Yi Wang, Xiaogang Tang, Xiaoou Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China Chinese Univ Hong Kong Dept Elect Engn Hong Kong Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China

ISBN: (纸本)9780769549897

We propose a new approach for estimation of the positions of facial keypoints with three-level carefully designed convolutional networks. At each level, the outputs of multiple networks are fused for robust and accurate estimation. thanks to the deep structures of convolutional networks, global high-level features are extracted over the whole face region at the initialization stage, which help to locate high accuracy keypoints. there are two folds of advantage for this. First, the texture context information over the entire face is utilized to locate each keypoint. Second, since the networks are trained to predict all the keypoints simultaneously, the geometric constraints among keypoints are implicitly encoded. the method therefore can avoid local minimum caused by ambiguity and data corruption in difficult image samples due to occlusions, large pose variations, and extreme lightings. the networks at the following two levels are trained to locally refine initial predictions and their inputs are limited to small regions around the initial predictions. Several network structures critical for accurate and robust facial point detection are investigated. Extensive experiments show that our approach outperforms state-of-the- art methods in both detection accuracy and reliability(1).

关键词： face recognition feature extraction hidden feature removal image texture pose estimation

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Notice of Violation of ieee Publication Principles: Hand gesture recognition framework for recognizing sign gestures and handling movement epenthesis using Level Building nested dynamic programming approach

Notice of Violation of IEEE Publication Principles: Hand ges...

引用

Canadian conference on Electrical and computer Engineering (CCECE)

作者： R Elakkiya K Selvamani S Kanimozhi Agni College of Technology Chennai Anna University Chennai

Notice of Violation of ieee Publication Principles “Hand Gesture recognition Framework for Recognizing Sign Gestures and Handling Movement Epenthesis Using Level Building Nested Dynamic Programming Approach” by Elakkiya A., Selvamani K., Kanimozhi S. in the Proceedings of the ieee 27th Canadian conference on Electrical and computer Engineering (CCECE), May 2014 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of ieee’s Publication Principles. this paper duplicated extensive amounts of text from the paper cited below. the original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. A. Elakkiya was solely responsible for the copied material. Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article: “Enhanced Level Building Algorithm for the Movement Epenthesis Problem in Sign Language recognition” by Ruiduo Yang, Sudeep Sarkar, Barbara Loeding in the Proceedings of the ieee conference on computer vision and pattern recognition (cvpr), June 2007 In this research paper, two crucial problems in continuous sign language recognition from unaided video sequences are considered. At the feature level, the problem of hand segmentation and grouping is considered and at the sentence level, the movement epenthesis problem is considered. A framework that can handle both of these problems based on an enhanced, nested version of the dynamic programming approach is constructed. To handle movement epenthesis problem, a nested version of a dynamic programming framework, called Level Building is used to simultaneously segment and to match signs from continuous sign language sentences. this approach is then coupled with a trigram grammar model to optimally segment and l

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What is a 'Good' Periocular Region for recognition?

What is a 'Good' Periocular Region for Recognition?

引用

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

作者： Smereka, Jonathon M. Kumar, B. V. K. Vijaya

ISBN: (纸本)9780769549903

In challenging image acquisition settings where the performance of iris recognition algorithms degrades due to poor segmentation of the iris, image blur, specular reflections, and occlusions from eye lids and eye lashes, the periocular region has been shown to offer better recognition rates. However, the definition of a periocular region is subject to interpretation. this paper investigates the question of what is the best periocular region for recognition by identifying sub-regions of the ocular image when using near-infrared (NIR) or visible light (VL) sensors. To determine the best periocular region, we test two fundamentally different algorithms on challenging periocular datasets of contrasting build on four different periocular regions. Our results indicate that system performance does not necessarily improve as the ocular region becomes larger. Rather in NIR images the eye shape is more important than the brow or cheek as the image has little to no skin texture (leading to a smaller accepted region), while in VL images the brow is very important (requiring a larger region).

关键词： image restoration image segmentation image sensors iris recognition

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Single-Sample Face recognition with Image Corruption and Misalignment via Sparse Illumination Transfer

Single-Sample Face Recognition with Image Corruption and Mis...

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

作者： Zhuang, Liansheng Yang, Allen Y. Zhou, Zihan Sastry, S. Shankar Ma, Yi Univ Sci & Technol China Hefei 230026 Peoples R China Univ Calif Berkeley Dept EECS Berkeley CA 94720 USA Univ Illinois Dept ECE Chicago IL 60680 USA

ISBN: (纸本)9780769549897

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel face recognition algorithm to address this problem based on a sparse representation based classification (SRC) framework. the new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required training images to one sample per class. To compensate the missing illumination information typically provided by multiple training images, a sparse illumination transfer (SIT) technique is introduced. the SIT algorithms seek additional illumination examples of face images from one or more additional subject classes, and form an illumination dictionary. By enforcing a sparse representation of the query image, the method can recover and transfer the pose and illumination information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the existing algorithms in the single-sample regime and with less restrictions. In particular, the face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple training images;and the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

关键词： computer vision face recognition image classification image representation image resolution image retrieval lighting pose estimation

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Evaluating Open-Universe Face Identification on the Web

Evaluating Open-Universe Face Identification on the Web

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

作者： Becker, Brian C. Ortiz, Enrique G. Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (纸本)9780769549903

Face recognition is becoming a widely used technique to organize and tag photos. Whether searching, viewing, or organizing photos on the web or in personal photo albums, there is a growing demand to index real-world photos by the subjects in them. Even consumer platforms such as Google Picasa, Microsoft Photo Gallery, and social network sites such as Facebook have integrated forms of automated face tagging and recognition;furthermore, a number of libraries and cloud-based APIs that perform face recognition have become available. With such a plethora of choices, comparisons of recent advances become more important to gauge the state of progress in the field. this paper evaluates face identification in the context of not only research algorithms, but also considers consumer photo products, client-side libraries, and cloud-based APIs on a new, large-scale dataset derived from PubFig83 and LFW in a realistic open-universe scenario.

关键词： application program interfaces cloud computing face recognition

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