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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是341-350 订阅

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L∞ minimization in geometric reconstruction problems

L∞ minimization in geometric reconstruction problems

引用

conference on computer vision and pattern recognition

作者： Hartley, R Schaffalitzky, F National ICT Australia Australia Australian National University Australia Oxford University United Kingdom

ISBN: (纸本)0769521584

We investigate the use of the L-infinity cost function in geometric vision problems. This cost function measures the maximum of a set of model-fitting errors, rather than the sum-of-squares, or L-2 cost function that is commonly used (in least-squares fitting). We investigate its use in two problems;multiview triangulation and motion recovery from omnidirectional cameras, though the results may also apply to other related problems. It is shown that for these problems the L-infinity cost function is significantly simpler than the L-2 Cost. In particular L-infinity minimization involves finding the minimum of a cost function with a single local (and hence global) minimum on a convex parameter domain. The problem may be recast as a constrained minimization problem and solved using commonly available software. The optimal solution was reliably achieved on problems of small dimension.

关键词： computer vision

来源：评论

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Shadow Removal with Paired and Unpaired Learning

Shadow Removal with Paired and Unpaired Learning

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Vasluianu, Florin-Alexandru Romero, Andres Van Gool, Luc Timofte, Radu Swiss Fed Inst Technol Zurich Switzerland

ISBN: (纸本)9781665448994

Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photorealistic restoration of the image contents. Decades of research produced a multitude of hand-crafted restoration techniques and, more recently, learned solutions from shadowed and shadow free training image pairs. In this work, we propose a single image shadow removal solution via self-supervised learning by using a conditioned mask. We rely on self-supervision and jointly learn deep models to remove and add shadows to images. We derive two variants for learning from paired images and unpaired images, respectively. Our validation on the recently introduced ISTD and USR datasets demonstrate large quantitative and qualitative improvements over the state-of-the-art for both paired and unpaired learning settings.

关键词： Training computer vision conferences Image restoration pattern recognition Task analysis Light sources

来源：评论

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Human Action Adverb recognition: ADHA Dataset and A Three-Stream Hybrid Model 31

Human Action Adverb Recognition: ADHA Dataset and A Three-St...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Pang, Bo Zha, Kaiwen Lu, Cewu Shanghai Jiao Tong Univ Shanghai Peoples R China

ISBN: (数字)9781538661000

ISBN: (纸本)9781538661000

We introduce the first benchmark for a new problem - recognizing human action adverbs (HAA): "Adverbs Describing Human Actions" (ADHA). We demonstrate some key features of ADHA: a semantically complete set of adverbs describing human actions, a set of common, describable human actions, and an exhaustive labelling of simultaneously emerging actions in each video. We commit an in-depth analysis on the implementation of current effective models in action recognition and image captioning on adverb recognition, and the results reveal that such methods are unsatisfactory. Furthermore, we propose a novel three-stream hybrid model to tackle the HAA problem, which achieves better performances and receives relatively promising results.

关键词： Image recognition Mood Emotion recognition Sports Benchmark testing Face pattern recognition

来源：评论

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MV-TAL: Mulit-view Temporal Action Localization in Naturalistic Driving

MV-TAL: Mulit-view Temporal Action Localization in Naturalis...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Li, Wei Chen, Shimin Gu, Jianyang Wang, Ning Chen, Chen Guo, Yandong OPPO Res Inst Beijing Peoples R China Zhejiang Univ Hangzhou Peoples R China East China Univ Sci & Technol Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Human risky behavior in driving is an important visual recognition problem. In this paper, we propose a multi-view temporal action localization system based on the grayscale video to achieve action recognition in naturalistic driving. Specifically, we adopted SwinTransformer as feature extractor, and a single framework to detect boundary and class at the same time. Also, we improve multiple loss function for explicit constraints of embedded feature distributions. Our proposed framework achieves the overall F1 -score of 0.3154 on A2 dataset.

关键词： Location awareness Measurement Visualization computer vision conferences Aggregates Gray-scale

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Optimal servoing for active foveated vision

Optimal servoing for active foveated vision

引用

1966 ieee computer society conference on computer vision and pattern recognition

作者： Rotstein, HP Rivlin, E TECHNION ISRAEL INST TECHNOL DEPT ELECT ENGNIL-32000 HAIFAISRAEL

ISBN: (纸本)0818672587

Foveated vision and two-mode tracking, as inspired by the human oculomotor system, are often used in active vision system. The purpose of this paper is to provide answers to the following basic questions which arise from implementations. First, is it beneficial to have foveated vision and what is the optimal size of the foveal window? Second, is there a need for two control mechanisms (smooth pursuit and saccade) for improved performance and how can one efficiently switch between them? In order to do so, a setup is proposed in which these strategies can be evaluated in a systematic manner. It is shown that the fovea appears as a compromise between the tightness of the tracking specifications and computational constraints. Introducing a model for the later and postulating some a priori knowledge of the target behavior, it is possible to compute the size of the fovea in an optimal way. As a by-product, 'smooth-pursuit' can be defined in a natural way, and the use of a two-mode tracking scheme is justified. The second mode, i.e. 'saccadic control', aims at re-centering the target on the fovea so that the smooth pursuit controller can continue to operate. It is shown that a control strategy can indeed be defined so that this objective can be met under appropriate operating conditions.

关键词： computer vision

来源：评论

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Practical pattern recognition system for translation, scale and rotation invariance

Practical pattern recognition system for translation, scale ...

引用

Proceedings of the 1994 ieee computer society conference on computer vision and pattern recognition

作者： Kin, Whoi-Yul Yuan, Po Univ of Texas at Dallas Richardson United States

ISBN: (纸本)0818658274

In this paper we present a practical pattern recognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-N N rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10 × 10 to 512 × 512 pixels.

关键词： pattern recognition systems

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Symmetry Detection from Real World Images Competition 2013: Summary and Results

Symmetry Detection from Real World Images Competition 2013: ...

引用

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

作者： Liu, Jingchen Slota, George Zheng, Gang Wu, Zhaohui Park, Minwoo Lee, Seungkyu Rauschert, Ingmar Liu, Yanxi Penn State Univ University Pk PA 16802 USA Object Video San Antonio TX USA Samsung Res Seoul South Korea

ISBN: (纸本)9780769549903

Symmetry is a pervasive phenomenon presenting itself in all forms and scales in natural and manmade environments. Its detection plays an essential role at all levels of human as well as machine perception. The recent resurging interest in computational symmetry for computer vision and computer graphics applications has motivated us to conduct a US NSF funded symmetry detection algorithm competition as a workshop affiliated with the computer vision and pattern recognition (CVPR) conference, 2013. This competition sets a more complete benchmark for computer vision symmetry detection algorithms. In this report we explain the evaluation metric and the automatic execution of the evaluation workflow. We also present and analyze the algorithms submitted, and show their results on three test sets of real world images depicting reflection, rotation and translation symmetries respectively. This competition establishes a performance baseline for future work on symmetry detection.

关键词： computer graphics computer vision

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Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution

Pixel-Guided Dual-Branch Attention Network for Joint Image D...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xi, Si Wei, Jia Zhang, Weidong Netease Games AI Lab Hangzhou Peoples R China

ISBN: (纸本)9781665448994

Image deblurring and super-resolution (SR) are computer vision tasks aiming to restore image detail and spatial scale, respectively. Besides, only a few recent works of literature contribute to this task, as conventional methods deal with SR or deblurring separately. We focus on designing a novel Pixel-Guided dual-branch attention network (PDAN) that handles both tasks jointly to address this issue. Then, we propose a novel loss function better focus on large and medium range errors. Extensive experiments demonstrated that the proposed PDAN with the novel loss function not only generates remarkably clear HR images and achieves compelling results for joint image deblurring and SR tasks. In addition, our method achieves second place in NTIRE 2021 Challenge on track 1 of the Image Deblurring Challenge.

关键词： Training computer vision conferences Superresolution Feature extraction Image restoration pattern recognition

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Training support vector machines: an application to face detection

Training support vector machines: an application to face det...

引用

1997 ieee computer society conference on computer vision and pattern recognition (CVPR 97)

作者： Osuna, E Freund, R Girosi, F Massachusetts Inst of Technology Cambridge United States

ISBN: (纸本)0780342364

We investigate the application of Support Vector Machines (SVMs) in computet vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radical Basis Functions classifiers. The decision surfaces are found by solving a linearly constrained quadratic programming problem. This optimization problem is challenging because the quadratic form is completely dense and the memory requirements grow with the square of the number of data points. We present a decomposition algorithm that guarantees global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping criteria for the algorithm. We present experimental results of our implementation of SVM, and demonstrate the feasibility of our approach on a face detection problem that involves a data set of 50,000 data points.

关键词： computer vision

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Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example

Deep Graphics Encoder for Real-Time Video Makeup Synthesis f...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Kips, Robin Jiang, Ruowei Ba, Sileye Phung, Edmund Aarabi, Parham Gori, Pietro Perrot, Matthieu Bloch, Isabelle LOreal Res & Innovat Clichy France Inst Polytech Paris Telecom Paris LTCI Paris France Modiface Toronto ON Canada Sorbonne Univ CNRS LIP6 Paris France

ISBN: (纸本)9781665448994

While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task. In this paper, we introduce an inverse computer graphics method for automatic makeup synthesis from a reference image, by learning a model that maps an example portrait image with makeup to the space of rendering parameters. This method can be used by artists to automatically create realistic virtual cosmetics image samples, or by consumers, to virtually try-on a makeup extracted from their favorite reference image.

关键词： computer vision conferences Estimation Streaming media Rendering (computer graphics) Real-time systems pattern recognition

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