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Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

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發(fā)表于 2025-3-21 17:49:53 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computer Vision -- ECCV 2014
副標題13th European Confer
編輯David Fleet,Tomas Pajdla,Tinne Tuytelaars
視頻videohttp://file.papertrans.cn/235/234172/234172.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati
描述.The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014..The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions..
出版日期Conference proceedings 2014
關鍵詞3D; activity recognition and understanding; artificial intelligence; computational photography; computer
版次1
doihttps://doi.org/10.1007/978-3-319-10578-9
isbn_softcover978-3-319-10577-2
isbn_ebook978-3-319-10578-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing Switzerland 2014
The information of publication is updating

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Pipe-Run Extraction and Reconstruction from Point Cloudseristics in this modeling problem, namely, primitives, similarities, and joints. While . capture the dominant cylindric shapes, . reveal the inter-primitive relations intrinsic to industrial structures because of human design and construction. Statistical analysis over point normals discovers primit
板凳
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Image-Based 4-d Reconstruction Using 3-d Change Detectiond models of the scene at each time instant. Instead, the approach employs an initial 3-d model which is continuously updated with changes in the environment to form a full 4-d representation. This updating scheme is enabled by a novel algorithm that infers 3-d changes with respect to the model at on
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Robust Global Translations with 1DSfMg for global camera rotations using averaging schemes, we focus on the problem of solving for 3D camera translations given a network of noisy pairwise camera translation directions (or 3D point observations). To do this well, we have two main insights. First, we propose a method for removing outlier
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RGBD Salient Object Detection: A Benchmark and Algorithmsodels. In this work, we first introduce a large scale RGBD image dataset to address the problem of data deficiency in current research of RGBD salient object detection. To make sure that most existing RGB saliency models can still be adequate in RGBD scenarios, we continue to provide a simple fusion
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發(fā)表于 2025-3-23 00:10:47 | 只看該作者
Saliency Detection with Flash and No-flash Image Pairsst, only the foreground objects are significantly brightened by the flash as they are relatively nearer to the camera than the background. Second, the brightness variations introduced by the flash provide hints to surface orientation changes. Accordingly, the first observation is explored to form th
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Alpha Matting of Motion-Blurred Objects in Bracket Sequence Imagesf a sharp, short-exposure snapshot in the sequence to help overcome major challenges in this task, including blurred object detection, spatially-variant object motion, and foreground/background color ambiguity. A key component of our matte estimation is the inference of approximate, spatially-varyin
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發(fā)表于 2025-3-23 08:07:44 | 只看該作者
An Active Patch Model for Real World Texture and Appearance Classificationstate of the art on datasets ranging from homogeneous texture (e.g., material texture), to less homogeneous texture (e.g., the fur of animals), and to inhomogeneous texture (the appearance patterns of vehicles). Our method uses a bag-of-words model where the features are based on a dictionary of act
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