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Computer Vision -- ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I de Kyoung Mu Lee

Computer Vision -- ACCV 2012: 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I: Lecture Notes in Computer Science, cartea 7724

Editat de Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu
en Limba Engleză Paperback – 4 apr 2013
The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869 submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.
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Specificații

ISBN-13: 9783642373305
ISBN-10: 3642373305
Pagini: 864
Ilustrații: XLII, 821 p. 349 illus.
Dimensiuni: 155 x 235 x 45 mm
Greutate: 1.18 kg
Ediția:2013
Editura: Springer Berlin, Heidelberg
Colecția Springer
Seriile Lecture Notes in Computer Science, Image Processing, Computer Vision, Pattern Recognition, and Graphics

Locul publicării:Berlin, Heidelberg, Germany

Public țintă

Research

Cuprins

Oral Session 1: Object Detection and Learning.- Beyond Dataset Bias: Multi-task Unaligned Shared Knowledge Transfer.- Cross-Database Transfer Learning via Learnable and Discriminant Error-Correcting Output Codes.- Human Reidentification with Transferred Metric Learning.- Poster Session 1: Object Detection, Learning and Matching.- Tell Me What You Like and I’ll Tell You What You Are: Discriminating Visual Preferences on Flickr Data.- Local Context Priors for Object Proposal Generation.- Arbitrary-Shape Object Localization Using Adaptive Image Grids.- Disambiguation in Unknown Object Detection by Integrating Image and Speech Recognition Confidences.- Class-Specific Weighted Dominant Orientation Templates for Object Detection.- Salient Object Detection via Color Contrast and Color Distribution.- Data Decomposition and Spatial Mixture Modeling for Part Based Model.- Appearance Sharing for Collective Human Pose Estimation.- Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching.- Coupling-and-Decoupling: A Hierarchical Model for Occlusion-Free Car Detection.- The Pooled NBNN Kernel: Beyond Image-to-Class and Image-to-Image.- Local Hypersphere Coding Based on Edges between Visual Words.- Spatially Local Coding for Object Recognition.- Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach.- Semi-Supervised Learning on a Budget: Scaling Up to Large Datasets.- One-Class Multiple Instance Learning via Robust PCA for Common Object Discovery.- Online Semi-Supervised Discriminative Dictionary Learning for SparseRepresentation.- Efficient Discriminative Learning of Class Hierarchy for Many Class Prediction.- Oral Session 2: Object Recognition I.- Grouping Active Contour Fragments for Object Recognition.- Detecting Partially Occluded Objects with an Implicit Shape Model Random Field.- Relative Forest for Attribute Prediction.- Discriminative Dictionary Learning with Pairwise Constraints.- PosterSession 2: Feature, Representation, and Recognition.- Adaptive Unsupervised Multi-view Feature Selection for Visual Concept Recognition.- Iris Recognition Using Consistent Corner Optical Flow.- Face Recognition in Videos – A Graph Based Modified Kernel Discriminant Analysis.- Learning Hierarchical Bag of Words Using Naive Bayes Clustering.- Efficient Human Parsing Based on Sketch Representation.- Exclusive Visual Descriptor Quantization.- Underwater Live Fish Recognition Using a Balance-GuaranteedOptimized Tree.- Local 3D Symmetry for Visual Saliency in 2.5D Point Clouds.- Exploiting Features – Locally Interleaved Sequential Alignment forObject Detection.- Efficient and Scalable 4th-Order Match Propagation.- Hierarchical Object Representations for Visual Recognition via WeaklySupervised Learning.- Invariant Surface-Based Shape Descriptor for Dynamic Surface Encoding.- Linear Discriminant Analysis with Maximum Correntropy Criterion.- AfNet: The Affordance Network.- A Directed Graphical Model for Linear Barcode Scanning from Blurred Images.- A Probabilistic 3D Model Retrieval System Using Sphere Image.- Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes.- Boosting with Side Information.- Generalized Mutual Subspace Based Methods for Image Set Classification.- Oral Session 3: Segmentation and Grouping Simultaneous Monocular 2D Segmentation, 3D Pose Recovery and 3D Reconstruction.- Joint Kernel Learning for Supervised Image Segmentation.- Application of Heterogenous Motion Models towards Structure Recovery from Motion.- Poster Session 3: Segmentation, Grouping, and Classification Locality-Constrained Active Appearance Model.- Modeling Hidden Topics with Dual Local Consistency for Image Analysis.- Design of Non-Linear Discriminative Dictionaries for Image Classification.- Efficient Background Subtraction under Abrupt Illumination Variations.- Naive Bayes Image Classification: Beyond Nearest Neighbors.-Contextual Pooling in Image Classification.- Spatial Graph for Image Classification.- Knowledge Leverage from Contours to Bounding Boxes: A Concise Approach to Annotation.- Efficient Pixel-Grouping Based on Dempster’s Theory of Evidence for Image Segmentation.- Video Segmentation with Superpixels.- A Noise Tolerant Watershed Transformation with Viscous Force for Seeded Image Segmentation.- Active Learning for Interactive Segmentation with Expected ConfidenceChange.- Cross Anisotropic Cost Volume Filtering for Segmentation.

Caracteristici

Up-to-date results in computer vision Fast-track conference proceedings State-of-the-art research