Computer Vision – ACCV 2012 [E-Book] : 11th Asian Conference on Computer Vision, Daejeon, Korea, November 5-9, 2012, Revised Selected Papers, Part I / edited by Kyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu.
Lee, Kyoung Mu.
Matsushita, Yasuyuki. / Rehg, James M. / Hu, Zhanyi.
Berlin, Heidelberg : Springer, 2013
XLII, 821 p. 349 illus. digital.
englisch
Printed edition: 9783642373305
9783642373312
10.1007/978-3-642-37331-2
Lecture notes in computer science ; 7724
Full Text
Table of Contents:
  • 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 Sparse Representation
  • 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
  • Poster Session 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-Guaranteed Optimized Tree
  • Local 3D Symmetry for Visual Saliency in 2.5D Point Clouds
  • Exploiting Features – Locally Interleaved Sequential Alignment for Object Detection
  • Efficient and Scalable 4th-Order Match Propagation
  • Hierarchical Object Representations for Visual Recognition via Weakly Supervised 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 Confidence Change
  • Cross Anisotropic Cost Volume Filtering for Segmentation.