Computer Vision -- ACCV 2012 : 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.

Format:

Book

Contributor:

Lee, Kyoung Mu, Editor.

Matsushita, Yasuyuki, Editor.

Rehg, James M., Editor.

Hu, Zhanyi, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 7724

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 7724

Language:

English

Subjects (All):

Computer vision.

Pattern recognition systems.

Artificial intelligence.

Medical informatics.

Computer Vision.

Automated Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Local Subjects:

Computer Vision.

Automated Pattern Recognition.

Artificial Intelligence.

Health Informatics.

Physical Description:

1 online resource (XLII, 821 pages) : 349 illustrations

Edition:

1st ed. 2013.

Contained In:

Springer Nature eBook

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.

System Details:

text file PDF

Summary:

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.

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.

Other Format:

Printed edition:

ISBN:

978-3-642-37331-2

9783642373312

Access Restriction:

Restricted for use by site license.