Computer Vision - ACCV 2012 Workshops ACCV 2012 International Workshops, Daejeon, Korea, November 5-6, 2012, Revised Selected Papers, Part I / [electronic resource] :
edited by Jong-Il Park, Junmo Kim.
- Berlin, Heidelberg : Springer Berlin Heidelberg, 2013.
- 1 online resource (XXXIV, 335 p. 173 ill.)
- Lecture Notes in Computer Science, 7728 0302-9743 ; .
Binary Patterns -- Rotation Invariant Co-occurrence among Adjacent LBPs -- 3D LBP-Based Rotationally Invariant Region Description -- Dynamic Texture Synthesis in Space with a Spatio-temporal Descriptor -- Adaptive Kernel Size Selection for Correntropy Based Metric -- Vitality Assessment of Boar Sperm Using an Adaptive LBP Based on Oriented Deviation -- Background Subtraction Based on Multi-channel SILTP -- Elliptical Local Binary Patterns for Face Recognition -- Block LBP Displacement Based Local Matching Approach for Human Face Recognition -- Face Recognition with Learned Local Curvelet Patterns and 2-Directional L1-Norm Based 2DPCA -- LBP-TOP Based Countermeasure against Face Spoofing Attacks -- An Efficient LBP-Based Descriptor for Facial Depth Images Applied to Gender Recognition Using RGB-D Face Data -- Face Spoofing Detection Using Dynamic Texture -- Class-Specified Segmentation with Multi-scale Superpixels -- A Flexible Auto White Balance Based on Histogram Overlap -- Region Segmentation and Object Extraction Based on Virtual Edge and Global Features -- Adaptive Sampling for Low Latency Vision Processing -- Colorimetric Correction for Stereoscopic Camera Arrays -- Camera Calibration Using Vertical Lines -- Vehicle Localization Using Omnidirectional Camera with GPS Supporting in Wide Urban Area -- Efficient Development of User-Defined Image Recognition Systems -- Transforming Cluster-Based Segmentation for Use in OpenVL by Mainstream Developers -- Efficient GPU Implementation of the Integral Histogram -- Play Estimation with Motions and Textures in Space-Time Map Description -- A Benchmark Dataset for Outdoor Foreground/Background Extraction -- One-Class Background Model -- Illumination Invariant Background Model Using Mixture of Gaussians and SURF Features -- Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint -- Temporal Saliency for Fast Motion Detection -- Background Model Based on Statistical Local Difference Pattern.