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ICMCS
2009
IEEE
415views Multimedia» more  ICMCS 2009»
13 years 6 months ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel label...
Xiaofeng Wang, Xiao-Ping Zhang
PAMI
2011
13 years 3 months ago
Multiple Kernel Learning for Dimensionality Reduction
—In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. The resulting ...
Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
CVPR
2008
IEEE
14 years 10 months ago
Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation
In background subtraction, cast shadows induce silhouette distortions and object fusions hindering performance of high level algorithms in scene monitoring. We introduce a nonpara...
André Zaccarin, Nicolas Martel-Brisson
ICANN
2009
Springer
14 years 1 months ago
Switching Hidden Markov Models for Learning of Motion Patterns in Videos
Abstract. Building on the current understanding of neural architecture of the visual cortex, we present a graphical model for learning and classification of motion patterns in vid...
Matthias Höffken, Daniel Oberhoff, Marina Kol...
ICCV
2011
IEEE
12 years 8 months ago
A Linear Subspace Learning Approach via Sparse Coding
Linear subspace learning (LSL) is a popular approach to image recognition and it aims to reveal the essential features of high dimensional data, e.g., facial images, in a lower di...
Lei Zhang, Pengfei Zhu, Qinghu Hu, David Zhang