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» Explanation-Based Learning for Image Understanding
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ICIP
2007
IEEE
13 years 11 months ago
Unsupervised Nonlinear Manifold Learning
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables t...
Matthieu Brucher, Christian Heinrich, Fabrice Heit...
ICMCS
2006
IEEE
174views Multimedia» more  ICMCS 2006»
14 years 1 months ago
Web Image Mining Based on Modeling Concept-Sensitive Salient Regions
In this paper, we propose a probabilistic model for web image mining, which is based on concept-sensitive salient regions without human intervene. Our goal is to achieve a middle-...
Jing Liu, Qingshan Liu, Jinqiao Wang, Hanqing Lu, ...
ICCV
2009
IEEE
14 years 10 months ago
Image Saliency by Isocentric Curvedness and Color
In this paper we propose a novel computational method to infer visual saliency in images. The method is based on the idea that salient objects should have local characteristics tha...
Roberto Valenti
TEI
2010
ACM
132views Hardware» more  TEI 2010»
14 years 2 months ago
TessalTable: tile-based creation of patterns and images
In this paper we introduce the TessalTable, a collaborative play system for learning about tessellations and symmetry through augmented pattern blocks. Children use tiles to pick ...
Abel Allison, Sean Follmer, Hayes Raffle
IJCNN
2006
IEEE
14 years 1 months ago
P-SVM Variable Selection for Discovering Dependencies Between Genetic and Brain Imaging Data
— The joint analysis of genetic and brain imaging data is the key to understand the genetic underpinnings of brain dysfunctions in several psychiatric diseases known to have a st...
Johannes Mohr, Imke Puis, Jana Wrase, Sepp Hochrei...