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» Learning Models for Object Recognition
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AAAI
2008
13 years 11 months ago
Unsupervised Categorization (Filtering) of Google Images Based on Visual Consistency
The objective of this paper is to study the existing methods for unsupervised object recognition and image categorization and propose a model that can learn directly from the outp...
Pooyan Fazli, Ara Bedrosian
CVPR
2010
IEEE
14 years 5 months ago
The Role of Features, Algorithms and Data in Visual Recognition
There are many computer vision algorithms developed for visual (scene and object) recognition. Some systems focus on involved learning algorithms, some leverage millions of trainin...
Devi Parikh and C. Lawrence Zitnick
AGI
2011
13 years 19 days ago
Generalization of Figure-Ground Segmentation from Binocular to Monocular Vision in an Embodied Biological Brain Model
Abstract. Humans have the remarkable ability to generalize from binocular to monocular figure-ground segmentation of complex scenes. This is clearly evident anytime we look at a p...
Brian Mingus, Trent Kriete, Seth A. Herd, Dean Wya...
ICPR
2006
IEEE
14 years 10 months ago
Object Localization Using Input/Output Recursive Neural Networks
Localizing objects in images is a difficult task and represents the first step to the solution of the object recognition problem. This paper presents a novel approach to the local...
Lorenzo Sarti, Marco Maggini, Monica Bianchini
BVAI
2007
Springer
14 years 3 months ago
Incremental Subspace Learning for Cognitive Visual Processes
In real life, visual learning is supposed to be a continuous process. Humans have an innate facility to recognize objects even under less-than-ideal conditions and to build robust ...
Bogdan Raducanu, Jordi Vitrià