EigenHistograms: Using Low Dimensional Models of Color Distribution for Real Time Object Recognition

Authors:
Jordi Vitrià;Petia Radeva;Xavier Binefa
Affiliations:
-;-;-
Venue:
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Year:
1999

Citing 5
Cited 0

Object recognition by computer: the role of geometric constraints

Object recognition by computer: the role of geometric constraints
Color indexing

International Journal of Computer Vision
Color Constant Color Indexing

IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Based Object Recognition

ICIAP '97 Proceedings of the 9th International Conference on Image Analysis and Processing-Volume I - Volume I
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

Distribution of object colors has been used in computer vision for recognition and indexing. Most of the recent approaches to this problem have been focused on defining optimal spaces for representing pixel values that are related to physical models and that present some invariance. We propose a new approach to identify individual object color distributions by using statistical learning techniques and to allow their compact representation in low dimensional spaces. This approach out-performs generic "optimal" spaces when color illumination is constant, allowing changes in object pose and illumination direction. This approach has been tested for real time industrial inspection of multicolored objects.