Adaptive color segmentation-a comparison of neural and statistical methods
- 1 January 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Neural Networks
- Vol. 8 (1) , 175-185
- https://doi.org/10.1109/72.554203
Abstract
With the availability of more powerful computers it is nowadays possible to perform pixel based operations on real camera images even in the full color space. New adaptive classification tools like neural networks make it possible to develop special-purpose object detectors that can segment arbitrary objects in real images with a complex distribution in the feature space after training with one or several previously labeled image(s). The paper focuses on a detailed comparison of a neural approach based on local linear maps (LLMs) to a classifier based on normal distributions. The proposed adaptive segmentation method uses local color information to estimate the membership probability in the object, respectively, background class. The method is applied to the recognition and localization of human hands in color camera images of complex laboratory scenes.Keywords
This publication has 10 references indexed in Scilit:
- Learning and Generalization in Cascade Network ArchitecturesNeural Computation, 1996
- Neural recognition of human pointing gestures in real imagesNeural Processing Letters, 1996
- Detection of Regions Matching Specified Chromatic FeaturesComputer Vision and Image Understanding, 1995
- Contour extraction of moving objects in complex outdoor scenesInternational Journal of Computer Vision, 1995
- A hybrid system to detect hand orientation in stereo imagesPublished by Elsevier ,1994
- A Resource-Allocating Network for Function InterpolationNeural Computation, 1991
- Regularization Algorithms for Learning That Are Equivalent to Multilayer NetworksScience, 1990
- Pattern Analysis and UnderstandingPublished by Springer Nature ,1990
- Color information for region segmentationComputer Graphics and Image Processing, 1980
- Picture segmentation using a recursive region splitting methodComputer Graphics and Image Processing, 1978