Adaptive color segmentation-a comparison of neural and statistical methods

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 arbitrar...

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Bibliographic Details
Published inIEEE transactions on neural networks Vol. 8; no. 1; pp. 175 - 185
Main Authors Littmann, E., Ritter, H.
Format Journal Article
LanguageEnglish
Published United States IEEE 01.01.1997
Subjects
Availability
Cameras
Detectors
Gaussian distribution
Humans
Image recognition
Image segmentation
Neural networks
Object detection
Pixel
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Summary: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.
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ISSN:1045-9227
1941-0093
DOI:10.1109/72.554203