<title>Adaptive classification for image segmentation and target recognition</title>
Author(s) -
B. Bargel,
Karl-Heinz Bers,
Klaus Jaeger,
Gabriele Schwan
Publication year - 2002
Publication title -
proceedings of spie, the international society for optical engineering/proceedings of spie
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
eISSN - 1996-756X
pISSN - 0277-786X
DOI - 10.1117/12.477031
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , image segmentation , computer vision , segmentation , multispectral image , classifier (uml) , contextual image classification , image texture , image (mathematics)
This paper on adaptive image segmentation and classification describes research activities on statistical pattern recognition in combination with methods of object recognition by geometric matching of model and image structures. In addition, aspects of sensor fusion for airborne application systems like terminal missile guidance were considered using image sequences of multispectral data from real sensor systems and from computer simulations. The main aspect of the adaptive classification is the support of model-based structural image analysis by detection of image segments representing specific objects, e.g. forests, rivers and urban areas. The classifier, based on textural features, is automatically adapted to the changes of textural signatures during target approach by interpretation of the segmentation results of each actual frame of the image sequence
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