Model-based detection,segmentation, and classification for image analysis using on-line shape learning |
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Authors: | Kyoung-Mi Lee W Nick Street |
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Affiliation: | (1) Center for Artificial Vision Research, Korea University, Seoul 136–701, Korea (e-mail: kmlee@image.korea.ac.kr) , KR;(2) Department of Managment Sciences, University of Iowa, Iowa City, IA 52242, USA (e-mail: nick-street@uiowa.edu) , US |
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Abstract: | Detection, segmentation, and classification of specific objects are the key building blocks of a computer vision system for
image analysis. This paper presents a unified model-based approach to these three tasks. It is based on using unsupervised
learning to find a set of templates specific to the objects being outlined by the user. The templates are formed by averaging
the shapes that belong to a particular cluster, and are used to guide a probabilistic search through the space of possible
objects. The main difference from previously reported methods is the use of on-line learning, ideal for highly repetitive
tasks. This results in faster and more accurate object detection, as system performance improves with continued use. Further,
the information gained through clustering and user feedback is used to classify the objects for problems in which shape is
relevant to the classification. The effectiveness of the resulting system is demonstrated in two applications: a medical diagnosis
task using cytological images, and a vehicle recognition task.
Received: 5 November 2000 / Accepted: 29 June 2001
Correspondence to: K.-M. Lee |
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Keywords: | :Detection – Segmentation – Incremental clustering – Classification – Unsupervised learning |
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