Journal article
Model-based detection, segmentation, and classification for image analysis using on-line shape learning
Machine vision and applications, Vol.13(4), pp.222-233
02/01/2003
DOI: 10.1007/s00138-002-0061-6
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.
Details
- Title: Subtitle
- Model-based detection, segmentation, and classification for image analysis using on-line shape learning
- Creators
- K M Lee - Korea UniversityW N Street - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Machine vision and applications, Vol.13(4), pp.222-233
- Publisher
- Springer Nature
- DOI
- 10.1007/s00138-002-0061-6
- ISSN
- 0932-8092
- eISSN
- 1432-1769
- Number of pages
- 12
- Language
- English
- Date published
- 02/01/2003
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380498102771
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