1994 Fiscal Year Final Research Report Summary
Constraint Satisfying Image Processing Using Top-down Information
| Project/Area Number |
05680296
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
Intelligent informatics
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| Research Institution | KYOTO UNIVERSITY |
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Principal Investigator |
MINOH Michihiko Kyoto University, Integ.Media Env.Exp.Lab., Assoc.Pro, 工学部, 助教授 (70166099)
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| Co-Investigator(Kenkyū-buntansha) |
HIROSE Shouichi Information Science, Inst., 工学部, 助手 (20228836)
AMANO Akira Information Science, Inst., 工学部, 助手 (60252491)
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| Project Period (FY) |
1993 – 1994
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| Keywords | Top-down Information / Constraint Satisfaction / Image Processing / Interface |
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| Research Abstract |
In order to recognize an image, we need knowledge processing based on constructive inference, because there are not enough information for image recognition in an image. Top-down process, which is used to verify candidates, were recognized as control system which controls the processing area of images or parameters of image processing algorithm. In this research, we propose the concept of "Constraint Satisfying Image Processing" which is generalization of image processing algorithm which uses top-down information, and we implemented this concept.
1. In the former image recognition system, knowledge processing like object hypothesis generation, were applied to the results of bottom-up image processing. However, this approach is something like "smoothing over vague results" and this is the main fact that the image recognition system can not be used practically. Therefore, we constructed the concept of "Constraint Satisfying Image Processing" which gives the information of what kind of object is needed, or what kind of result is needed, to the image processing system.
2. We discussed about the representation of constraints in order to realize "Constraint Satisfying Image Processing". As a result, we realized that the image processing can be controlled by the constraints of feature value level representation.
3. Active Contour Model (snakes) is a contour detection method which is an application of regularization method that is based on energy minimization principle. We developped the mechanism which controls the snakes to detect needed contour by providing information which represents what kind of contour must be detected.
4. We have done the experience of controlling the process by belief factor of constraints in order to realize "Constraint Satisfying Image Processing" in various situation.
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Research Products
(12 results)
