Abstract | ||
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This paper formally analyzes the image understanding (IU) task at the knowledge level and in the observer domain. The analysis is done at three levels: task, method and domain knowledge, distinguishing the generic components in most of the IU tasks, thereby enabling the components to be reused. We model the IU problem as a design task and define a generic problem solving method (PSM) that allows us to tackle the task in a hierarchical and recursive way. The main advantage of this generic PSM is the possibility of instantiating specific PSMs through parameter space configuration, which enables the structure to be reused both across the task decomposition at different hierarchical levels and across different application domains. This work has been done following the well-established foundations of knowledge engineering that prescribe the maintenance of the conceptual structure from the modeling stage at the knowledge level down to the implementation. Finally, we apply the proposed framework to the problem of identifying the papilla in eye fundus images in order to exemplify the successive stages in the modeling process and system design, and accordingly justify the framework's validity. |
Year | DOI | Venue |
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2005 | 10.1016/j.eswa.2005.01.020 | Expert Syst. Appl. |
Keywords | Field | DocType |
knowledge modeling,generic component,medical image analysis,optic nerve head,knowledge level,image understanding task,design task,task decomposition,eye fundus,knowledge engineering,generic problem,iu problem,domain knowledge,generic psm,image understanding,iu task,system design,parameter space | Conceptual structure,Knowledge level,Domain knowledge,Computer science,Systems design,Artificial intelligence,Knowledge engineering,Observer (quantum physics),Machine learning,Knowledge modeling,Recursion | Journal |
Volume | Issue | ISSN |
29 | 1 | Expert Systems With Applications |
Citations | PageRank | References |
5 | 0.52 | 18 |
Authors | ||
3 |
Name | Order | Citations | PageRank |
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mariano rincon | 1 | 45 | 5.48 |
M. Bachiller | 2 | 28 | 3.05 |
José Mira | 3 | 124 | 9.22 |