A Knowledge Based Approach for Image Understanding

1988 ◽  
pp. 137-145
Author(s):  
S. Losito ◽  
G. Pasquariello ◽  
G. Sylos-Labini ◽  
A. Tavoletti
Keyword(s):  
Image Understanding ◽  
Knowledge Based

USING EXPERT SYSTEMS FOR IMAGE UNDERSTANDING

1987 ◽  
Vol 01 (02) ◽  
pp. 237-250 ◽  
Author(s):  
P. SUETENS ◽  
A. OOSTERLINCK
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Review Paper ◽  
Image Understanding ◽  
Interested Reader ◽  
Knowledge Based ◽  
Survey Papers ◽  
Application Fields

Expert systems and image understanding have traditionally been considered as two separate application fields of artificial intelligence (AI). In this paper it is shown, however, that the idea of building an expert system for image understanding may be fruitful. Although this paper may serve as a framework for situating existing works on knowledge-based vision, it is not a review paper. The interested reader will therefore be referred to some recommended survey papers in the literature.

Knowledge-Based Image Understanding Systems: A Survey

1997 ◽  
Vol 67 (2) ◽  
pp. 161-185 ◽  
Author(s):  
Daniel Crevier ◽  
Richard Lepage
Keyword(s):  
Image Understanding ◽  
Knowledge Based

Knowledge based interpretation of images: a biomedical perspective

1987 ◽  
Vol 2 (4) ◽  
pp. 249-264 ◽  
Author(s):  
Nicholas Walker ◽  
John Fox
Keyword(s):  
Image Processing ◽  
Knowledge Engineering ◽  
Image Understanding ◽  
Imaging Systems ◽  
Symbolic Form ◽  
Biomedical Images ◽  
Knowledge Based ◽  
General Relevance ◽  
Shape Structure ◽  
Pixel Array

AbstractThe traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

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