Many works in the field of image processing stress the utility of using Artificial Intelligence tools to obtain enhanced performances in the scene understanding task. Feigenbaum (1977) states that "..the power of an expert system derives from the knowledge it possesses not from the particular formalism and inference schemas it employs...". In our opinion this assertion can be extended to all complex cognitive problems (such as natural language and automatic image understanding) in which human reasoning capabilities are required. "From this point of view a theory for the vision problem resolution must necessarily contain elements of a more general theory of thinking (Minsky, 1974)". In order to translate these philosophical statements into action, we have to understand what kind of knowledge is useful for the vision problem resolution and how to represent it. Looking at the image understanding problem as a perception problem it is possible to individuate two different knowledge sources. The first one descends from the perceptual grouping laws of visible entities; the other derives directly from an explicit description of the objects to be recognized in the image describing a real scene. Normally these two knowledges are used in a hierarchical fashion with a special emphasis on one source at the expenses of the other. Moreover in many of these applications a low level processing attached to the image extracts features and a high level step tries to match the previously selected features with the model descriptions. A drawback of this approach is that an intelligent matching can be applied to an essentially non intelligent image partition.
A Knowledge Based Approach for Image Understanding
G Pasquariello;
1988
Abstract
Many works in the field of image processing stress the utility of using Artificial Intelligence tools to obtain enhanced performances in the scene understanding task. Feigenbaum (1977) states that "..the power of an expert system derives from the knowledge it possesses not from the particular formalism and inference schemas it employs...". In our opinion this assertion can be extended to all complex cognitive problems (such as natural language and automatic image understanding) in which human reasoning capabilities are required. "From this point of view a theory for the vision problem resolution must necessarily contain elements of a more general theory of thinking (Minsky, 1974)". In order to translate these philosophical statements into action, we have to understand what kind of knowledge is useful for the vision problem resolution and how to represent it. Looking at the image understanding problem as a perception problem it is possible to individuate two different knowledge sources. The first one descends from the perceptual grouping laws of visible entities; the other derives directly from an explicit description of the objects to be recognized in the image describing a real scene. Normally these two knowledges are used in a hierarchical fashion with a special emphasis on one source at the expenses of the other. Moreover in many of these applications a low level processing attached to the image extracts features and a high level step tries to match the previously selected features with the model descriptions. A drawback of this approach is that an intelligent matching can be applied to an essentially non intelligent image partition.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
