Recognition and Interpretation
Recognizing objects and interpreting the meaning of static or mobile configurations of objects is a problem which has to be solved in different applications, like medical diagnosis, autonomous mobile systems, or remote sensing. Any system for the recognition and interpretation of images, image sequences, or sensor signals in general implicitly or explicitly makes use of a priori knowledge about the origin and properties of the image, about the objects, scenes, and events visible in an image, about the requirements of the user or the application, and about actions and conclusions which may be infered due to the image content. In a model-based approach to image recognition and/or interpretation all or at least a significant amount of this a priori knowledge is represented explicitly in a model. The model may contain declarative knowledge, that is, knowledge about structural properties, and procedural knowledge, that is, procedures computing certain attributes of the structural components. In the most general case an algorithm is provided which computes an interpretation based on the input image and the availaole model. Basically, this algorithm specifies which procedural knowledge to activate and which intermediate results to use for further processing. In this sense the algorithm computes a processing strategy or controls the interpretation process and hence will be referred to as the control algorithm. Two approaches to modeling, that is, semantic models and statistical models are treated in Section 3.2 and Section 3.3, respectively.
KeywordsObject Recognition Markov Random Field Semantic Network Active Vision Segmentation Object
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