In order to perform higher-level analysis, a priori information must be incor- rated into the interpretation process. Furthermore, - belling an object is often only the ?rst step of the interpretation process.

These can p- ducereasonableresultsonsimpleimages, butthepresenceofnoise, occlusion, andstructuralcomplexity oftenleadstoerroneouslabelling. Early work on automated interpretation used low-level operations such as edge detection and region growing to label objects in images. Automated methods that promise fast and - jective image interpretation have therefore stirred up much interest and have become a signi?cant area of research activity.

Manual image interpretation is, however, a time-consuming, error-prone, and subjective process that often requires specialist knowledge. In medicine, for example, a radiologist looks at a medical image, interprets it, and tra- lates the data into a clinically useful form.

The goal of image interpretation is to convert raw image data into me- ingful information.

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