Publisher Summary This chapter reviews the image modeling approaches for pictorial feature extraction and recognition. Research activities in computer-based picture processing have been centered around picture enhancement, interpretation, recognition, generation, and editing. Quantitative study of a picture is often concerned with four types of parameters that are of fundamental importance. They are contrast, color, shape, and texture. The contents of a picture may be categorized into three general types: (1) identifiable objects with well-defined structural patterns, (2) identifiable objects with fuzzy or diffused patterns, and, (3) non-identifiable objects. To process a picture with identifiable objects, the objects must be localized and their contrast, color, and shape for recognition must be studied. In the case of objects with fuzzy structures, identification of objects may require texture analysis. When pictures are processed with non-identifiable objects such as wood grain or forests, the study of the textures in the picture becomes indispensable. Under this circumstance, textural information provides important features for interpretation and recognition. Texture information thus is very useful in automatic photointerpretation, earth resources exploration, and biomedical image processing. Textures may be regarded as repetitive arrangements of a unit sub-pattern, as inhomogeneities in the gray scale, or as global properties of a picture or scene in a statistical sense.