The segmentation of well-contrasted objects is no problem and the success of several algorithms is proved by applications. But if the objects are poorly contrasted, it is difficult to find a threshold, which leads to a right object segmentation and, in many cases, (e.g., touching or overlapping objects) a threshold for the right segmentation of the image into isolated object regions does not exist. Some methods are presented which can help to overcome these problems. Global information and a priori knowledge are used for the selection of an optimum segmentation threshold (a threshold is selected independently for each object). An algorithm for the separation of conglomerates of convex objects is presented based on contour information (information about the shape of the objects). The main characteristics of this algorithm are: construction of a recursive convexity polygon, determination of fuzzy features for the description of possible parts of the conglomerate, and dynamic programming. Several applications demonstrate the use of further information about shape, grey value distribution, and topology.