Introductory remarks

One of the most interesting problems in image processing and computer vision is the detection of specific patterns or objects. The dimensionality of the problem is related to the primary needs of the experiment. For example, the problem is referred to as two-dimensional object detection in satellite picture processing problems and related areas. In this case, what is available is merely a projection of the object on a two-dimensional plane. Three-dimensional object detection relates to the case where multiple projections of the object are available, and one has to make a decision regardless of the viewing position. Examples of the latter class of problems are active vision problems such as those encountered in robotics, where the robot hand is to be directed to specific locations depending on the presence of targeted objects that are actively sought by imaging sensors, such as on-board cameras in the case of a moving robot. The shape of the object is presumably known and stored in a reference database.

The primary intent of such procedures for the detection of two-dimensional objects that are embedded in a noisy environment is a satisfactory performance over a wide range of noise conditions, in contrast to traditional methods that rely on concepts such as the matched filter.