Introduces efficient pipeline architectures for the recursive morphological operations. The standard morphological operation is applied directly on the original input image and produces an output image. The order of image scanning in which the operator is applied to the input pixels is irrelevant. However, the intent of the recursive morphological operations is to feed back the output at the current scanning pixel to overwrite its corresponding input pixel to be considered into computation at the following scanning pixels. The resultant output image by recursive morphology inherently depends on the image scanning sequence. Two pipelined implementations of the recursive morphological operations are presented. The design of an application-specific systolic array is first introduced. The systolic array uses 3 n cells to process an nxn image in 6 n-2 cycles. The cell utilization rate is 100%. Second, a parallel program implementing the recursive morphological operations and running on distributed-memory multicomputers is described. Performance of the program can be finely tuned by choosing appropriate partition parameters.

The properties of soft morphological operations and the new definitions of binary soft morphological operations are presented. It is shown that soft morphological filtering on an arbitrary signal is equivalent to decomposing the signal into binary signals, filtering each binary signal with a binary soft morphological filter, and then reversing the decomposition. This equivalence allows problems in the analysis and the implementation of soft morphological operations in real time by using only logic gates for binary signals instead of sorting numbers.>

A skeletonization algorithm based on the Euclidean distance function using the sequential maxima-tracking method is described which, when applied to a connected image, generates a connected skeleton composed of simple digital arcs. With a slight modification, the algorithm can preserve the more important features in the skeletal branches which touch the object boundary at corners. Therefore its application to shape recognition can be easily achieved.>

A novel medial axis transformation (MAT) algorithm extracted from the Euclidean distance transform of a binary image is presented. The extracted MAT satisfies the following properties: reconstructivity, rotation-invariance, connectivity, and single-pixel width. The preservation of properties is proved, and some experimental results are shown. The skeleton is trimmed by removing short branches to make it simpler and useful for object recognition.>