New method for labeling objects based on convolution
Kent P. Qing, Robert W. Means · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
Labeling objects in an image is an important step in many application areas such as target tracking, circuit board and IC mask inspection, medical image analysis, environmental analysis, and character recognition. However, in a real time system the speed of the labeling operation is often hindered by bottlenecks. Our new, fast method to perform labeling first obtains the connectivity information for each pixel in the entire image by means of convolution. Then, it uses the connectivity information to assign a temporary label and generate a small equivalence table. Finally, the algorithm uses the equivalence table to obtain the result. The advantage of this algorithm is its ability to exploit high-speed convolutional processors such as HNC's Vision Processor (ViP). Using the ViP and HNC's Balboa 860 coprocessor board, it takes between 36 and 66 milliseconds for most 512 X 512 images of interest (the time taken for the second step is dependent on image content). This type of fast algorithm running in processors such as the ViP, will yield a new wave in imaging processing algorithm development.