Proceedings PaperInfrared image segmentation through iterative thresholding
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A real time segmentation algorithm for infrared sequences is presented, based on the iterative application of thermal histogram processing and thresholding. The segmentation algorithm is intrinsically not sequential and may therefore be decomposed into a graph of concurrent processing tasks well suited to a parallel implementation. The algorithm is based on the assumption that two pixel populations are present, namely object and background. Since in this hypothesis the separation goodness function should have one single maximum, the algorithm forces this behaviour by redistributing the area of the histogram, i.e. by progressively brightening the target population. An initialization phase finds out potential target areas throughout the current frame in cooperation with a temporal tracking and labeling task, and compiles the search windows set. For each search window the thermal histogram is computed. Then a family of modified histograms is obtained by removing greater and greater areas from the hot tail of the original histogram and by replacing it with an impulse of the same area at the highest extreme of the thermal range. Each of these histograms enters a module which computes a separation goodness function. The separation function presents two adjacent segments: the high thermal segment and the low thermal segment. The former is a constant segment which lasts from the high extreme of the thermal range down to the lowest thermal value of the removed area, the latter has a variable shape and lasts as far as the lowest extreme of the thermal range. The iteration is stopped as soon as the variable segment reaches a monotonically decreasing behaviour. The boundary value between the two segments is chosen as threshold within that window. Experimental results are presented.