[1] It has applications in computer vision, and it is commonly used in visual correspondence problems such as optical flow calculation and disparity estimation.
Similarity between images is determined by comparing the values of the census transform for corresponding pixels, using the Hamming distance.
[3] Several variations of the algorithm exist, using different size of the window, order of the neighbours in the pattern (row-wise, clockwise, counterclockwise), comparison operator (greater, greater or equal, lesser, lesser or equal).
[4] An extension of the algorithm uses a three-way comparison that allows to represent similar pixels, whose intensity difference is smaller than a tolerance parameter
, defined as[5] whose result can be encoded with two bits for each neighbour, thus doubling the size of the pattern for each pixel.