Edges are important image features that serve as basis of follow-up measurement and shape registration. To achieve good edge information

an edge detection method of block distance combined with summed area table is proposed in this work. The major innovation points of this study are as follows. 1) The edge of an image is detected by local block distance. 2) The sum of block pixels is accelerated by an integral diagram

and the method for completing a Gauss template block with an integral map is modified to improve the execution efficiency. The pixel difference of each block is computed. The pixel difference values are then accumulated to detect the edge of an image. The principle of this method is as follows: 1) the difference accumulation for gray regions tends to be zero

and 2) the difference accumulation for edge regions is different. The neighborhood features are then considered in a small range by comparing the differences among all the pixels in the adjacent region to determine the gradient of the central position. The structure of the Gauss template is analyzed

and the algorithm execution efficiency is improved by introducing an integral diagram. The size of the Gauss template is determined according to the rectangular area by using the integral diagram. The elements of the integral diagram

which are all ones

are constructed on the basis of the size of the Gauss template

whereas those

which are all zeros

are constructed on the basis of the size of the rectangular area. Then

the matrix of ones is used to achieve ergodicity for the matrix of zeros. Subsequently

the traversal times of each unit in the rectangular area after the completion of the traversal are obtained. A matrix of the traversal times could thus be formed and is then decomposed into the matrix of multiple ones until further decomposition is no longer possible. According to the matrix block of ones obtained after the decomposition

the pixels of the central points could be accumulated by the integral diagram. Finally

the overall distance value is remapped as the result of the edge. Unlike manual results

the results obtained with the proposed method indicate an overlap rate that is higher than 97%

which is greater than the overlap rate of the Canny edge detection algorithm (less than 80%)

as well as the Gauss Manhattan distance and Euclidean distance (63% and 28%

respectively). Experiments on real images reveal that the execution times of the proposed method and the Canny edge detection algorithm slightly increase with image size

as indicated by their execution times of 1.7 and 4.6 s

respectively

for an image size of 1 024 × 768. As the proposed method increases the integral chart and block solution

it achieves a longer execution time than the Canny algorithm. As for the Gauss Manhattan distance and Euclidean distance

their execution times are longer than those of the two previous algorithms. The experiments on simulated and real images prove that the proposed method achieves higher accuracy in edge extraction than other algorithms. This method is also highly practical

as it maintains a relatively short processing time as image size increases.