A method of edge detection based on improved canny algorithm for the lidar depth image

Jingzhong Xu; Youchuan Wan; Xubing Zhang

doi:10.1117/12.712923

28 October 2006 A method of edge detection based on improved canny algorithm for the lidar depth image

Jingzhong Xu, Youchuan Wan, Xubing Zhang

Proceedings Volume 6419, Geoinformatics 2006: Remotely Sensed Data and Information; 64190O (2006) https://doi.org/10.1117/12.712923
Event: Geoinformatics 2006: GNSS and Integrated Geospatial Applications, 2006, Wuhan, China

Abstract

Edge-detection of LIDAR depth-image is an important work for further image analysis. Based on the theory of Canny algorithm, this paper discusses insufficiencies of Canny operator and proposes an improved method. Instead of using Gaussian smoothing filter, the improved algorithm carries on the smoothing operation by an adaptive median filter for the characteristics of LIDAR depth-image. As a result, it can not only eliminate noises effectively but also protect unclear edges. Gradient computation and determination of edge points are also improved, gradient magnitudes of pixels are calculated with first-order derivatives within eight neighborhoods instead of four, and the precision of edge location is enhanced consequently. Considering the deficiency of uniform threshold for the whole image in Canny operator and its non-objectivity in determining threshold values, the improved algorithm divides image into a number of sub-images and detects edges with adaptive threshold values respectively. Therefore, edge points with low height values are protected and adaptation of the algorithm is also improved. Datasets from urban areas were selected to test this algorithm. The results show that the improved algorithm can make up for the disadvantages of canny algorithm, and can detect edges of LIDAR depth-images effectively.

Citation Download Citation

Jingzhong Xu, Youchuan Wan, and Xubing Zhang "A method of edge detection based on improved canny algorithm for the lidar depth image", Proc. SPIE 6419, Geoinformatics 2006: Remotely Sensed Data and Information, 64190O (28 October 2006); https://doi.org/10.1117/12.712923

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