Affiliations: [a]
College of Computer and Information Science, Chongqing Normal University, Chongqing, China
| [b]
Chongqing Center of Engineering Technology Research on Digital Agricultural Service, Chongqing, China
| [c]
The Master Station of Agricultural Technology Promotion, Chongqing Agricultural and Rural Committee, Chongqing, China
| [d]
The Center of Agricultural Technology Promotion, Agricultural and Rural Committee of Shapingba District, Chongqing, China
Abstract: Shadow detection is a significant preprocessing work that soil type is classified with machine vision. Thus, Density peak clustering based on histogram fitting(DPCHF) is proposed to segment soil image shadows. First, its clustering centers are adaptively obtained by constructing a new parameterless density formula and decision value measure. Then the Fourier series are drawn into it to approximate the gray histogram and a part of gray-levels are allocated by valley points of the histogram fitting curve. Finally, an optimization model is established to optimize the threshold of detecting the shadow in the soil image, and the remaining gray-levels are clustered by the threshold. The simulation results show that DPCHF is better than the contrast algorithm. The average brightness standard deviations of the shadow and non-shadow are respectively 20.9348 and 20.3081 with DPCHF. It can realize the adaptive shadow detection of soil images and there is not the “domino” error propagation in it.
Keywords: Shadow detection, density peak clustering, soil image, machine vision
