Segmentation of farmland obstacle images based on intuitionistic fuzzy divergence

Abstract

In order to solve the problems of poor universality, auxiliary algorithm complexity and great limitation in general segmentation algorithms, a new segmentation algorithm for farmland obstacle images using an intuitionistic fuzzy divergence based on threshold techniques was proposed. The original th ree-dimensional color image was converted to (Z-Y) chromatic aberration grayscale image on XYZ color space as the input images. While using intuitionistic fuzzy divergence, a modified Wu’s membership function and Sugeno’s intuitionistic fuzzy generator were used to find the membership and non-membership functions respectively. A new exponential intuitionistic fuzzy divergence based entropy formulas has been proposed and the optimum threshold value has been obtained by minimizing intuitionistic fuzzy divergence. The experimental results indicated that the proposed algorithm could clearly detect all types of obstacles and overcome the influence of unstructured environments well such as uneven illumination, shadow, weather and so on. The results inspire us explore further applications of intuitionistic fuzzy sets in the segmented images that contain a high degree of uncertain information. Furthermore, this proposed method can be used for the agricultural robots vision navigation accurately.