Method for segmentation of overlapping fish images in aquaculture - Zhou, Chao; Lin, Kai; Xu, Daming; Liu, Jintao; Zhang, Song; Sun, Chuanheng; Yang, Xinting (International Journal of Agricultural and Biological Engineering, 2019)
Individual fish segmentation is a prerequisite for feature extraction and object identification in any machine vision system. In this paper, a method for segmentation of overlapping fish images in aquaculture was proposed. First, the shape factor was used to determine whether an overlap exists in the picture. Then, the corner points were extracted using the curvature scale space algorithm, and the skeleton obtained by the improved Zhang-Suen thinning algorithm. Finally, intersecting points were obtained, and the overlapped region was segmented. The results show that the average error rate and average segmentation efficiency of this method was 10% and 90%, respectively. Compared with the traditional watershed method, the separation point is accurate, and the segmentation accuracy is high. Thus, the proposed method achieves better performance in segmentation accuracy and effectiveness. This method can be applied to multi-target segmentation and fish behavior analysis systems, and it can effectively improve recognition precision. Keywords: aquaculture, image processing, overlapping segmentation, corner detection, improved Zhang-Suen algorithm DOI: 10.25165/j.ijabe.20191206.3217 Citation: Zhou C, Lin K, Xu D M, Liu J T, Zhang S, Sun C H, et al. Method for segmentation of overlapping fish images in aquaculture. Int J Agric & Biol Eng, 2019; 12(6): 135–142.