Automatic extraction of contour features of fish morphology based on computer vision

With the rapid development of artificial intelligence, modern fish biology research technology has been constantly updated. Automated and intelligent fish identification will help to promote the development of modern fish biology research. The contour feature of fish morphology is one of the important features of fish recognition. Fish morphology is diverse, and the contour features of fish morphology have species specificity, and it serves as an important scientific basis for fish identification and classification. The extraction effect of morphological contour features directly affects the accuracy of automatic fish identification. Therefore, in order to study the effect of computer vision on the automatic extraction of fish morphological contour features, a two-dimensional image of 1 T. obesus was collected in the Pacific Ocean from September to November 2017 for computer vision analysis. Through the fish image gray level transformation, bilateral filter, binary image processing and contour extraction and other image processing. 8 - direction chain code technology was used to automatically extract the chain code information of fish contour. The morphological information coefficient was calculated by elliptic Fourier transform and the contour reconstruction was carried out. The results revealed that the contour image of tuna can be obtained well after processing, and the chain code information changes with the size of the contour pixel of fish shape, and the contour reconstruction of fish shape changes with the change of harmonic number. Research has shown that automatic extraction of fish contour features was effective. The morphological coefficients of fish fluctuated greatly in low harmonic number and little in high harmonic number. The low harmonic number transformation had a great influence on the overall contour information of fish, while the high harmonic number transformation had a great influence on the local contour information of fish. The results of this study lay a preliminary foundation for automatic fish recognition and classification, and also provide reference for other related automation research.