Comparative Analysis of Sobel, Prewitt, and Canny Methods in Detecting Object Edges in Betta Fish Images

Alfin Alfarizi; Cici El Dirrah Syafitri Simanungkalit; Fahmi Nur Alimsyah Purba; Lailan Sofinah Harahap

doi:10.59934/jaiea.v5i3.2293

Authors

Alfin Alfarizi Universitas Islam Negeri Sumatera Utara
Cici El Dirrah Syafitri Simanungkalit Universitas Islam Negeri Sumatera Utara
Fahmi Nur Alimsyah Purba Universitas Islam Negeri Sumatera Utara
Lailan Sofinah Harahap Universitas Islam Negeri Sumatera Utara

DOI:

https://doi.org/10.59934/jaiea.v5i3.2293

Keywords:

Image Processing, Edge Detection, Sobel, Prewitt, Canny, Betta Fish

Abstract

Edge detection is a crucial stage in digital image processing for recognizing the shape and structure of an object. The application of edge detection to betta fish images presents a unique challenge due to their layered, intricately textured, and often semi-transparent fin morphology. This study aims to analyze and compare the performance of three edge detection algorithms, namely Sobel, Prewitt, and Canny, in extracting shape features from betta fish images. The research methodology involved converting the dataset images into a grayscale format and subsequently implementing the three algorithms using the OpenCV library in the Python programming language. The evaluation was conducted visually by observing the sharpness of the edge lines, object continuity, and the occurrence of noise. The results indicate that the Canny algorithm provides the most optimal performance, as it is capable of detecting the thin edge lines of the fish fins with greater detail and continuity due to its hysteresis thresholding process. Meanwhile, the Sobel and Prewitt methods produced thicker edge lines but were less sensitive to the details of the transparent fins. This study is expected to serve as a reference in selecting the appropriate segmentation method for biological objects with complex morphologies.

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