Research of Some Image Filter Algorithms Used in Object Detection

Edge detection is an important step in object detection that allows accurate object localization and identification of objects in an image. In this paper, we present a comprehensive explanation of some edge detection filters, namely Sobel, Prewitt, Canny, Laplacian of Gaussian (LoG), and Scharr filters. This paper discusses the operation of the above filters, their mathematical representations, and their applications in the real world in different fields like security, medical imaging, and self-driving cars. The article contrasts the benefits and limitations of using each filter, i.e., their sensitivity to noise, computational complexity, and precision. The article ends with suggestions for future work that can be done to improve edge detection and its integration into more efficient and resilient object detection systems.

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Publication Details

Published
2024-12-11
DOI
https://doi.org/10.1145/3726122.3726236
Primary Topic
Medical Image Segmentation Techniques
Type
article
Field-Weighted Citation Impact
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article

Research of Some Image Filter Algorithms Used in Object Detection

Javlonbek Saydazimov, Shakhboz Ergashev, Asadjon Nosirkulov
Medical Image Segmentation Techniques
article

Research of Some Image Filter Algorithms Used in Object Detection

Javlonbek Saydazimov, Shakhboz Ergashev, Asadjon Nosirkulov
article en

Abstract

Edge detection is an important step in object detection that allows accurate object localization and identification of objects in an image. In this paper, we present a comprehensive explanation of some edge detection filters, namely Sobel, Prewitt, Canny, Laplacian of Gaussian (LoG), and Scharr filters. This paper discusses the operation of the above filters, their mathematical representations, and their applications in the real world in different fields like security, medical imaging, and self-driving cars. The article contrasts the benefits and limitations of using each filter, i.e., their sensitivity to noise, computational complexity, and precision. The article ends with suggestions for future work that can be done to improve edge detection and its integration into more efficient and resilient object detection systems.

Tashkent University of Information Technology (UZ), Tashkent State University of Economics (UZ)
Openalex Percentile: Top 70%
Medical Image Segmentation Techniques
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