An Edge Detection Accelerator Using Otsu Method Based on Iterative Operation 


Vol. 14,  No. 3, pp. 143-148, Mar.  2025
https://doi.org/10.3745/TKIPS.2025.14.3.143


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  Abstract

Edge detection is a key technology that significantly enhances the performance of vision-based systems by extracting object boundaries, and its importance is increasingly recognized in various real-time applications. To generate an edge image, selecting a threshold to distinguish between edge and non-edge regions is essential. The Otsu method is an effective technique for calculating the optimal threshold that divides an image into two classes. In particular, the Otsu method automatically selects a suitable threshold for each image, enabling class classification in environments where images change in real-time. However, implementing the Otsu method in hardware typically involves the use of logarithmic approximation techniques, w hich improve computational efficiency but increase hardw are resource consumption. This paper proposes an edge detection accelerator that enhances hardware resource efficiency by introducing a histogram-based iterative computation approach to the process of finding the maximum between-class variance in the Otsu method. Performance evaluation using 100 images from the ImageNet dataset revealed that the proposed method achieved an absolute error of 5.04 compared to the theoretical value in Otsu threshold calculation, and the binary classification using the computed threshold achieved an accuracy of 97.88%. Also, synthesis results show that the proposed hardware architecture utilizes 9,442 slice LUTs and 5,060 slice registers in the Otsu module. The proposed edge detection accelerator reduces hardware resource consumption while maintaining high computational accuracy compared to conventional logarithmic approximation techniques, making it suitable for various real-time image processing applications.

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[IEEE Style]

J. Kang, I. Jeong, S. Jo, B. Moon, "An Edge Detection Accelerator Using Otsu Method Based on Iterative Operation," The Transactions of the Korea Information Processing Society, vol. 14, no. 3, pp. 143-148, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.143.

[ACM Style]

Joowan Kang, Insu Jeong, Seungjun Jo, and Byungin Moon. 2025. An Edge Detection Accelerator Using Otsu Method Based on Iterative Operation. The Transactions of the Korea Information Processing Society, 14, 3, (2025), 143-148. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.143.

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