An improved Canny edge detector and its realization on FPGA

He, Wenhao; Yuan, Kui

doi:10.1109/wcica.2008.4594570

Cited by 26 publications

(2 citation statements)

References 8 publications

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“…Langkah kedua adalah menghitung besaran dan arah gradien gambar. Algoritma Canny tradisional mengadopsi perbedaan terbatas dari 2ͪ 2 area tetangga untuk dihitung nilai dan arah gradien citra [11]. Urutan pertama pendekatan turunan parsial pada arah x dan y dapat diperoleh dari rumus berikut ini:…”

Section: B2 Perhitungan Gradien Gambarunclassified

Penerapan Algoritma Deteksi Tepi Canny Menggunakan Python Dan Opencv

Saluky¹,

Marine²

2023

smart-techno

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Object detection is one step in object recognition in the field of computer vision. The edges of the image characterize the boundaries that distinguish it from other objects and are therefore a very important problem in image processing. Accurate Image Edge Detection can significantly reduce the amount of data and filter out useless information while retaining important structural properties in the image. Since edge detection is at the forefront of image processing for object detection, it is very important to have a good understanding of edge detection algorithms. In this study, applying canny edge detection using python and OpenCV and also compared with other image processing methods. The result is that Canny edge detection has a better performance compared to other algorithms such as LoG (Laplacian of Gaussian), Robert, Prewitt and Sobel.

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Section: B2 Perhitungan Gradien Gambarunclassified

Penerapan Algoritma Deteksi Tepi Canny Menggunakan Python Dan Opencv

Saluky¹,

Marine²

2023

smart-techno

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“…In [7], [8], constant threshold is used regardless of the image characteristics to reduce the computational complexity, where the performance of edge detection is relatively degraded compared with adaptive threshold approaches. He et al [9] present a pipelined implementation of an improved Canny edge detector with a self-adapt threshold mechanism. In [10], a parallel design of Canny implementation is proposed for high-throughput real-time edge detection.…”

Section: Introductionmentioning

confidence: 99%

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Xiao

Fan

Feng

et al. 2020

IEICE Trans. Inf. & Syst.

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Optical navigation (OPNAV) is the use of the on-board imaging data to provide a direct measurement of the image coordinates of the target as navigation information. Among the optical observables in deep-space, the edge of the celestial body is an important feature that can be utilized for locating the planet centroid. However, traditional edge detection algorithms like Canny algorithm cannot be applied directly for OPNAV due to the noise edges caused by surface markings. Moreover, due to the constrained computation and energy capacity on-board, lightweight image-processing algorithms with less computational complexity are desirable for real-time processing. Thus, to fast and accurately extract the edge of the celestial body from high-resolution satellite imageries, this paper presents an algorithm-hardware co-design of real-time edge detection for OPNAV. First, a lightweight edge detection algorithm is proposed to efficiently detect the edge of the celestial body while suppressing the noise edges caused by surface markings. Then, we further present an FPGA implementation of the proposed algorithm with an optimized real-time performance and resource efficiency. Experimental results show that, compared with the traditional edge detection algorithms, our proposed one enables more accurate celestial body edge detection, while simplifying the hardware implementation.

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Signal Processing

2014

Machinery Prognostics and Prognosis Oriented Maintenance Management

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An improved Canny edge detector and its realization on FPGA

Cited by 26 publications

References 8 publications

Penerapan Algoritma Deteksi Tepi Canny Menggunakan Python Dan Opencv

Penerapan Algoritma Deteksi Tepi Canny Menggunakan Python Dan Opencv

Algorithm-Hardware Co-Design of Real-Time Edge Detection for Deep-Space Autonomous Optical Navigation

Signal Processing

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