An Enhanced Adaptive Sobel Edge Detector Based on Improved Genetic Algorithm and Non-Maximum Suppression

Chen, ShuKang; Xu, Yang

doi:10.1109/cac53003.2021.9727626

Cited by 7 publications

(5 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The central lines show good coincidence with the original edges, except some disconnection at the corners and the intersections, which is negligible in pattern recognition based on the structure feature. In order to have a deeper understanding of the performance of this algorithm, the thinning algorithm in this paper is tested on Lena's portrait and indoor bowling dataset, and compared with MMA [16], NMS [19], and a parallel thinning algorithm (FTPA) [21]. Five quantitative indicators are used to evaluate the performance of the thinning algorithms, including the number of edges, number of pixels with the longest edge (NLE), average number of pixels of all edges (ANAE), total number of pixels (TNP), and the running time.…”

Section: Resultsmentioning

confidence: 99%

“…Different logic rules produce different distortions. In the nonmaximum suppression (NMS) edge thinning method, an edge point is deleted if its magnitude is less than that of the points marked in the selected mask [19]. Amos Sironi et al [20] extract the centrelines of tubular structures in 2D images by reformulating centreline detection in terms of a regression problem.…”

Section: Introductionmentioning

confidence: 99%

“…Different logic rules produce different distortions. In the non‐maximum suppression (NMS) edge thinning method, an edge point is deleted if its magnitude is less than that of the points marked in the selected mask [19]. Amos Sironi et al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An edge thinning algorithm based on newly defined single‐pixel edge patterns

Ren

Wang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

To improve the uniformity of one‐pixel width and continuity of the thinned edges, this paper proposes an edge thinning algorithm acting on grey‐scale edge images based on 24 self‐defined single‐pixel connection patterns. First, for binary or blurred grey‐scale gradient edge images, a distance–greyscale coupling algorithm is proposed to achieve gradient enhancement in the edge width direction. Then the elimination rules of noise points and gradient calculation method are given. Secondly, the marking rules of the first three pixels of each edge are given. The next pixel to be marked must meet that the new last three pixels belong to the 24 connection modes. Whether the qualified pixels are retained depends on its grey value and the local edge gradient. The algorithm is tested on four types of images. The results show that the proposed method can guarantee uniform, smooth, and connected one‐pixel‐wide lines that lie at the centre of the initial edges. The algorithm and the existing algorithms are performed on portrait image and four scenarios of the indoor datasets. Five evaluation indicators are statistically analyzed to prove the feasibility and effectiveness of the proposed algorithm.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An edge thinning algorithm based on newly defined single‐pixel edge patterns

Ren

Wang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…Among these, the Sobel operator stands out due to its stronger noise resistance and more precise edge localization compared to other differential operators. However, the detection results using Sobel are still intermittent, and it cannot achieve adaptive thresholding [4][5][6][7] .…”

Section: Introductionmentioning

confidence: 99%

“…The adaptive local threshold and G are synchronously input to the data comparator, and the edge information can be output after processing. The calculation formulas are as follows: (4), ( 5) , (6). FPGA has good parallelism and computing power, and has unique advantages in complex logic control and large-scale data calculation and processing.…”

mentioning

confidence: 99%

P‐6.16: Design of Hardware Accelerator for Real‐Time Edge Detection System Based on FPGA

Yuan,

Hu,

et al. 2024

Symp Digest of Tech Papers

View full text Add to dashboard Cite

Edge detection is an important research field in image processing and computer vision, especially in feature extraction. The detection efficiency of the traditional Sobel edge detection algorithm is reduced due to serial instructions, and its recognition ability for complex image textures is weak. The threshold needs to be set artificially. In this context, we present an improved Sobel algorithm, which enlarges the traditional operator convolution factor size to 5×5, extends the direction to 16 directions, and introduces the median as a local adaptive threshold for judgment. The improved algorithm is implemented using FPGA and MATLAB respectively, and processes image data with a resolution of 1280×720. The experimental results show that: When the clock frequency of the image processing module is 84 MHz, the FPGA processes a 1280×720 image in about 0.0109 s, while the MATLAB processes the same image in an average time of 0.915 s. The Sobel edge detection algorithm proposed in this work effectively denoises the image while also detecting the edge information of the image efficiently and accurately. The algorithm processing speed is improved by about 84 times, which can well meet the real‐time requirements of video image processing systems.

show abstract