Prewitt edge detection based on BM3D image denoising

Ye, Hanmin; Shen, Biyi; Yan, Shili

doi:10.1109/iaeac.2018.8577643

Cited by 17 publications

(10 citation statements)

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“…Face Recognition [2] Line Edge Map Vision systems [3] DNN Machine vision [4] Multilevel Fuzzy Edge Detection (FMFED) Medical image and Robotics [5] LoG Medical Images [6] Statistical Texture Analysis [7] Roberts, Prewitt, Canny Brain Tumor Segmentation [8] Sobel Robotics, medical imaging [9] DNN Identification interpretation [10] Fractional Derivatives Optics [11] Spin Hall Effect State of forests analysis [12] Canny Satellite Images [13] Prewitt, Kirsch, LoG, morphological Object extraction [14] Fuzzy Mealtime Face Recognition [14] LoG Image processing [15] Robert, Sobel, Prewitt and Canny MRI Medical Images [16] DNN Cataract Detection [18] Canny Synthetic Aperture Radar [19] Oriented Gaussian filter Human discrimination objects [20] Prewitt Computed tomography [21] DNN Meta-materials and Meta-surfaces [22] Optical edge However, noise is a crucial problem in edge detection and contours recovery. It significantly reduces performances of high-level processing, such as image recognition and classification [1], [2], [5], [6].…”

Section: Application Methodsmentioning

confidence: 99%

A novel edge detection method based on efficient gaussian binomial filter

Mansour¹,

Bretaudeau²

2021

Int. J. Adv. Intell. Informatics

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Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.

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Section: Application Methodsmentioning

confidence: 99%

A novel edge detection method based on efficient gaussian binomial filter

Mansour¹,

Bretaudeau²

2021

Int. J. Adv. Intell. Informatics

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“…Different edge extraction results obtained by applying the improved anti-noise morphology algorithm and other popular existing edge extraction algorithms such as Sobel [36,37,38,39,40], Roberts [41,42], Prewitt [43,44], and Log [45] are shown in Figure 15. There were some breakages in the extracted edges using the latter four algorithms, which led to large errors during navigation line identification processing.…”

Section: Methodsmentioning

confidence: 99%

Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Lü

Zeng

Wang

et al. 2019

Sensors

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An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. At first, the two key steps of guided filtering and improved anti-noise morphology navigation line extraction were addressed in detail. Then, the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image-processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption of 0.094 s in comparison with HSV, HIS, and 2R-G-B color spaces. The guided filtering method can effectively distinguish the boundary between the tillage soil compared to other competing vanilla methods such as Tarel, multi-scale retinex, wavelet-based retinex, and homomorphic filtering in spite of having the fastest processing speed of 0.113 s . The extracted soil boundary line of the improved anti-noise morphology algorithm has the best precision and speed compared to other operators such as Sobel, Roberts, Prewitt, and Log. After comparing different sizes of image templates, the optimal template with the size of 140 × 260 pixels could achieve high-precision vision navigation while the course deviation angle was not more than 7.5 ° . The maximum tractor speed of the optimal template and global template were 51.41 km / h and 27.47 km / h , respectively, which can meet the real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the tillage soil boundary line extracted by the proposed improved anti-noise morphology algorithm, which has broad application prospect.

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“…The navigation error can decrease from 10° by using the traditional morphology algorithm to 0.5° by using the improved anti-noise morphology. The comparison edge extraction result between the improved anti-noise morphology algorithm and the popular edge extraction algorithms such as Sobel [36][37][38][39][40], Roberts [41,42], Prewitt [43,44] and Log [45] is shown in Figure 15. There are some breakages in the extracted edges by using the later four algorithms which will lead to large error during navigation line identification processing.…”

Section: Navigation Line Extraction Using Improved Anti-noise Morpholmentioning

confidence: 99%

Navigation Algorithm Based on the Boundary Line of New & Old Soil Using Improved Anti-Noise Morphology

Lü¹,

Zeng²,

Wang³

et al. 2019

Preprint

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An improved anti-noise morphology vision navigation algorithm is proposed for intelligent tractor tillage in a complex agricultural field environment. Firstly, the two key steps, Guided Filtering and improved anti-noise morphology navigation line extraction, were addressed in detail. Then the experiments were carried out in order to verify the effectiveness and advancement of the presented algorithm. Finally, the optimal template and its application condition were studied for improving the image processing speed. The comparison experiment results show that the YCbCr color space has minimum time consumption, 0.094 s, compared with HSV, HIS and 2R-G-B color spaces. The Guided Filtering method can enhance the new & old soil boundary effectively than any other methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering, meanwhile, has the fastest processing speed of 0.113 s. The extracted soil boundary line of the improved anti-noise morphology algorithm has best precision and speed compared with other operators such as Sobel, Roberts, Prewitt and Log. After comparing different size of image template, the optimal template with the size of 140×260 pixels can meet high precision vision navigation while the course deviation angle is not more than 7.5°. The maximum tractor speed of the optimal template and global template are 51.41 km/h and 27.47 km/h respectively which can meet real-time vision navigation requirement of the smart tractor tillage operation in the field. The experimental vision navigation results demonstrated the feasibility of the autonomous vision navigation for tractor tillage operation in the field using the new & old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

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Prewitt edge detection based on BM3D image denoising

Cited by 17 publications

References 0 publications

A novel edge detection method based on efficient gaussian binomial filter

A novel edge detection method based on efficient gaussian binomial filter

Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Navigation Algorithm Based on the Boundary Line of New & Old Soil Using Improved Anti-Noise Morphology

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Prewitt edge detection based on BM3D image denoising

Cited by 17 publications

References 0 publications

A novel edge detection method based on efficient gaussian binomial filter

A novel edge detection method based on efficient gaussian binomial filter

Navigation Algorithm Based on the Boundary Line of Tillage Soil Combined with Guided Filtering and Improved Anti-Noise Morphology

Navigation Algorithm Based on the Boundary Line of New &amp; Old Soil Using Improved Anti-Noise Morphology

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Navigation Algorithm Based on the Boundary Line of New & Old Soil Using Improved Anti-Noise Morphology