Quantum image edge extraction based on Laplacian operator and zero-cross method

Fan, Ping; Zhou, Ri-Gui; Hu, Wen Hua; Jing, Naihuan

doi:10.1007/s11128-018-2129-x

Cited by 45 publications

(19 citation statements)

References 38 publications

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“…Introducing the concept of mathematical morphology [15,16,17,18,19] to the image edge detection operator can overcome the shortcomings of the classical operator [20,21,22] and can greatly reduce the calculation amount. This paper proposes an improved anti-noise morphology algorithm for image navigation line extraction, which selects a pair of smaller-scale structuring elements for further anti-noise processing to extract an image navigation line based on the edge feature.…”

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…Introducing the concept of mathematical morphology [15][16][17][18][19] to the image edge detection operator can overcome the shortcomings of the classical operator [20][21][22], and greatly reduce the calculation amount. The paper proposed an improved anti-noise morphology algorithm for image navigation line extraction which selects a pair of smaller-scale structural elements for further antinoise processing to extract image navigation line based on the edge feature.…”

Section: Improved Anti-noise Morphology Algorithm For Image Navigatiomentioning

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.

show abstract

“…Introducing the concept of mathematical morphology [15][16][17][18][19] to the image edge detection operator can overcome the shortcomings of the classical operator [20][21][22] and can greatly reduce the calculation amount. This paper proposed an improved anti-noise morphology algorithm for image navigation line extraction which selects a pair of smaller-scale structuring elements for further antinoise processing to extract image navigation line based on the edge feature.…”

Section: Improved Anti-noise Morphology Algorithm For Image Navigatiomentioning

confidence: 99%

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

Lu¹,

Zeng²,

Wang³

et al. 2019

Preprint

View full text Add to dashboard Cite

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, 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 it’s 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 new and old soil than other competing vanilla methods such as Tarel, Multi-scale Retinex, Wavelet-based Retinex and Homomorphic Filtering inspite of having 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 and old soil boundary line extracted by the proposed improved anti-noise morphology algorithm which has broad application prospect.

show abstract

Quantum image edge extraction based on Laplacian operator and zero-cross method

Cited by 45 publications

References 38 publications

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 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

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

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Quantum image edge extraction based on Laplacian operator and zero-cross method

Cited by 45 publications

References 38 publications

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 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

Navigation Algorithm Based on the Boundary Line of New and Old Soil Combined Using Guided Filtering and Improved Anti-noise Morphology

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About

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