Edge detection in mammogram images using log-normal distribution

El-Zaart, Ali; Al-Jibory, Wafaa Kamel

doi:10.1109/ictea.2012.6462889

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…The main idea of edge extraction is to use the edge enhancement operator to highlight the local edge in the image, then define the “edge intensity” of the pixel, and extract the edge point set by setting the threshold. Common edge extraction algorithms include differential operator, Laplace operator [ 24 ], Canny operator [ 25 ], etc. Canny operator is not easily disturbed by noise and can extract weak edges, but it also has the shortcomings of the above line feature extraction: it cannot perform good edge extraction to adapt to brightness changes.…”

Section: Introductionmentioning

confidence: 99%

An Improved FAST Algorithm Based on Image Edges for Complex Environment

Ding

2022

Sensors

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In complex environments such as those with low textures or obvious brightness changes, point features extracted from a traditional FAST algorithm cannot perform well in pose estimation. Simultaneously, the number of point features extracted from FAST is too large, which increases the complexity of the build map. To solve these problems, we propose an L-FAST algorithm based on FAST, in order to reduce the number of extracted points and increase their quality. L-FAST pays more attention to the intersection of line elements in the image, which can be extracted directly from the related edge image. Hence, we improved the Canny edge extraction algorithm, including denoising, gradient calculation and adaptive threshold. These improvements aimed to enhance the sharpness of image edges and effectively extract the edges of strong light or dark areas in the images as brightness changed. Experiments on digital standard images showed that our improved Canny algorithm was smoother and more continuous for the edges extracted from images with brightness changes. Experiments on KITTI datasets showed that L-FAST extracted fewer point features and increased the robustness of SLAM.

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Section: Introductionmentioning

confidence: 99%

An Improved FAST Algorithm Based on Image Edges for Complex Environment

Ding

2022

Sensors

View full text Add to dashboard Cite

show abstract

Edge detection in mammogram images using log-normal distribution

Cited by 1 publication

References 7 publications

An Improved FAST Algorithm Based on Image Edges for Complex Environment

An Improved FAST Algorithm Based on Image Edges for Complex Environment

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