A Novel Approach to Improve Sobel Edge Detector

Mathur, Neha; Mathur, Shruti; Mathur, Divya

doi:10.1016/j.procs.2016.07.230

Cited by 68 publications

(31 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sobel operator Sobel operator deals with edge detection in image processing and computer vision. Sobel and Feldman displayed the idea of a "mask 3x3 Image Gradient Operator" at a talk at SAIL in 1968 (17). Sobel operator is based on the mathematical convolution image with a small, separable, and integer valued filter in the two directions, horizontal and vertical, so relatively not expensive in terms of computations, and the gradient approximation that it obtains is relatively crude, in particular for highfrequency variations in the image.…”

Section: Standard Deviation (σ)mentioning

confidence: 99%

New algorithms to Enhanced Fused Images from Auto-Focus Images

2020

BSJ

View full text Add to dashboard Cite

Section: Standard Deviation (σ)mentioning

confidence: 99%

New algorithms to Enhanced Fused Images from Auto-Focus Images

2020

BSJ

View full text Add to dashboard Cite

“…In this study, the information used was based on the intensity to locate the edges in an image, for which the polynomial evaluation was performed to decompose the high and low-index values. An improved edge detection algorithm as proposed in [60] based on k-means clustering, where the image data were related to a brain tumor to detect the associated disease. Another approach of edge detection is called anisotropic method.…”

Section: Corner Detectionmentioning

confidence: 99%

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

Zakariah¹,

Al-Shalfan²

2020

IJCEE

View full text Add to dashboard Cite

“…Mathur et al. [21] have proposed a novel approach to detect brain tumor from MRI images using k-means clustering, mamdani fuzzy inference system and sobel edge detector. It gives exact location of tumor based on appropriate threshold selection.…”

Section: Related Workmentioning

confidence: 99%

An improved edge detection algorithm for X-Ray images based on the statistical range

Bharodiya¹,

Gonsai

2019

Heliyon

View full text Add to dashboard Cite

Edge detection is the prior stage to object recognition and considered as a pillar for image processing task. It is a process to detect such locations from images in terms of pixels where their intensity changing is abruptly. There are many types of images such as medical images, satellite images, articular images, industrial images, general purpose images etc. X-Ray is a type of medical image in which electronic radiation is passed into the human body to capture image of inner parts for better disease diagnoses by orthopaedics or radiologist. In this research paper, we have proposed an improved method to detect edges from human being's X-Ray images based on Gaussian filter and statistical range. Gaussian filter is used for image preprocessing and enhancement. Whereas, Statistical range is used to calculate difference between maximum and minimum pixels from every 3X3 image matrix partition. These two can work to detect edges from X-Ray images. We have also presented a comprehensive comparison of our proposed method with four existing latest methods/algorithms of edge detection. Apart from X-Ray images, experiments have also been conducted on human X-Ray images to detect edges. Further, we have found that our proposed method is superior in terms of MSE, RMSE, PSNR and computation time to detect edges from X-Ray images of human being.

show abstract

A Novel Approach to Improve Sobel Edge Detector

Cited by 68 publications

References 5 publications

New algorithms to Enhanced Fused Images from Auto-Focus Images

New algorithms to Enhanced Fused Images from Auto-Focus Images

Image Boundary, Corner, and Edge Detection: Past, Present, and Future

An improved edge detection algorithm for X-Ray images based on the statistical range

Contact Info

Product

Resources

About