Robust edge detection based on Modified Moore-Neighbor

Biswas, Soumen; Hazra, Ranjay

doi:10.1016/j.ijleo.2018.05.011

Cited by 45 publications

(17 citation statements)

References 11 publications

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“…MSE is cumulative squared error between the compressed and the original image. As per papers [3, 36] the formulae to calculate PSNR, MSE and RMSE are given below:PSNR=10log10true(2n−1true)2MSE…”

Section: Methodsmentioning

confidence: 99%

“…Biswas and Hazra [3] have proposed an edge detection method based Modified Moore-Neighbor. This algorithm operates in two steps, namely modified Moore-Neighbor algorithm followed by range filtering to detect the edges.…”

Section: Related Workmentioning

confidence: 99%

“…Edge detection is one of the crucial pre-processing stages of digital image processing. It, basically, aims at identifying points in the image where the contrast and brightness changes abruptly [3]. In edge detection stage, the input is the original image and output is image in the form edges based on selected algorithm or method.…”

Section: Introductionmentioning

confidence: 99%

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

Bharodiya¹,

Gonsai

2019

Heliyon

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Bharodiya¹,

Gonsai

2019

Heliyon

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show abstract

“…However, the entropy used in this method has an impact on the edge detection results. Using a modified Moore-Neighbor algorithm, an edge detection method [9] was proposed. It is also called as 8-Neighborhood.…”

Section: Literature Surveymentioning

confidence: 99%

Edge detection based on Improved Non-Maximum Suppression Method

Sreedhar¹,

Sathappan²

2019

IJITEE

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Semantic Segmentation and edge detection are important research fields for scene understanding in computer vision. A hierarchical framework called Contextual Hierarchical Model (CHM) was proposed for semantic image segmentation and edge detection. It learned contextual information using a Logistic Disjunctive Normal Networks (LDNN) classifier. The class average accuracy of CHM was improved by defining a global constraint using Conditional Random Field (CRF), Hierarchical CRF (HCRF), Higher order HCRF (HHCRF). The LDNN was improved by using proximal gradient method which minimizes the quadratic error and it had high convergence rate than gradient descent method. The weight and bias terms of LDNN was optimized by using Grey Wolf Optimization algorithm (GWO) which improves the classification accuracy and it also reduces time complexity of LDNN. During the edge detection using CHM, a multi-scale strategy was adopted to compute edge maps. A Non-Maximum Suppression (NMS) was used to obtain the thinned edges in images. However, it is a post-processing step which consumes additional time in edge detection process. So, in this paper, the edge detection is interpreted as a classification problem where the thinned edges in images are obtained without any post-processing step. It reduces the time consumption. Two key ingredients such as loss and joint processing are included in NMS to improve the detection of thinned edges. The loss is used to penalize the double edge detection and the joint processing is used to reduce the loss of edge detection by including a pair features as additional feature for edge detection. The pair features, Haar, Histogram of Gradient (HOG) and SIFT features are given as input to LDNN to detect the edges that improve the efficiency of CHM based edge detection.

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“…However, all of this gain particular weight and interest with the emergence and implementation of such systems on devices like Raspberry Pi and Arduino, which very often use real-time image processing, object detection and segmentation [35][36][37][38][39]. Many techniques and enhancements have been proposed to maximize the quality of edge detection and segmentation [39][40][41][42][43]. Given that the resolutions and image quality are increasing, thus occupying a large storage space, it is important to do compression so as not to impair the quality.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Edge Detection on Compressed Images with Different Complexities

Maksimovic¹,

Jakšić²,

Petrović³

et al. 2020

ACTA POLYTECH HUNG

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This paper provides edge detection analysis on images, which consist of different numbers of details (small, medium and high number of details) and which are compressed by different compression algorithms -JPEG, JPEG2000 and SPIHT. Images from the BSD (Berkeley Segmentation Database) database were used and compressed with different number of bits per pixel. The analysis was performed for five edge detectors: Canny, LoG, Sobel, Prewitt, and Roberts. The fidelity of the detected edges was determined using the objective measures Figure of Merit (FOM), F measure and Performance Ratio (PR), where the reference value was taken from the GroundTruth image. Based on the results presented in the tables, it can be concluded that edge detection behaves differently depending on the number of bits per pixel and applied compression algorithm, as well as, the number of details in the image. Roberts operator has been proven to be the best solution, when it is necessary to perform better edge detection over compressed images with small a number of details, but Canny shows better results for images with a high number of details.

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Robust edge detection based on Modified Moore-Neighbor

Cited by 45 publications

References 11 publications

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

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

Edge detection based on Improved Non-Maximum Suppression Method

Analysis of Edge Detection on Compressed Images with Different Complexities

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