Edge Detection with different Parameters in Digital Image Processing using GUI

Srujana, P.; Priyanka, J.; Patnaikuni, V Y S S Sudir; Vejendla, Nancharaiah

doi:10.1109/iccmc51019.2021.9418327

Cited by 7 publications

(5 citation statements)

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“…The Sobel operator is more sensitive to diagonal edges than vertical and horizontal edges It is different from the Prewitt operator, which is more sensitive to vertical and horizontal edges [26]. This is in contrast to the Prewitt operator, which is more sensitive to vertical and horizontal edges.…”

Section: Sobel Operatormentioning

confidence: 90%

Image Edge Detection Capture Zoom for Facial Recognition Using Gradient Operators

Hadi

Sumijan

2022

komtekinfo

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Imagery on edge detection is a process that will display the edges of an image. Basically, edge detection is one of the techniques for the analysis of image quality in the spatial domain and is also one of the initial process in digital image processing. Edge detection serves to detect the border line of an object contained in the image. This study aims to identify and recognize face pattern objects in the capture zoom image. To perform face identification begins with collecting image data, image processing, image edge detection, thinning of the image, and identification process using the template matching method. The method used in edge detection uses 3 methods, namely Sobel, Roberts and Prewitt which are gradient operators to detect edges in facial images. The dataset used is image capture zoom. The trial was carried out in two stages, namely the identification of the face shape and the identification of the edges of the face. The conclusion of the study is that the Roberts operator is the operator that finds the least edge patterns in facial images than the other two operators, namely Prewitt and Sobel. Meanwhile, the Sobel operator produces edge patterns that are better in quality and quantity than using the Roberts and Prewitt operators.

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Section: Sobel Operatormentioning

confidence: 90%

Image Edge Detection Capture Zoom for Facial Recognition Using Gradient Operators

Hadi

Sumijan

2022

komtekinfo

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“…The evaluation showed improvements over the traditional techniques for image denoising. The study in [38], [39] investigated a MATLAB-based approach for edge detection and analysis of an image. Edge detection is a fundamental step in image processing operations utilizing several detection operators that perform image processing operations on an image.…”

Section: Review Of Related Workmentioning

confidence: 99%

Image processing system using MATLAB-based analytics

et al. 2021

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Owing to recent technological advancement, computers and other devices running several image editing applications can be further exploited for digital image processing operations. This paper evaluates various image processing techniques using matrix laboratory (MATLAB-based analytics). Compared to the conventional techniques, MATLAB gives several advantages for image processing. MATLAB-based technique provides easy debugging with extensive data analysis and visualization, easy implementation and algorithmic-testing without recompilation. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. Moreover, MATLAB codes are much concise compared to C++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the codes faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent MATLAB-based image processing application-tools. We also provide an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. Using the most recent algorithms running on MATLAB toolbox, we performed simulations to evaluate the performance of our proposed technique. The results largely present MATLAB as a veritable approach for image processing operations.

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“…With the rapid development of digital image processing technology, digital image has been widely used in the fields of archaeology, astronomy, automation and so on [1][2][3][4][5]. As a special kind of signal, it consists of a series of discrete pixel dots, with the value of each pixel representing the brightness or color information of the corresponding point [1].…”

Section: Introductionmentioning

confidence: 99%

Image processing based digital rubbing generation and application for the Han dynasty stone relief

Zheng,

Jiang

2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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Aiming at the problems of high damage rate of cultural relics, low processing efficiency and fuzzy detail processing when producing handmade rubbing, a method of generating digital rubbing of Han Dynasty Stone Relief based on image processing is proposed. The method first uses grayscale and mean value filter to eliminate the difference in shadows generated by image acquisition, and then generates adaptive thresholds for local pixel blocks for binarization to remove the noise caused by relief erosion and obtain the preprocessed image. In order to highlight the differences in texture details of the Han Dynasty Stone Relief, the foreground template with blurred edges is generated using contrast stretching and Ostu's method to obtain an enhanced image. Finally, the preprocessed image and the enhanced image are image fused to obtain a relief digital rubbing with noise elimination and enhanced details. Testing the effect of digital rubbing generation of Han Dynasty Stone Relief by MATLAB program. The experimental results show that, compared with the manual rubbing, the similarity reaches 80.26%, and the average processing time is 0.44s, which solves the problems of low conversion rate and fuzzy texture details of the traditional scheme successfully, and it plays a vital role in the protection and inheritance of cultural relics.

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Edge Detection with different Parameters in Digital Image Processing using GUI

Cited by 7 publications

References 0 publications

Image Edge Detection Capture Zoom for Facial Recognition Using Gradient Operators

Image Edge Detection Capture Zoom for Facial Recognition Using Gradient Operators

Image processing system using MATLAB-based analytics

Image processing based digital rubbing generation and application for the Han dynasty stone relief

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