scholarly journals A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems

Molecules ◽  
2019 ◽  
Vol 24 (7) ◽  
pp. 1235 ◽  
Author(s):  
Jianying Yuan ◽  
Dequan Guo ◽  
Gexiang Zhang ◽  
Prithwineel Paul ◽  
Ming Zhu ◽  
...  
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Time Complexity ◽  
Parallel Implementation ◽  
Fundamental Problem ◽  
Image Data ◽  
Image Resolution ◽  
P System ◽  
Image Edge Detection ◽  
Image Edge

Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O ( 1 ) can be achieved regardless of image resolution theoretically.

Comparison and Evaluation of Edge Detection Segmentation Techniques

2014 ◽  
Vol 889-890 ◽  
pp. 1069-1072
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Root Mean Square Error ◽  
Edge Detection ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Basic Problem ◽  
Mean Square ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Image Edge

Edge detection is the basic problem in the field of image processing. Various image edge detection techniques are introduced. Using various edge detection techniques different images are analyzed and compared by MATLAB7.0. In order to evaluate the effect of edge segmentation, the root mean square error is used. The experimental results show that no an edge detection technique works well for all types of images.

Large NC Machining Fast Knife Mould Based on Image Technology

2011 ◽  
Vol 188 ◽  
pp. 613-616
Author(s):  
Xian Li Liu ◽  
Xiao Ran Song ◽  
L.J. Liu ◽  
Zhong Yang Zhao ◽  
D.L. Ma
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Nc Machining ◽  
Machining Accuracy ◽  
Thinning Algorithm ◽  
Image Edge Detection ◽  
Image Edge ◽  
Characteristics Of Image ◽  
Small Parts

In NC machining large moulds, second mold will install deviated orientation problem. Based on image technology, after the second for installation of workpiece, collected clip in the concrete pins on measuring and calculating the image processing, analysis, and finally got the localization generated during installation and adjustment, the deviation of the machine to eliminate biases. In image processing of PSCP thinning algorithm, based on the characteristics of image edge detection were analyzed, the extraction and processing, improve the machining accuracy and efficiency. This method can also be used in small parts processing detection, etc.

scholarly journals Comparative Analysis of Image Quality Values on Edge Detection Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 345-351
Author(s):  
Wicaksono Yuli Sulistyo ◽  
Imam Riadi ◽  
Anton Yudhana
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Edge Detection ◽  
Computer Technology ◽  
Detection Method ◽  
Detection Methods ◽  
Sobel Operator ◽  
Image Edge Detection ◽  
Image Edge ◽  
Edge Detection Method

Identification of object boundaries in a digital image is developing rapidly in line with advances in computer technology for image processing. Edge detection becomes important because humans in recognizing the object of an image will pay attention to the edges contained in the image. Edge detection of an image is done because the edge of the object in the image contains very important information, the information obtained can be either size or shape. The edge detection method used in this study is Sobel operator, Prewitt operator, Laplace operator, Laplacian of Gaussian (LoG) operator and Kirsch operator which are compared and analyzed in the five methods. The results of the comparison show that the clear margins are the Sobel, Prewitt and Kirsch operators, with PSNR calculations that produce values ​​above 30 dB. Laplace and LoG operators only have an average PSNR value below 30 dB. Other quality comparisons use the histogram value and the contrast value with the highest value results in the Laplace and LoG operators with an average histogram value of 110 and a contrast value of 24. The lowest histogram and contrast value are owned by the Sobel and Prewitt operators.  

scholarly journals Algorithmic Approaches For Image Edge Detection

2021 ◽  
Vol 10 (3) ◽  
pp. 1942-1948
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Image Enhancement ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Algorithmic Approaches

Edge detection is most important technique in digital image processing. It play an important role in image segmentation and many other applications. Edge detection providesfoundation to many medical and military applications.It difficult to generate a generic code for edge detection so many kinds ofalgorithms are available. In this article 4 different approaches Global image enhancement with addition (GIEA), Global image enhancement with Multiplication (GIEM),Without Global image enhancement with Addition (WOGIEA),and without Global image enhancement with Multiplication (WOGIEM)for edge detection is proposed. These algorithms are validatedon 9 different images. The results showthat GIEA give us more accurate results as compare to other techniques.

scholarly journals Comparison of Adaptive Ant Colony Optimization for Image Edge Detection of Leaves Bone Structure

2018 ◽  
Vol 6 (2) ◽  
pp. 328-336 ◽  
Author(s):  
Febri Liantoni ◽  
Rifki Indra Perwira ◽  
Daniel Silli Bataona
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Ant Colony Optimization ◽  
Digital Image Processing ◽  
Optimization Algorithm ◽  
Bone Structure ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Image Edge Detection ◽  
Image Edge

Leaf bone structure has a characteristic that can be used as a reference in digital image processing. One form of digital image processing is image edge detection. Edge detection is the process of extracting edge information from an image. In this research, Adaptive Ant Colony Optimization algorithm is proposed for edge image detection of leaf bone structure. The Adaptive Ant Colony Optimization method is a modification of Ant Colony Optimization, in which the initial an ant dissemination process is no longer random, but it is done by a pixel placement process that allows for an edge based on the value of the image gradient. As a comparison also performed edge detection using Robert and Sobel method. Based on the experiments performed, Adaptive Ant Colony Optimization algorithm is capable of producing more detailed image edge detection and has thicker borders than others. Keywords: edge detection, ant colony optimization, robert, sobel

Algorithm for Improving Image Edge Detection Based on Directional Wavelet Transform

2014 ◽  
Vol 543-547 ◽  
pp. 2763-2765
Author(s):  
Xiang Shi Wang ◽  
Gui Feng Liu
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Median Filter ◽  
Image Data ◽  
Edge Image ◽  
Image Edge Detection ◽  
Image Edge ◽  
Directional Wavelet

The information of the image edge is the important parameters in identifying, segmenting and compressing image. The performance of the algorithms about edge image algorithms closely relies on the noises generally included in the image. The main goal of this paper is firstly to eliminate the false edges by the median filter and extract the information of the edge image by directional wavelet transform. Application on image data shows that the proposed tool can enhance the direction edge images which is fused to form the complete image edge.

Research of Image Edge Detection Algorithms Based on Android Platform

2014 ◽  
Vol 644-650 ◽  
pp. 1154-1157
Author(s):  
Yi He ◽  
Tian Li Li ◽  
Ying Qian Zhang
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Mobile Platform ◽  
Detection Algorithms ◽  
Image Edge Detection ◽  
Android Platform ◽  
Image Edge ◽  
Platform Development

With mobile platform development there are more and more Android-based image processing applications. The principles of four kinds of edge detection algorithms are analyzed in this paper and such algorithms are realized by adopting JNI technology based on android platform. At last the effect and efficiency of such algorithms are also compared and summarized.

scholarly journals Image Edge Detection Algorithm Based on Fuzzy Radial Basis Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Lin Feng ◽  
Jian Wang ◽  
Chao Ding
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Image Understanding ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Median Filters ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Radial Basis ◽  
Image Edge

Digital image processing technology is widely used in production and life, and digital images play a pivotal role in the ever-changing technological development. Noise can affect the expression of image information. The edge is the reflection of the main structure and contour of the image, and it is also the direct interpretation of image understanding and the basis for further segmentation and recognition. Therefore, suppressing noise and improving the accuracy of edge detection are important aspects of image processing. To address these issues, this paper presents a new detection algorithm combined with information fusion based on the existing image edge detection techniques, and the algorithm is studied from two aspects of fuzzy radial basis fusion discrimination, in terms of preprocessing algorithm, comparing the denoising effect of mean and median filters with different template sizes on paper images with added noise, and selecting the improved median filter denoising, comparing different operator edge detection. The effect of image edge detection contour is finally selected as the 3 ∗ 3 Sobel operator for edge detection; the binarized image edge detection contour information is found as the minimum outer rectangle and labeled, and then, the original paper image is scanned line by line to segment the target image edge region. The image edge detection algorithm based on fuzzy radial basis fuser can not only speed up the image preprocessing, meet the real-time detection, and reduce the amount of data processed by the upper computer but also can accurately identify five image edge problems including folds and cracks, which has good application prospects.

Image Edge Detection Based on Solitary Wave Algorithm

2014 ◽  
Vol 608-609 ◽  
pp. 489-493
Author(s):  
Zi Min Zhu ◽  
Rong Qiang Liu
Keyword(s):  
Image Processing ◽  
Solitary Wave ◽  
Edge Detection ◽  
Change Detection ◽  
Local Information ◽  
Important Goal ◽  
Image Edge Detection ◽  
Salient Points ◽  
Image Edge ◽  
Image Change Detection

Edge detection is the most basic arithmetic of the local information of image change detection, image edge detection can help people better describe or identify image, is abasic technique in image processing. Edge detection is mainly to find out the important goal of the salient points on the boundary of the image,because the traditional edge detection operators have the same direction, so easily missed in the detection process.In this paper, the solitary wave algorithm for image edge detection, this algorithm can provide both the direction and size of the image edge, a certain extent, can also solve the missing edges, it is possible to extract more accurate image information.

Research on Edge Detection Technology

2013 ◽  
Vol 860-863 ◽  
pp. 2884-2887 ◽  
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Canny Edge Detection ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Detection Technology ◽  
Canny Edge ◽  
Important Field ◽  
Image Edge ◽  
Better Than

Edge detection is an important field in image processing. Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection techniques. Various image edge detection techniques are introduced. These techniques are compared by using MATLAB7.0. The qualities of these techniques are elaborated. The results show that Canny edge detection techniques is better than others.

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