Quantum methodology for Edge detection: A compelling approach to enhance edge detection in digital image processing

2014 ◽  
Author(s):  
Rashmi Dubey ◽  
Rajesh Prasad Singh ◽  
Sarika Jain ◽  
Rakesh Singh Jadon
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image

Cellular Automata Algorithms for Digital Image Processing

Biometrics ◽  
2017 ◽  
pp. 382-402
Author(s):  
Petre Anghelescu
Keyword(s):  
Image Processing ◽  
Cellular Automata ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Image Understanding ◽  
Software Application ◽  
Detection Algorithms ◽  
Human Image ◽  
The Impact

In this paper are presented solutions to develop algorithms for digital image processing focusing particularly on edge detection. Edge detection is one of the most important phases used in computer vision and image processing applications and also in human image understanding. In this chapter, implementation of classical edge detection algorithms it is presented and also implementation of algorithms based on the theory of Cellular Automata (CA). This work is totally related to the idea of understanding the impact of the inherently local information processing of CA on their ability to perform a managed computation at the global level. If a suitable encoding of a digital image is used, in some cases, it is possible to achieve better results in comparison with the solutions obtained by means of conventional approaches. The software application which is able to process images in order to detect edges using both conventional algorithms and CA based ones is written in C# programming language and experimental results are presented for images with different sizes and backgrounds.

A Study on Different Edge Detection Techniques in Digital Image Processing

2017 ◽  
pp. 100-122 ◽  
Author(s):  
Shouvik Chakraborty ◽  
Mousomi Roy ◽  
Sirshendu Hore
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Basic Feature ◽  
Detection Methods ◽  
Natural Image ◽  
Detection Techniques ◽  
Image Edges

Image segmentation is one of the fundamental problems in image processing. In digital image processing, there are many image segmentation techniques. One of the most important techniques is Edge detection techniques for natural image segmentation. Edge is a one of the basic feature of an image. Edge detection can be used as a fundamental tool for image segmentation. Edge detection methods transform original images into edge images benefits from the changes of grey tones in the image. The image edges include a good number of rich information that is very significant for obtaining the image characteristic by object recognition and analyzing the image. In a gray scale image, the edge is a local feature that, within a neighborhood, separates two regions, in each of which the gray level is more or less uniform with different values on the two sides of the edge. In this paper, the main objective is to study the theory of edge detection for image segmentation using various computing approaches.

scholarly journals The Segmentation of Neutron Digital Radiography Image through the Edge Detection Method

2020 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Ayu Fitri Amalia ◽  
Widodo Budhi
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Neutron Beam ◽  
Digital Radiography ◽  
Digital Image ◽  
Digital Images ◽  
Neutron Radiography ◽  
Detection Methods

The digital image processing is one way to manipulate one or more digital images. Image segmentation has an essential role in the field of image analysis. The aim of this study was to develop an application to perform digital image processing of neutron digital radiographic images, hoping to improve the image quality of the digital images produced. The quality of edge detection could be used for the introduction of neutron digital radiographic image patterns through artificial intelligence. Interaction of neutrons with the matter mainly by nuclear reaction, elastic, and inelastic scattering. A neutron can quickly enter into a nucleus of an atom and cause a reaction. It is because a neutron has no charge. Neutrons can be used for digital imaging due to high-resolution information from deep layers of the material. The attenuated neutron beam in neutron radiography are passing through the investigated object. The object in a uniform neutron beam is irradiated to obtain an image neutron. The technique used in segmenting the neutron radiography in this study was a digital technique using a camera with a charge-coupled device (CCD), which was deemed more efficient technique compared to the conventional one. Through this technique, images could be displayed directly on the monitor without going through the film washing process. Edge detection methods were implemented in the algorithm program. It was the first step to complement the image information where edges characterize object boundaries. It is useful for the process of segmenting and identifying objects in neutron digital radiography images. The edge detection methods used in this study were Sobel, Prewitt, Canny, and Laplacian of Gaussian. According to the results of the image that have been tested for edge detection, the best image was carried out by the Canny operator because the method is more explicit. The obtained edges were more connected than the other methods which are still broken. The Canny technique provided edge gradient orientation which resulted in a proper localization.

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.

Study of Various Image Segmentation Methodologies

2017 ◽  
pp. 1-27
Author(s):  
Abahan Sarkar ◽  
Ram Kumar
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
New Technologies ◽  
Region Of Interest ◽  
Literature Survey ◽  
Segmentation Algorithms ◽  
Segmentation Image

In day-to-day life, new technologies are emerging in the field of Image processing, especially in the domain of segmentation. Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, the digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. This chapter presents a brief outline of some of the most common segmentation techniques (e.g. Segmentation based on thresholding, Model based segmentation, Segmentation based on edge detection, Segmentation based on clustering, etc.,) mentioning its advantages as well as the drawbacks. The Matlab simulated results of different available image segmentation techniques are also given for better understanding of image segmentation. Simply, different image segmentation algorithms with their prospects are reviewed in this chapter to reduce the time of literature survey of the future researchers.

Research on Image Processing Method of Train Wheel

2014 ◽  
Vol 487 ◽  
pp. 699-701
Author(s):  
Fang Li ◽  
Wan Tao Li
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Detection Method ◽  
Processing Method ◽  
Wheel Surface ◽  
Filtering Method ◽  
Edge Detection Method ◽  
Detection Speed

In order to improve the detection precision and speed of train wheel image,it is necessary to research digital image processing technology.In this paper,a kind of appropriate image filtering method,combination of smoothing and sharpening,and a kind of appropriate edge detection method,combination directly and by column in binarization,were introduced.A clear image of wheel surface can be obtained.The method of detection of scratch was designed.Non-contact measurement of train wheel surface quality can be realized,it will lead to the detection speed is faster, accuracy is higher.

scholarly journals A High-Level Synthesis Implementation and Evaluation of an Image Processing Accelerator

Technologies ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Dimitris Tsiktsiris ◽  
Dimitris Ziouzios ◽  
Minas Dasygenis
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
High Level Synthesis ◽  
Bare Metal ◽  
Sobel Edge Detection ◽  
High Level ◽  
Graphics Processing ◽  
Better Than

Most frequently, an FPGA is used as an implementation platform in applications of graphics processing, as its structure can effectively exploit both spatial and temporal parallelism. Such parallelization techniques involve fundamental restrictions, namely being their dependence on both the processing model and the system’s hardware constraints, that can force the designer to restructure the architecture and the implementation. Predesigned accelerators can significantly assist the designer to solve this problem and meet his deadlines. In this paper, we present our accelerators for Grayscale and Sobel Edge Detection, two of the most fundamental algorithms used in digital image processing projects. We have implemented those algorithms with a “bare-metal” VHDL design, written purely by hand, as a portable USB accelerator device, as well as an HLS-based overlay of a similar implementation designed to be used by a Python interface. The comparisons of the two architectures showcase that the HLS generated design can perform equally to or even better than the handwritten HDL equivalent, especially when the correct compiler directives are provided.

scholarly journals A COMPARATIVE STUDY OF COMMON EDGE DETECTION OPERATORS IN DIGITAL IMAGE PROCESSING

2021 ◽  
Vol 6 (4) ◽  
pp. 147-152
Author(s):  
Benjamin Kommey ◽  
John Kwame Dunyo ◽  
Eric Tutu Tchao ◽  
Andrew Selasi Agbemenu
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Comparative Study ◽  
Digital Image Processing ◽  
Digital Image ◽  
Common Edge

scholarly journals PERANCANGAN SISTEM SORTING BUAH JERUK MANIS BERBASIS PERBEDAAN WARNA KULIT BUAH

Heuristic ◽  
2016 ◽  
Vol 12 (02) ◽  
Author(s):  
Edi Kurniawan
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Detection Method ◽  
Citrus Fruit ◽  
Citrus Fruits ◽  
Maturity Level ◽  
Edge Detection Method ◽  
Orange Color

Almost all farmers use manual way to identify the level of maturity of oranges in doing the sorting. Identifying using the manual method has some drawbacks, namely the level of accuracy and farmers have already experience. With advances in technology now, sort of citrus fruits is based on the level of maturity can be done using digital image processing. Many digital image processing method one of them is the edge detection method. Edge detection method is used to determine the borders of an object. This study uses edge detection method to determine the extent of the orange color of the peel of citrus fruits because one of the characteristics of citrus fruit maturity level is how dominate the area of orange color on its peel. The results showed that with 10 oranges with a different maturity level, the system can determine it exactly. The average time required by the system to determine the level of maturity of citrus fruits is 0.6 secondsKeywords : citrus maturity, digital image processing, edge detection

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