scholarly journals Edge Detection in Digital Image Using Statistical Method

2014 ◽  
Vol 9 (3) ◽  
pp. 15-19 ◽  
Author(s):  
Huvaida Manzoor ◽  
Dr.Yogeshwer Singh Randhawa
Keyword(s):  
Edge Detection ◽  
Statistical Method ◽  
Digital Image

scholarly journals Novel Boundary Edge Detection for Accurate 3D Surface Profilometry Using Digital Image Correlation

2018 ◽  
Vol 8 (12) ◽  
pp. 2541 ◽  
Author(s):  
Liang-Chia Chen ◽  
Ching-Wen Liang
Keyword(s):  
Digital Image Correlation ◽  
Edge Detection ◽  
Digital Image ◽  
Optical Diffraction ◽  
Image Correlation ◽  
Detection Accuracy ◽  
Boundary Edge ◽  
Discontinuous Surface ◽  
Full Field ◽  
Surface Profilometry

Digital image correlation (DIC) has emerged as a popular full-field surface profiling technique for analyzing both in-plane and out-of-plane dynamic structures. However, conventional DIC-based surface 3D profilometry often yields erroneous contours along surface edges. Boundary edge detection remains one of the key issues in DIC because a discontinuous surface edge cannot be detected due to optical diffraction and height ambiguity. To resolve the ambiguity of edge measurement in optical surface profilometry, this study develops a novel edge detection approach that incorporates a new algorithm using both the boundary subset and corner subset for accurate edge reconstruction. A pre-calibrated gauge block and a circle target were reconstructed to prove the feasibility of the proposed approach. Experiments on industrial objects with various surface reflective characteristics were also conducted. The results showed that the developed method achieved a 15-fold improvement in detection accuracy, with measurement error controlled within 1%.

Edge Detection by Maximum Entropy

2013 ◽  
pp. 146-159
Author(s):  
Ibrahim Guelzim ◽  
Ahmed Hammouch ◽  
El Mustapha Mouaddib ◽  
Driss Aboutajdine
Keyword(s):  
Edge Detection ◽  
Statistical Method ◽  
Maximum Entropy ◽  
Image Resolution ◽  
Entropy Measure ◽  
Geometric Distortions ◽  
Omnidirectional Images ◽  
Grayscale Images

In the edge detection, the classical operators based on the derivation are sensitive to noise which causes detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure. The authors compared its behavior with existing methods on omnidirectional images and perspectives images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images, the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors compared the results of detection visually. The tests are performed on grayscale images.

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.

Edge Detection by Maximum Entropy

2011 ◽  
Vol 1 (3) ◽  
pp. 1-15
Author(s):  
Ibrahim Guelzim ◽  
Ahmed Hammouch ◽  
El Mustapha Mouaddib ◽  
Driss Aboutajdine
Keyword(s):  
Edge Detection ◽  
Statistical Method ◽  
Maximum Entropy ◽  
Image Resolution ◽  
Entropy Measure ◽  
Geometric Distortions ◽  
Omnidirectional Images ◽  
Grayscale Images

In the edge detection, the classical operators based on the derivation are sensitive to noise which causes detection errors. It is even more erroneous in the case of omnidirectional images, due to geometric distortions caused by the used sensors. This paper proposes a statistical method of edge detection invariant to image resolution applied to omnidirectional images without preliminary treatments. It is based on the entropy measure. The authors compared its behavior with existing methods on omnidirectional images and perspectives images. The criteria of comparisons are the parameters of Fram and Deutsch. For omnidirectional images, the authors used two types of neighborhood: fixed and adapted to the parameters of the sensor. The authors compared the results of detection visually. The tests are performed on grayscale images.

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