An Analysis of Various Edge Detection Techniques on Illuminant Variant Images

2014 ◽  
pp. 521-532
Author(s):  
S. H. Krishna Veni ◽  
L Padma Suresh
Keyword(s):  
Edge Detection ◽  
Detection Techniques

scholarly journals Computerized or Automated Object Recognition and Analysis of Pattern Matching in Runways Using Surface Track Data

2021 ◽  
Vol 23 (11) ◽  
pp. 159-165
Author(s):  
JAYANTH DWIJESH H P ◽  
◽  
SANDEEP S V ◽  
RASHMI S ◽  
◽  
...  
Keyword(s):  
Edge Detection ◽  
Object Detection ◽  
Pattern Matching ◽  
Aerial Images ◽  
3 Dimensional ◽  
Detection Techniques ◽  
Detection Algorithms ◽  
Detection Analysis ◽  
Efficient Detection ◽  
Minimal Damage

In today’s world, accurate and fast information is vital for safe aircraft landings. The purpose of an EMAS (Engineered Materials Arresting System) is to prevent an aeroplane from overrunning with no human injury and minimal damage to the aircraft. Although various algorithms for object detection analysis have been developed, only a few researchers have examined image analysis as a landing assist. Image intensity edges are employed in one system to detect the sides of a runway in an image sequence, allowing the runway’s 3-dimensional position and orientation to be approximated. A fuzzy network system is used to improve object detection and extraction from aerial images. In another system, multi-scale, multiplatform imagery is used to combine physiologically and geometrically inspired algorithms for recognizing objects from hyper spectral and/or multispectral (HS/MS) imagery. However, the similarity in the top view of runways, buildings, highways, and other objects is a disadvantage of these methods. We propose a new method for detecting and tracking the runway based on pattern matching and texture analysis of digital images captured by aircraft cameras. Edge detection techniques are used to recognize runways from aerial images. The edge detection algorithms employed in this paper are the Hough Transform, Canny Filter, and Sobel Filter algorithms, which result in efficient detection.

Adaptive Edge Detection Method Towards Features Extraction From Diverse Medical Imaging Technologies

2018 ◽  
pp. 1245-1278
Author(s):  
Indra Kanta Maitra ◽  
Samir Kumar Bandhyopadhyaay
Keyword(s):  
Edge Detection ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Detection Algorithm ◽  
Edge Detection Algorithm ◽  
Detection Techniques ◽  
Robust Segmentation ◽  
Image Characteristics ◽  
Edge Detection Method ◽  
Medical Image Enhancement

The CAD is a relatively young interdisciplinary technology, has had a tremendous impact on medical diagnosis specifically cancer detection. The accuracy of CAD to detect abnormalities on medical image analysis requires a robust segmentation algorithm. To achieve accurate segmentation, an efficient edge-detection algorithm is essential. Medical images like USG, X-Ray, CT and MRI exhibit diverse image characteristics but are essentially collection of intensity variations from which specific abnormalities are needed to be isolated. In this chapter a robust medical image enhancement and edge detection algorithm is proposed, using tree-based adaptive thresholding technique. It has been compared with different classical edge-detection techniques using one sample two tail t-test to exam whether the null hypothesis can be supported. The proposed edge-detection algorithm showing 0.07 p-values and 2.411 t-stat where α = 0.025. Moreover the proposed edge is single pixeled and connected which is very significant for medical edge detection.

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.

Segmentation of digital images with various edge detection techniques using OpenCV

2021 ◽  
pp. 535-539
Author(s):  
Saumitra Shukla ◽  
Vaishali Kikan ◽  
Ritvi Sachdeva ◽  
Yash Chaturvedi ◽  
Ronak Jain
Keyword(s):  
Edge Detection ◽  
Digital Images ◽  
Detection Techniques

Boundary Layer Detection Techniques Applied to Edge Detection

2019 ◽  
Vol 19 (02) ◽  
pp. 1950010 ◽  
Author(s):  
Fernando O. Guillén-Reyes ◽  
Francisco J. Domínguez-Mota
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Edge Detection ◽  
Digital Image ◽  
Execution Time ◽  
Digital Images ◽  
Intensity Function ◽  
Detection Techniques ◽  
Novel Algorithm

In this paper, we describe a novel algorithm for edge detection on a digital image, which is based locally on the directional averaged gradient properties of the intensity function, and produces very satisfactory results in high-resolution digital images in low execution time. Several examples show results which are comparable to those obtained by Canny and Sobel methods.

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