scholarly journals Performance Evaluation of Edge Detection Techniques for Fractal Images

2020 ◽  
Vol 8 (6) ◽  
pp. 34-39
Author(s):  
Priyanka Kujur
Keyword(s):  
Performance Evaluation ◽  
Edge Detection ◽  
Detection Techniques ◽  
Fractal Images

Performance evaluation of canny edge detection on a tiled multicore architecture

2011 ◽  
Author(s):  
Andrew Z. Brethorst ◽  
Nehal Desai ◽  
Douglas P. Enright ◽  
Ronald Scrofano
Keyword(s):  
Performance Evaluation ◽  
Edge Detection ◽  
Multicore Architecture ◽  
Canny Edge Detection ◽  
Canny Edge

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.

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