Automatic thresholding method for edge detection algorithms

2016 ◽  
Author(s):  
Petre Anghelescu ◽  
Vlad Gabriel Iliescu ◽  
Constantin Mara ◽  
Mihai-Bogdan Gavriloaia
Keyword(s):  
Edge Detection ◽  
Automatic Thresholding ◽  
Thresholding Method ◽  
Detection Algorithms

scholarly journals Fractional Order Processing of Satellite Images

2021 ◽  
Vol 11 (11) ◽  
pp. 5288
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Rui Melicio
Keyword(s):  
Edge Detection ◽  
Fractional Order ◽  
High Performance ◽  
Satellite Images ◽  
Automatic Detection ◽  
Automatic Processing ◽  
Order Processing ◽  
Detection Algorithms ◽  
Grey Scale ◽  
Fixed Set

Nowadays, satellite images are used in many applications, and their automatic processing is vital. Conventional integer grey-scale edge detection algorithms are often used for this. This study shows that the use of color-based, fractional order edge detection may enhance the results obtained using conventional techniques in satellite images. It also shows that it is possible to find a fixed set of parameters, allowing automatic detection while maintaining high performance.

scholarly journals Efficient and Improved Edge Detection Via a Hysteresis Thresholding Method

2020 ◽  
Vol 118 (6) ◽  
pp. 954
Author(s):  
Sajid Khan ◽  
Dong-Ho Lee ◽  
Muhammad Asif Khan ◽  
Abdul Rehman Gilal ◽  
Junaid Iqbal ◽  
...  
Keyword(s):  
Edge Detection ◽  
Thresholding Method ◽  
Hysteresis Thresholding

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.

A WT-Based Edge Detection with Adaptive Threshold

2011 ◽  
Vol 204-210 ◽  
pp. 1386-1389
Author(s):  
Deng Yin Zhang ◽  
Li Xiao ◽  
Shun Rong Bo
Keyword(s):  
Wavelet Transform ◽  
Edge Detection ◽  
Threshold Value ◽  
Detection Algorithm ◽  
Adaptive Threshold ◽  
Value Iteration ◽  
Edge Detection Algorithm ◽  
Detection Algorithms ◽  
Image Edge ◽  
Threshold Setting

The existing edge detection algorithms with wavelet transform need to artificially set the threshold value and are lack of flexibility.To salve the limitations, in this paper, we propose a WT(wavelet transform)-based edge detection algorithm with adaptive threshold, which uses threshold value iteration method to achieve adaptive threshold setting. Comparison of experiment results for the CT image shows that the method which improve the clarity and continuity of the image edge can effectively distinguish edge and noise, and get more completely information of the edge. It has good application value in the fields of medical clinical diagnosis and image processing.

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