.png){kind=icon}
.png){kind=icon}
Return
Ant algorithms for image feature extraction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.01.020.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper extends on previous work in applying an ant algorithm to image feature extraction, focusing on edge pattern extraction, as well as the broader study of self-organisation mechanisms in digital image environments. A novel method of distributed adaptive thresholding is introduced to the ant algorithm, which enables automated distributed adaptive thresholding across the swarm. This technique is shown to increase performance of the algorithm, and furthermore, eliminates the requirement for a user set threshold, allowing the algorithm to autonomously adapt an appropriate threshold for a given image, or data set. Additionally this approach is extended to allow for simultaneous multiple-swarm multiple-feature extraction, as well as dynamic adaptation to changing imagery. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Ant algorithm
Swarm intelligence
Feature extraction
Image processing
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W