We propose a totally novel method based on a revised ant colony clustering algorithm (ACCA) to explore the topic of
textural defect detection. In this algorithm, our efforts are mainly made on the definition of local irregularity
measurement and the implementation of the revised ACCA. The local irregular measurement defined evaluates the local
textural inconsistency of each pixel against their mini-environment. In our revised ACCA, the behaviors of each ant are
divided into two steps: release pheromone and act. The quantity of pheromone released is proportional to the irregularity
measurement; the actions of the ants to act next are chosen independently of each other in a stochastic way according to
some evaluated heuristic knowledge. The independency of ants implies the inherent parallel computation architecture of
this algorithm. We apply the proposed method in some typical textural images with defects. From the series of
pheromone distribution map (PDM), it can be clearly seen that the pheromone distribution approaches the textual defects
gradually. By some post-processing, the final distribution of pheromone can demonstrate the shape and area of the
defects well.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.