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Researcher Dashora, Anjali
Advisor Joshi, Sunil
Source KrishiKosh-Indian National Agricultural Research System
Content type Text
Educational Degree Master of Technology (M.Tech.)
Publisher MPUAT
File Format PDF
Language English
Subject Domain (in DDC) Technology ♦ Agriculture & related technologies
Subject Keyword Fuzzy ♦ Edge Detection ♦ Image ♦ Ant Colony Optimization ♦ A Fuzzy Based Edge Detection Technique of An Image Using Ant Colony Optimization ♦ Agricultural Engineering
Abstract Many Edge detection methods are developed so far .Ant Colony Optimization is an algorithm inspired by the nature to solve the computational problems.ACO uses the concept of collective activities of ants, that they are self-organized and work without a central control. In this thesis modification in the ACO algorithm is proposed. First step in ACO algorithm is the initialization of ants. Then ants are moved from one node to another followed by updating the pheromone matrix. In this thesis the ant’s position are logically initialized using the intensity variation in the grayscale image. Each ant’s movement is influenced by the heuristic information. Further the algorithm was followed by the condition for updating the ant’s pheromone concentration and stopping the ant movement. Finally the binary decision was made whether the detected edge is correct or not using threshold method. The detected edge map is compared with traditional edge detectors and also with the ACO algorithm. Comparison is done using parameters Figure of Merit, Hausdorff Distance and BDM. The experiment was performed on software MATLABR2008 using images of size 128 128.
Educational Use Research
Education Level UG and PG
Learning Resource Type Thesis
Publisher Place Udaipur
Size (in Bytes) 8.89 MB
Page Count 76