Galaxy gravity optimization(GGO) an algorithm for optimization, inspired by comets life cycle

Mousavi, Seyed Muhammad Hossein; Mirinezhad, S. Younes; Dezfoulian, Mir Hossein

doi:10.1109/aisp.2017.8515125

Cited by 5 publications

(1 citation statement)

References 16 publications

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“…Also system is almost fast and can work in real time purposes too. It is suggested to use other evolutionary algorithms for edge detection purposes such as ABC [45], Bat algorithm [46], PSO [47], GGO [48] and ICA [49], which may perform better in that way.…”

Section: Conclusion Discussion and Suggestionsmentioning

confidence: 99%

Analysis of a robust edge detection system in different color spaces using color and depth images

2019

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Edge detection is very important technique to reveal significant areas in the digital image, which could aids the feature extraction techniques. In fact it is possible to remove un-necessary parts from image, using edge detection. A lot of edge detection techniques has been made already, but we propose a robust evolutionary based system to extract the vital parts of the image. System is based on a lot of pre and post-processing techniques such as filters and morphological operations, and applying modified Ant Colony Optimization edge detection method to the image. The main goal is to test the system on different color spaces, and calculate the system's performance. Another novel aspect of the research is using depth images along with color ones, which depth data is acquired by Kinect V.2 in validation part, to understand edge detection concept better in depth data. System is going to be tested with 10 benchmark test images for color and 5 images for depth format, and validate using 7 Image Quality Assessment factors such as Peak Signal-to-Noise Ratio, Mean Squared Error, Structural Similarity and more (mostly related to edges) for prove, in different color spaces and compared with other famous edge detection methods in same condition. Also for evaluating the robustness of the system, some types of noises such as Gaussian, Salt and pepper, Poisson and Speckle are added to images, to shows proposed system power in any condition. The goal is reaching to best edges possible and to do this, more computation is needed, which increases run time computation just a bit more. But with today's systems this time is decreased to minimum, which is worth it to make such a system. Acquired results are so promising and satisfactory in compare with other methods available in validation section of the paper..Keywords: Edge detection, ant colony optimization (ACO), color spaces, depth image, kinect V.2, image quality assessment (IQA), image noises.Citation: Mousavi SMH, Lyashenko V, Prasath VBS. Analysis of a robust edge detection system in different color spaces using color and depth images.

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Section: Conclusion Discussion and Suggestionsmentioning

confidence: 99%