Segmentation Images Using Improved Genetic Algorithm


  • Kailasam Leelavathi 1Department of Computer Science, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India
  • T. Sudha Department of Computer Science, Sri Padmavati Mahila Visva Vidhyalayam, Tirupati, Andhra Pradesh, India



Segmentation Images, Hyper-spectral Imaging, Aerial Photography, Satellite Image Segmentation


With the expanding openness to new advancements, the principle issues in locale acknowledgment of remote detecting pictures are: (1) arrangement techniques are reliant on the division quality; and (2) the choice of delegate tests for preparing. The significant test is that the examples shown by the client are not in every case enough to characterize the best division scale. Besides, the sign of tests can be expensive, since it regularly requires visiting considered places in loco. The choice of delegate tests, then again, was bolstered in this work by the improvement of another intelligent characterization approach based on dynamic learning. Critical commitments were likewise acquired concerning the depiction of areas in remote detecting pictures by methods for: an assessment investigation of 19 descriptors; and two new methodologies for accelerating highlight extraction from a progressive system of sectioned districts.


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How to Cite

Leelavathi, K., & Sudha, T. (2019). Segmentation Images Using Improved Genetic Algorithm. Asian Journal of Computer Science and Technology, 8(S3), 81–84.

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