Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/340027
Title: An heuristic cloud based segmentation technique using edge and texture components in two dimensional entropy
Researcher: Jaganathan, M
Guide(s): Sabari, A
Keywords: Two dimensional entropy
Edge and texture
Cloud computing
University: Anna University
Completed Date: 2019
Abstract: One Of The Most Research Topics In The Current Day Is Cloud Computing. This Technology Provides Users With Several Features. In Image Processing Applications There Are Many Approaches Which Requires Large Computation Or Storage Is Required, The Technology Cloud Computing Is Advantageous. A Very Popular Technology In Image Processing And Analysis Technology Is Edge Detection Technology Which Is Now Being Applied Across Fields Such As Pattern Recognition, Image Enhancement, Image Segmentation, Feature Description And The Other Image Analysis And Processing Fields. The Edge Detection Will Localize The Objects And Their Boundaries Within An Image Which Is A Basis For Various Image Analysis And The Applications Of Machine Vision. There Are Conventional Approaches To Edge Detection Which Are Expensive In Terms Of Computation As Each Set Of Such Operations Are Conducted For Every Pixel. In Case Of Some Approaches That Are Conventional The Time Taken For Computation Will Increase With The Image Size. The Edge Detection Is Used Extensively In Case Of Image Segmentation Of The Medical Images. A Nature-Inspired Computation Has An Attention In The Recent Decades And Most Of Such Popular Existing Algorithms Are The Evolutionary Algorithms (Eas) And The Swarm Intelligence (SI). In These Existing Works, Proposes TheGenetic Algorithm (GA), Ant Colony Optimization (ACO) And Glowworm Swarm Optimization (GSO) Based Edge And Texture Segmentation Methods Are Discussed. The GA, Have Been Inspired With The Natural Selection And Also The Survival Of The Fittest. First Initialization Is Taken On Given Data Set. Second Step Is Fitness To Find Survival Points. Third Is Selection Of Area Which Is Proportional To Fitness Value. Finally Crossover And Fitness Is Applied To Fetch Better Results. The ACO Based Algorithms For Evolution Which Is A Mechanism For Positive Feedback Having Advantages In The Concepts Of Parallelism, Robustness Along With An Easy Combination Of One Or Two Methods With The Rest Of The Methods. newline
Pagination: xvii,136 p..
URI: http://hdl.handle.net/10603/340027
Appears in Departments:Faculty of Information and Communication Engineering

Files in This Item:
File Description SizeFormat 
01_title.pdfAttached File24.86 kBAdobe PDFView/Open
02_certificates.pdf97.8 kBAdobe PDFView/Open
03_vivaproceedings.pdf155.5 kBAdobe PDFView/Open
04_bonafidecertificate.pdf112.07 kBAdobe PDFView/Open
05_abstracts.pdf344.68 kBAdobe PDFView/Open
06_acknowledgements.pdf119.33 kBAdobe PDFView/Open
07_contents.pdf430.09 kBAdobe PDFView/Open
08_listoftables.pdf88.28 kBAdobe PDFView/Open
09_listoffigures.pdf89.8 kBAdobe PDFView/Open
10_listofabbreviations.pdf354.39 kBAdobe PDFView/Open
11_chapter1.pdf3.51 MBAdobe PDFView/Open
12_chapter2.pdf3.51 MBAdobe PDFView/Open
13_chapter3.pdf5.78 MBAdobe PDFView/Open
14_chapter4.pdf2.57 MBAdobe PDFView/Open
15_chapter5.pdf1.61 MBAdobe PDFView/Open
16_conclusion.pdf273.49 kBAdobe PDFView/Open
17_references.pdf1.69 MBAdobe PDFView/Open
18_listofpublications.pdf209.64 kBAdobe PDFView/Open
80_recommendation.pdf56.29 kBAdobe PDFView/Open
Show full item record


Items in Shodhganga are licensed under Creative Commons Licence Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0).

Altmetric Badge: