Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/470290
Title: Classification of Fruit Images Using Optimal Features Extracted By Soft Computing Techniques
Researcher: HARMANDEEP SINGH
Guide(s): Baljit Singh Khehra
Keywords: Computer Science
Computer Science Artificial Intelligence
Engineering and Technology
University: I. K. Gujral Punjab Technical University
Completed Date: 2019
Abstract: newlineFruit classification is found to be one of the emerging areas in computer vision and newlineclassification. The accuracy of the classification system depends on the quality of acquired newlineimages, the number of features, types of features, selection of optimal features newlinefrom extracted features and type of classifier used. Images taken in poor environment newlineconditions decrease the visibility and hidden information details of digital images. newlineTherefore, image enhancement techniques are necessary for improving the significant newlinedetails of these images. This thesis provides a review to highlight the recent newlineprogress of image enhancement techniques for improving the visibility of fruit images. newlineIn this research work, a new Type-II Fuzzy based image enhancement technique is newlinedesigned to improve the quality of visibly weather degraded fruit images. The Type-II newlineFuzzy logic can automatically extract the local atmospheric light and roughly eliminate newlinethe atmospheric veil in local detail enhancement.
Pagination: All pages
URI: http://hdl.handle.net/10603/470290
Appears in Departments:Department of Computer Applications

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01_title.pdfAttached File59.9 kBAdobe PDFView/Open
02_prelim page.pdf97.59 kBAdobe PDFView/Open
03_content.pdf42.28 kBAdobe PDFView/Open
04_abstract.pdf100.81 kBAdobe PDFView/Open
05_chapter1.pdf1.26 MBAdobe PDFView/Open
06_chapter2.pdf148.54 kBAdobe PDFView/Open
07_chapter3.pdf323.32 kBAdobe PDFView/Open
08_chapter4.pdf3.21 MBAdobe PDFView/Open
09_chapter5.pdf616.36 kBAdobe PDFView/Open
10_annexure.pdf93.57 kBAdobe PDFView/Open
80_recommendation.pdf114.74 kBAdobe PDFView/Open
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