Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad

Ahmad, Khairul Adilah (2018) Edge detection and contour segmentation for fruit classification in natural environment / Khairul Adilah Ahmad. In: The Doctoral Research Abstracts. IGS Biannual Publication, 14 . Institute of Graduate Studies, UiTM, Shah Alam.

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Abstract

This thesis addresses the problem of automatic delineation and recognition of the images of Harumanis mangoes acquired in the natural environment. Harumanis is one of the main export produce in Pedis as it is very popular because of its deliciousness, sweetness and aromatic fragrance. In the agricultural industry, the fundamental factor for consistent marketing of the fruit is its quality. The quality of Harumanis is based on the shape and size of the fruits. The ability to efficiently and consistently manufacture high-quality products, and to ensure correct delineation and recognition processes, are the basis for success in the highly competitive fruit industry. Computer vision is a technology that imitates effects of human vision by electronically perceiving and understanding an object in the image. In fact, computer vision is gaining more attention in image-processing applications especially in the agricultural area. The technology involves several stages relating to image acquisition, pre-processing, segmentation, feature extraction and classification. The aim of this research is to assess of the Harumanis fruit quality in natural images. This research adapted a methodology of computer vision and algorithms that exploit image segmentation, feature extraction and fuzzy classification to guide the research activities. In general, image segmentation isolates an object from the images, feature extraction creates features for classification phase while object classification categorizes objects into the correct groups…

Item Type: Book Section
Creators:
CreatorsEmail
Ahmad, Khairul AdilahUNSPECIFIED
Subjects: Q Science > QA Mathematics > Instruments and machines
Divisions: Institut Pengajian Siswazah (IPSis) : Institute of Graduate Studies (IGS)
Series Name: IGS Biannual Publication
Volume: 14
Item ID: 22096
Uncontrolled Keywords: Abstract; Abstract of thesis; Newsletter; Research information; Doctoral graduates; IPSis; IGS; UiTM; Edge detection
Last Modified: 13 Nov 2018 08:23
Depositing User: Staf Pendigitalan 5
URI: http://ir.uitm.edu.my/id/eprint/22096

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