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Home / programming language / Online Matlab Projects / Identifying rice grains using image analysis and sparse-representation-based classification
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Matlab Project - DIP, final year projects.
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Identifying rice grains using image analysis and sparse-representation-based classification

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SKU: PROJ7547 Categories: 2017 Projects, Digital Image Processing Projects, Mobile Computing Projects, Online Matlab Projects Tags: Android Project, Asp.Net Project, C# Project, Computer Engineering Final Year Projects, Computer Science Final Year Projects, Electronics Projects Engineering Students Final Year, Engineering Student Project Ideas, Final Semester Projects, Final Year Project Center, Final Year Projects, ieee ECE Projects, ieee EEE Projects, ieee Final Year Projects, IEEE Projects, ieee Projects Networking, Image Processing Projects, J2EE Project, Java Project, Java Project Code, Matlab Project, PHP Project, Students Projects
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Identifying rice grains using image analysis and
sparse-representation-based classification

Abstract— Rice (Oryza sativa L.) is a major staple food worldwide, and is traded extensively. The objective of this study is to distinguish the rice grains of 30 varieties nondestructively using image processing and
sparse-representation-based classification (SRC). SRC uses over-complete bases to capture the representative traits of rice grains. In the experiments, rice grain images were acquired by microscopy. The morphological, color, and textural traits of the grain body, sterile lemmas, and brush were quantified. An SRC classifier was subsequently developed to identify the varieties of the grains using the traits as the inputs. The proposed approach could discriminate rice grain varieties with an accuracy of 89.1%.< final year projects >

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