Image processing and understanding based on the fuzzy inference approach

Grading of rice grains has gain attentions due its requirement of quality assessment during import or export. Rice grain quality depends on milling operation, where rice hull is removed with a huller system followed by whitening operation. In such process, adjustment of rollers, control, and operation is important in terms of quality of milled rice. Especially, the basmati rice needed more quality assurance as it is not parboiled rice and exported globally with a high product value. In this present work, the basic problem of quality assessment in rice industry is addressed with digital image processing based technique. Machine vision and digital image processing provide an alternative with the automated, nondestructive, cost-effective, and fast approach as compared with traditional method which is done manually by human inspectors. A model of quality grade testing and identification is built based on morphological features using digital image processing and knowledge based adaptive neuro-fuzzy inference system (ANFIS). The qualities of rice kernels are determined with the help of shape descriptors and geometric features using the sample images of milled rice. The adopted technique has been tested on a sufficient number of training images of basmati rice grain. The proposed method gives a promising result in an evaluation of rice quality with 100% classification accuracy for broken and whole grain. The milling efficiency is also assessed using the ratio between head rice and broken rice percentage and it is 77.27% for the test sample. The overall results of the adopted methodology are promising in terms of classification accuracy and efficiency.

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Image Processing and Pattern Recognition with Mamdani Interval Type-2 Fuzzy Inference Systems

Combining Experimentation and Theory - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-642-24666-1_13 ◽

2012 ◽

pp. 179-190 ◽

Cited By ~ 8

Author(s):

Patricia Melin

Keyword(s):

Image Processing ◽

Pattern Recognition ◽

Fuzzy Inference ◽

Fuzzy Inference Systems ◽

Interval Type ◽

Inference Systems

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Representing Visual Complexity of Images Using a 3D Feature Space Based on Structure, Noise, and Diversity

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2012.p0631 ◽

2012 ◽

Vol 16 (5) ◽

pp. 631-640 ◽

Cited By ~ 11

Author(s):

Phuc Q. Le ◽

◽

Abdullah M. Iliyasu ◽

Jesus A. Garcia Sanchez ◽

Fangyan Dong ◽

...

Keyword(s):

Image Processing ◽

Fuzzy Inference System ◽

Clustering Algorithm ◽

Fuzzy Inference ◽

Visual Complexity ◽

Feature Space ◽

Inference System ◽

Object Category ◽

Simple Medium

A 3D feature space is proposed to represent visual complexity of images based on Structure, Noise, and Diversity (SND) features that are extracted from the images. By representing images using the proposed feature space, the human classification of visual complexity of images as being simple, medium, or complex can be implied from the structure of the space. The structure of the SND space as determined by a clustering algorithm and a fuzzy inference system are then used to assign visual complexity labels and values to the images respectively. Experiments on Corel 1000A dataset, Web-crawled, and Caltech 256 object category dataset with 1000, 9907, and 30607 images respectively using MATLAB demonstrate the capability of the 3D feature space to effectively represent the visual complexity. The proposal provides a richer understanding about the visual complexity of images which has applications in evaluations to determine the capacity and feasibility of the images to tolerate image processing tasks such as watermarking and compression.

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Estimating Some Physical Properties of Sour and Sweet Cherries Based on Combined Image Processing and AI Techniques

International Journal of Food Engineering ◽

10.1515/ijfe-2014-0027 ◽

2014 ◽

Vol 10 (3) ◽

pp. 403-415 ◽

Cited By ~ 1

Author(s):

Saeedeh Taghadomi-Saberi ◽

Mahmoud Omid ◽

Zahra Emam-Djomeh

Keyword(s):

Image Processing ◽

Physical Properties ◽

Fuzzy Inference ◽

Back Propagation ◽

Storage Conditions ◽

Back Propagation Algorithm ◽

Inference System ◽

Sweet Cherries ◽

A Charge ◽

Adsorption Desorption

Abstract Physical properties of agricultural products are considered as important factors in optimization of storage conditions, packaging, transportation, water adsorption/desorption, heat, pesticides, and foodstuff moving out and also their breathing. This paper presents a time and cost economizing method to determine these important attributes of sour and sweet cherries by combining image processing and two common artificial intelligence techniques, artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS). The measuring technique consisted of a charge-coupled device camera for image acquisition, fluorescent illuminants, capture card, and MATLAB for image analysis. Several networks were designed, trained, and generalized with a back-propagation algorithm using “trainlm” as training function. Several ANFIS models were designed with different number and type of membership functions (MFs) for each input. Generally, “gaussian” and “pi-shaped” MFs showed better results for estimating output variables among others. Considering statistical analysis, ANFIS showed better results than ANN.

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