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Research Article Open access CC BY 3.0

Color and Texture Information Processing to Improve Storage Beans

Ngatchou Alban, Bitjoka Laurent, Boukar Ousman, Tonye Emmanuel

Current Journal of Applied Science and Technology · pp. 96–111 · Published 27 Mar 2012

10.9734/BJAST/2012/796

Abstract

Aims: This paper attempts to improve automatic temporal change detection on a pair of beans images, acquired before and after storage under high temperature (≥ 25°C) and high relative humidity (≥ 65%), conditions that promote « Hard-To-Cook » phenomenon. Study Design: Image processing, Hard-To-Cook beans. Place and Duration of Study: Laboratory of Modelisation, Image Processing and Applications Research (MOTRIMA) Department of Electrical Engineering Energetic and Automatics, Laboratory of Biophysics and Food Biochemistry Department of Food Science and Nutrition of National School of Agro-Industrial Sciences (University of Ngaoundéré, Cameroon), Institute of Agricultural Research for Development (IRAD) between August 2009 and March 2010. Methodology: We want to get a robust extracting seed in acquired images and a good dissimilarity parameter for temporal change detection on a pair of textured images. To reach this goal, we analyze the characterization of textural properties and space color which are more relevant to textured beans seeds. We use wavelet transform and apply fuzzy logic segmentation. We define a confidence limit for the dissimilarity parameter before analyzing its evolution during storage of beans seeds. Finally we correlate this parameter with another Hard-To-Cook indicator. Results: After many tests, Daubechies 2(db2) wavelet family in RGB space allowed best extracting beans seeds in scene with fuzzy-c-means segmentation. The global intensity variation was a pertinent parameter for dissimilarity detection between two images. We obtained highly correlation between this parameter and cooking times beans (-0.96; -0.88; -0.72 respectively in Red, Green and Blue color space). Conclusion: The global intensity variation in red color space allowed the determination level of browning beans seeds as indicator of their Hard-To-Cook degree.

Texture and color image processing wavelet transform global intensity variation ECA PAN 019 beans (Phaseolus vulgaris) hard-to-cook fuzzy logic image segmentation

References (13)

  1. 1 A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data [DOI]
  2. 2 Color and texture descriptors [DOI]
  3. 3 Comparison between Mallat's and the ‘à trous’ discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images [DOI]
  4. 4 A REVIEW OF TEXTURAL DEFECTS IN COOKED RECONSTITUTED LEGUMES ?THE INFLUENCE OF STRUCTURE AND COMPOSITION [DOI]
  5. 5 Digital camera images processing of hard-to-cook beans [DOI]
  6. 6 The cookability of yellow peas. A colloid-chemical and biochemical study.
  7. 7 Motion estimation via cluster matching [DOI]
  8. 8 Modulation models for image processing and wavelet-based image demodulation [DOI]
  9. 9 Effects of minerals and apparent phytase activity in the development of the hard-to-cook state of beans
  10. 10 AUTOMATION OF A MATTSON BEAN COOKER FOR TESTING THE HARD-TO-COOK DEFECT IN LEGUME SEEDS [DOI]
  11. 11 PC-Based Instrumentation System for the Study of Bean Cooking Kinetic [DOI]
  12. 12 Détection multirésolution des changements par analyse spectrale locale de Walsh-Hadamard
  13. 13 Change Detection and Analysis in Multi-Spectral Images, [DOI]

Cited by 15

Quality Inspection of Bag Packaging Red Beans (Phaseolus vulgaris) Using Fuzzy Clustering Algorithm

Ngatchou Alban, Bitjoka Laurent, Yemefack Martin, Boukar Ousman · Journal of Advances in Mathematics and Computer Science · 2014

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Genetic dissection of seed appearance quality using recombinant inbred lines in soybean

Quan Hu, Yanwei Zhang, Ruirui Ma · Molecular Breeding · 2021

Evaluation of optimization techniques with support vector machine for identification of dry beans

Nabin Kumar Naik, Prabira Kumar Sethy, Rajat Amat · Indonesian Journal of Electrical Engineering and Computer Science · 2023

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