Color Based Identification and Classification of Boiled Food Grain Images
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Basvaraj S Anami
Texture and color are the important features used in identifying objects or regions of interest (ROI) in any image, be it a photomicrograph, an aerial photograph, or a satellite image. We propose a methodology for identification and classification of boiled food grains based on the level of boiling using two color models HSV and L*a*b*, in Indian context. These color models provide good texture definition for any image. The classification is performed at two levels: Level 1 determines the type of grain image and Level 2 estimates the amount of boiling, as full boiled, medium boiled and half boiled. Results show average accuracies of 80% and 96% for first level and 70% and 96% accuracies for second level classifications for HSV and L*a*b*, respectively. The work is applicable to automatic inspection of food preparations in food industry and monitoring, cooking, and serving food in restaurants, hotels and malls by service robots.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Artikel in diesem Heft
- Article
- Use of Cassava Peel as Carbon Source for Production of Amylolytic Enzymes by Aspergillus niveus
- Vapour Transport Mechanism in Osmotic Distillation Process
- Physical Properties of Rough Rice
- Effects of Different Pretreatments on Drying Kinetics and Quality of Sweet Potato Bars Undergoing Air Impingement Drying
- Selective Adsorption of Vitamin E from Palm Fatty Acid Distillate on Silica-Packed Fixed-Bed Columns
- Color Based Identification and Classification of Boiled Food Grain Images
- Critical Review
- Rapid Detection Techniques for Biological and Chemical Contamination in Food: A Review