Color Based Identification and Classification of Boiled Food Grain Images

Abstract

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.

Ekstraksi Fitur Tekstur dan Warna pada Kulit Katak Menggunakan GLCM dan Momen Warna

“…Energi dikatakan sebagai Angular Second Moment (ASM) merupakan fitur yang mempresentasikan ukuran dari sifat homogenitas citra digital yang dihitung menggunakan Persamaan 1 [12]:…”

“…Kontras memiliki nilai 0 (nol) untuk citra yang tetap. Untuk menghitung nilai kontras menggunakan Persamaan 2 [12].…”

“…Korelasi sering dipakai untuk menghitung mutasi bentuk, perpindahan dan gerakan pada suatu citra. Untuk menghitung nilai korelasi menggunakan Persamaan 3 [12].…”

Monitoring and grading of tea by computer vision – A review

“…Computer vision algorithms based on texture analysis and colour recognition have been reported as an efficient method of quality inspection and classification (Alfatni et al, 2008;Anami and Burkpalli, 2009). Colour is an important feature depending on which an image can be analyzed while texture analysis is one of the neighbourhood operations of digital images that distinguishes regularity in the visual appearance of local detail in an image.…”

Unified Approach in Food Quality Evaluation Using Machine Vision

“…[1] For bulk sugary products classification and identification developed by B. S. Anami et al (2009). [2]. they used gray level co-occurrence method for texture analysis and feature extraction and developed a neural network model to classify 10 different varieties of bulk sugary food products.…”