Image Segmentation for Food Quality Evaluation Using Computer Vision System

Quality evaluation is an important factor in food processing industries using the computer vision system where human inspection systems provide high variability. In many countries food processing industries aims at producing defect free food materials to the consumers. Human evaluation techniques suffer from high labour costs, inconsistency and variability. Thus this paper provides various steps for identifying defects in the food material using the computer vision systems. Various steps in computer vision system are image acquisition, Preprocessing, image segmentation, feature identification and classification. The proposed framework provides the comparison of various filters where the hybrid median filter was selected as the filter with the high PSNR value and is used in preprocessing. Image segmentation techniques such as Colour based binary Image segmentation, Particle swarm optimization are compared and image segmentation parameters such as accuracy, sensitivity , specificity are calculated and found that colour based binary image segmentation is well suited for food quality evaluation. Finally this paper provides an efficient method for identifying the defected parts in food materials.