Automatic image segmentation using sobel operator and k-means clustering: A case study in volume measurement system for food products

Abstract

Image segmentation is one of important step in visual inspection of food product using computer vision system. However, segmentation of food product image is not easily performed if the image has low contrast with its background or the background in acquired image is not homogeneous. This paper proposes k-means clustering combined with Sobel operator for automatic food product image segmentation. Sobel operator was used to determine region of interest (ROI) and k-means clustering was then employed to separate object and background in ROI. The area outside ROI was considered as background. The proposed method has been validated using 100 images of food product from ten different types. The validation results show that the proposed segmentation method achieves good segmentation result.