A METHOD FOR SEGMENTING FOOD COLOUR IMAGES

Abstract

In this note, a method for segmenting foods from their backgrounds in colour images is presented. The proposed method has three steps: i) determination of the optimal decision plane for the segmentation of an image; ii) coarse segmentation of the image; iii) morphological operations in order to correct possible errors in the segmented image. The method was implemented in MATLAB and tested on 40 colour images of foodstuff with very different morphological and chromatic characteristics, including meat, baked products, fruit and tubers. The experimental results are presented and the performance of method in the segmentation process is assessed. The method has shown to be both effective and efficient also for colour images with high spatial resolution.

Downloads

Download data is not yet available.
Published
2008-06-30
Section
Original Articles
Keywords:
colour images, food, image processing, segmentation.
Statistics
Abstract views: 765

PDF: 306
Share it

PlumX Metrics

PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

How to Cite
Peri, G., & Romaniello, R. (2008). A METHOD FOR SEGMENTING FOOD COLOUR IMAGES. Journal of Agricultural Engineering, 39(2), 53-56. https://doi.org/10.4081/jae.2008.2.53