Location of Repository

Visual detection of blemishes in potatoes using minimalist boosted classifiers

By Michael Barnes, Tom Duckett, Grzegorz Cielniak, Graeme Stroud and Glyn Harper

Abstract

This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image.\ud A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted.\ud Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes.\ud With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc.\ud The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost.\ud In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively

Topics: G400 Computer Science, D610 Food Science, G740 Computer Vision
Publisher: Elsevier
Year: 2010
DOI identifier: 10.1016/j.jfoodeng.2010.01.010
OAI identifier: oai:eprints.lincoln.ac.uk:2206

Suggested articles

Preview

Citations

  1. (2007). A computer vision system for appearance-based descriptive sensory evaluation of meals. doi
  2. (1999). A short introduction to boosting.
  3. (2005). A statistical approach to texture classi from single images. doi
  4. (1996). An automated inspection station for machine-cision grading of potatoes. doi
  5. (2008). An experimental machine vision system for sorting sweet taramind. doi
  6. (1998). An improvement of AdaBoost to avoid over In: doi
  7. (2009). Automatic detecting and grading method of potatoes with computer vision.
  8. (1999). Defect and disease detection in potato tubers. In: doi
  9. (1995). Fourier-based separation technique for shape grading of potatoes using machine vision. doi
  10. (2006). GML AdaBoost MATLAB Toolbox. URL http://research.graphicon.ru
  11. (2005). Guiding model search using segmentation. In: doi
  12. (2000). Highspeed potato grading and quality inspection based on a color vision system. In: doi
  13. (1998). Improved boosting algorithms using con predictions. In: doi
  14. (2005). Improved diagnosis of powdery scab (spongospora subterranea f.sp. subterranea) symptoms on potato tubers (solanum tuberosum l.). doi
  15. (2007). Inspection of the distribution and amount of ingredients in pasteurized cheese by computer vision. doi
  16. (1995). Machine vision for color inspection of potatoes and apples. doi
  17. (2008). Meeting with representatives of R.J.
  18. (2005). Omni-directional face detection based on Real AdaBoost. doi
  19. (2002). Rotation-invariant pattern matching with colourring projection. doi
  20. (2006). Stem and calyx recognition on 'jonagold' apples by pattern recognition. doi
  21. (1996). Veggievision: A produce recognition system. In: doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.