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Title: Computer vision based detection and identification of potato blemishes
Author: Barnes, Michael
ISNI:       0000 0004 5351 1306
Awarding Body: University of Lincoln
Current Institution: University of Lincoln
Date of Award: 2014
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This thesis addresses the problem of automatic detection and identification of blemishes in digital images of potatoes. Potatoes are an important food crop, with clear unblemished skin being the main factor affecting consumer preference. Potatoes with defects, diseases and blemishes caused by otherwise benign (to human) skin infections, are strongly avoided by consumers. Most potatoes are sorted into dfferent grades by hand, with inevitable mistakes and losses. The currently deployed computer vision systems for sorting potatoes require manual training and have limited accuracy and high unit costs. A further limitation of typical machine vision systems is that the set of image features for pattern recognition has to be designed by the system engineer to work with a specific configuration of produce, imaging system and operating conditions. Such systems typically do not generalise well to other configurations, where the required image features may well differ from those used to design the original system. The objective of the research presented in this thesis is to introduce an automatic method for detecting and identifying blemishes in digital images of potatoes, where the presented solution involves classifying individual pixels. A human expert is required to mark up areas of blemishes and non-blemishes in a set of training images. For blemish detection, each pixel is classified as either blemish or non-blemish. For blemish identification, each pixel is classified according to a number of pre-determined blemish categories. After training, the system should be able to classify individual pixels in new images of previously unseen potatoes with high accuracy. After segmenting the potato from the image background, 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. The features include statistical summaries of the whole potato and local regions centred on each pixel as well as the data of the pixel itself. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes. The AdaBoost algorithm (Freund and Schapire, 1999) is used to build a classifier, which combines results from so-called "weak" classifiers, each constructed using one of the candidate features, into one "strong" classifier that performs better than any of the weak classifiers alone. 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. For blemish detection, the classifier was trained using a subset of pixels which had been marked as blemish or non-blemish. Tests were done with the full set of features, "lesion experiments" were carried out to explore the impact of removing specific feature types, and experiments were also carried out on methods of speeding up classification both by restricting the number of weak classifiers and restricting the numbers of unique candidate features which can be used to produce weak classifiers. The results were highly accurate with visible examples of disagreement between classifier output and markup being caused by human inaccuracies in the markup rather than classifier inaccuracy. For blemish identification, a set of classifiers were trained on subsets of pixels marked as each blemish class against a subset of pixels drawn from all other classes. For classification, each pixel was tested with all classifiers and assigned to the classifier which returned the highest confidence of a positive result. Experiments were again performed with methods of speeding up classification as well as lesion experiments. Finally, to demonstrate how the system would work in an industrial context, the classification results were summarised for each potato, providing a high overall accuracy in detecting the presence or absence of significant blemish coverage for each blemish type.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: G740 Computer Vision