Stem and calyx recognition on ‘Jonagold’ apples by pattern recognition

Ünay, Devrim; Gosselin, Bernard

doi:10.1016/j.jfoodeng.2005.10.038

Cited by 84 publications

(37 citation statements)

References 16 publications

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“…to segment the object of interest from the background), features are extracted that summarise important qualities of the object, then a pattern recognition system is used to categorise the input data. For example, [1] developed methods to distinguish between blemishes in apples and healthy apples with visible stem or calyx. In [2], HSV-colour and texture histograms are used to classify different types of fruit and vegetables.…”

Section: Related Workmentioning

confidence: 99%

Minimalist AdaBoost for Blemish Identification in Potatoes

Barnes

Cielniak

Duckett

2010

Computer Vision and Graphics

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Abstract. We present a multi-class solution based on minimalist AdaBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to first reduce the feature set by selecting five features for each class, then train binary classifiers for each class, classifying each testing example according to the binary classifier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry defined criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively.

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Section: Related Workmentioning

confidence: 99%

Minimalist AdaBoost for Blemish Identification in Potatoes

Barnes

Cielniak

Duckett

2010

Computer Vision and Graphics

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“…They also used thresholding to detect ingredients such as chives, and developed methods to measure the distribution and quantity of the detected ingredients. [5] developed methods to distinguish between blemishes in apples and healthy apples with visible stem or calyx. Images were recorded using special filters to restrict the observed light frequencies, then various features including statistical moments and shape features were used for pattern recognition.…”

Section: B Related Workmentioning

confidence: 99%

“…An alternative solution would be to use the HSV colour space but this would create an additional processing overhead including colour conversion and calculation of circular statistics. Other systems use more complex hardware set-ups such as customised lighting, as is an option for the Maf-Roda Agrobotic [10] or using specific colour filters like in [5]. The code for the system software was implemented in MATLAB.…”

Section: Feature Extractionmentioning

confidence: 99%

“…It has been used previously in the classification of apples to avoid falsely classifying apple features as blemishes [5]. The high performance of the final strong classifier is due to the emphasis put on the training examples which are most difficult to classify during the learning process.…”

Section: Adaboostmentioning

confidence: 99%

“…AdaBoost has been implemented on a range of tasks, including face detection [12] but also in food quality control systems, such as [5].…”

Section: Adaboostmentioning

confidence: 99%

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Boosting minimalist classifiers for blemish detection in potatoes

Barnes

Duckett

Cielniak

2009

2009 24th International Conference Image and Vision Computing New Zealand

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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. 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. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and nonblemishes. 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. The results show that the method is able to build "minimalist" classifiers that optimise detection performance at low computational cost. In experiments, minimalist blemish detectors were trained for both white and red potato varieties, achieving 89.6% and 89.5% accuracy respectively.

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