TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computers and Electronics in Agriculture
Identifying defects in images of rotating apples
Computers and Electronics in Agriculture
Identification of citrus disease using color texture features and discriminant analysis
Computers and Electronics in Agriculture
Automatic fruit and vegetable classification from images
Computers and Electronics in Agriculture
In-line detection of apple defects using three color cameras system
Computers and Electronics in Agriculture
Automated strawberry grading system based on image processing
Computers and Electronics in Agriculture
Advanced Data Mining Techniques
Advanced Data Mining Techniques
Defect detection in random colour textures using the MIA t2 defect maps
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Computers and Electronics in Agriculture
Automatic recognition of quarantine citrus diseases
Expert Systems with Applications: An International Journal
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One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T^2 statistic. In addition, a multiresolution scheme is introduced in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for the task of citrus inspection.