Motion Reconstruction using Moments Analysis
SIBGRAPI '04 Proceedings of the Computer Graphics and Image Processing, XVII Brazilian Symposium
Using Lateral Coupled Snakes for Modeling the Contours of Worms
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Computers and Electronics in Agriculture
Harris function based active contour external force for image segmentation
Pattern Recognition Letters
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The cucumber is one of the most important crops worldwide and, because it is generally consumed fresh, it must be classified into quality categories. The European classification system includes a parameter that relates the degree of curvature relative to the length. Until now, this classification could not been be achieved with an automatic system due to the difficulty associated with correctly calculating the axis of a cucumber. This article describes a computer algorithm that uses active contours or ''snakes'' to classify cucumbers by length and curvature. This algorithm demonstrates an advantage in the determination of the central line of each cucumber, based on an iterative process that is quick and carries out the classification process efficiently. The method was validated against human classification for 360cucumbers and was also compared with an ellipsoid approximation method. The active contour method reduced the classification error by 15% points, compared with the ellipsoid approximation method, to 1%, with no serious errors (i.e., misclassification of Class Extra and I into Class II or vice versa). Meanwhile, the ellipsoid approximation method led to a 16% error rate, of which 2% were serious errors (an error of two classes). The developed approach is applicable to fresh cucumber commercial classification lines to meet the requirements of the European regulations for cucumber classification.