EDGE DETECTION USING THE CO-OCCURRENCE MATRIX: AN APPLICATION TO THE SEGMENTATION OF COFFEE CHERRIES IMAGES

A coffee-fruits image segmentation system based on the analysis of textural features computed from the co-occurrence matrix is presented. 121 indicators are measured and those with highest discrimination between two classes 'Fruit Center' and 'Edge', are selected. Segmentation is performed using the edge image, looking for their arc-connected regions. The edge detection system is a Bayesian classifier with five indicators as inputs computed using a structural element, resulting in the partition of the image. The classifier's output indicates the belongingness to one of the two classes for a 4x4 region (structural element). In order to decrease computational burden, a thresholding-based edge detection system is proposed, using one indicator with high discrimination. Both systems reach a correct detection level higher than 90% at 50% of tolerance.