基于分形特征的水果缺陷快速识别方法

计算机视觉和图象处理技术在水果自动分选和分级中起着重要的作用。因为缺陷检测的复杂性，水果表面缺陷的快速检测和识别一直是水果自动化分选和分级的障碍。在实数域分形盒维数计算方法的基础上，提出了双金字塔数据形式的盒维数快速计算方法。对于待识别水果图象的可疑缺陷区，提出用５个分形维数作为描述该区域粗糙度和纹理方向性的特征参数，并用所提出的快速计算方法进行计算，然后利用人工神经网络（ＢＰ）作为模式识别器，区

A Fast Identification Method for Fruit Surface DefectBased on Fractal Characters

Computer vision and image processing techniques have been found increasingly useful for the fruit automatic quality inspection and defect sorting operation. However, real time fruit surface defect inspection and recognition is still a challenging project due to its complexity. In this paper, a fast approach for box dimension estimation based on a dual pyramid data structure is developed. Utilizing traditional fractal dimension and 4 oriented fractal dimensions as input values, a BP neural network is designed for identifying fruit defect area and stem, calyx concave area. The results of experiment show that the approach is effective for real time defect identification and is accurate. The rate of correct classification is 93% and the executing time of microcomputer for recognition of one undefined blob on the surface of apple is 4~7ms.