基于形状的果业木沙种检测方法

M. Senthilarasi, S. M. Roomi, M. Prasanna
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引用次数: 2

摘要

农产品出口行业为印度经济创造了大量的收入。在水果行业中,香蕉、芒果、苹果、石榴等各种水果在输送机中运输,进行分类、分选、分级、榨汁等采收后工序。人工对各种水果进行判别费时,可实现自动化。本研究旨在建立一种图像处理算法,确保香蕉(芭蕉属)与柑橘、苹果、石榴等其他水果的自动区分。采用背景减法和阈值法对输入对象进行分割。形态学操作可获得被分割对象的清晰轮廓。香蕉和非香蕉的形状用尺度和平移不变签名来描述。基于特征向量的二值支持向量机自动从非香蕉果实中检测出香蕉果实。准确率为95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Shape based approach for detecting Musa Species in fruit industry
Agro export industries generate a substantial amount of revenue to Indian economy. In the fruit industry, various fruits like banana, mango, apple and pomegranate, etc. are transported in the conveyor for a post harvest process like classification, sorting, grading and juice extraction. The manual discrimination of various fruits consumes time and, it can be automated. This research work is intended to build an image processing algorithm that ensures automatic discrimination of banana (Musa Species.) from other fruits like Citrus, Apple, and Pomegranate. The input object is segmented using Background subtraction and threshold method. Morphological operations are performed to obtain the clear contour of the segmented objects. The shape of the banana and non-banana are described by scale and translation invariant signature. Binary SVM with signature feature vectors detect the banana fruit from the non-banana fruit automatically. The accuracy rate is 95%.
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