摘要
虾仁细化曲线长度是虾仁质量预测的一个重要因素,为了提取虾仁细化曲线长度,对机器视觉所采集的白虾虾仁图像进行形态学处理,减少细化处理后细化图像曲线的分支,经过对细化曲线每个像素为中心的3×3邻域分析,判断出细化曲线的分支,提出一种端点擦除方法去除分支,通过平均值补偿方法还原虾仁细化曲线并计算其长度。经过计算发现,虾仁细化曲线长度与体重函数关系的相关系数为0.894,虾仁面积与体重关系模型的相关系数为0.939,虾仁细化曲线长度、虾仁面积与体重关系模型的相关系数为0.954,可见虾仁细化曲线长度提高了虾仁体重预测的精度。
Refinement curve length of shrimp is an important factor in its weight prediction.Morphological filter was required to reduce branches of the refinement curve in order to get this value.The existence of branches was correctly judged by 3×3 neighborhood analysis of every pixel on the refinement curve.Then the length of refinement curves which contain branches could be calculated by erasing the endpoints to remove branches and average compensation method.The conclusion showed that the correlation was obvious,with the correlation coefficient of 0.894 between its refinement curve length and weight,0.939 between the size and weight,and prediction correlation coefficient of 0.954 between comprehensive details both of the length and size of shrimp and the weight after modeling.The results indicate that it＇s promising to increase the predicting accuracy with refinement curve length.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2009年第3期172-175,共4页
Transactions of the Chinese Society for Agricultural Machinery
关键词
虾仁
机器视觉
图像细化
分支
体重
Shrimp
Computer vision
Refinement
Branch
Weight
