Counting touching wheat grains in images based on elliptical approximation.

IF 0.9 Q3 AGRICULTURE, MULTIDISCIPLINARY
D R Avzalov, E G Komyshev, D A Afonnikov
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引用次数: 0

Abstract

The number of grains of a cereal plant characterizes its yield, while grain size and shape are closely related to its weight. To estimate the number of grains, their shape and size, digital image analysis is now generally used. The grains in such images may be completely separated, touching or densely packed. In the first case, the simplest binarization/segmentation algorithms, such as the watershed algorithm, can achieve high accuracy in segmentation and counting grains in an image. However, in the case of touching grains, simple machine vision algorithms may lead to inaccuracies in determining the contours of individual grains. Therefore, methods for accurately determining the contours of individual grains when they are in contact are relevant. One approach is based on the search for pixels of the grain contact area, in particular, by identification of concave points on the grain contour boundary. However, some grains may have chips, depressions and bulges, which leads to the identification of the corner points that do not correspond to the grain contact region. Additional data processing is required to avoid these errors. In this paper, we propose an algorithm for the identification of wheat grains in an image and determine their boundaries in the case when they are touching. The algorithm is based on using a modification of the concave point search algorithm and utilizes a method of assigning contour boundary pixels to a single grain based on approximation of grain contours by ellipses. We have shown that the proposed algorithm can identify grains in the image more accurately compared to the algorithm without such approximation and the watershed algorithm. However, the time cost for such an algorithm is significant and grows rapidly with increasing number of grains and contours including multiple grains.

基于椭圆近似的图像触摸小麦粒计数。
谷类作物的粒数决定其产量,而粒的大小和形状则与其重量密切相关。为了估计颗粒的数量、形状和大小,现在一般使用数字图像分析。这些图像中的颗粒可能完全分离,接触或密集排列。在第一种情况下,最简单的二值化/分割算法,如分水岭算法,可以达到较高的分割精度和对图像中颗粒的计数。然而,在触摸颗粒的情况下,简单的机器视觉算法可能导致在确定单个颗粒的轮廓时不准确。因此,准确确定单个颗粒接触时的轮廓的方法是相关的。一种方法是基于颗粒接触区域像素的搜索,特别是通过识别颗粒轮廓边界上的凹点。然而,一些颗粒可能有切屑、凹陷和凸起,从而导致识别出与颗粒接触区域不对应的角点。需要额外的数据处理来避免这些错误。在本文中,我们提出了一种算法来识别图像中的小麦颗粒,并在它们接触的情况下确定它们的边界。该算法基于对凹点搜索算法的改进,并利用一种基于椭圆逼近颗粒轮廓的将轮廓边界像素分配给单个颗粒的方法。我们已经证明,与没有这种近似的算法和分水岭算法相比,所提出的算法可以更准确地识别图像中的颗粒。然而,该算法的时间成本显著,并且随着颗粒数量的增加和包含多个颗粒的轮廓的增加而快速增长。
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来源期刊
Vavilovskii Zhurnal Genetiki i Selektsii
Vavilovskii Zhurnal Genetiki i Selektsii AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
1.90
自引率
0.00%
发文量
119
审稿时长
8 weeks
期刊介绍: The "Vavilov Journal of genetics and breeding" publishes original research and review articles in all key areas of modern plant, animal and human genetics, genomics, bioinformatics and biotechnology. One of the main objectives of the journal is integration of theoretical and applied research in the field of genetics. Special attention is paid to the most topical areas in modern genetics dealing with global concerns such as food security and human health.
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