Effects of classification methods on color-based feature detection with food processing applications
Effects of classification methods on color-based feature detection with food processing applications
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DOI：
10.1109/tase.2006.874972
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发表时间：
2007-01-01
影响因子：
5.6
通讯作者：
Daley, Wayne
中科院分区：
文献类型：
--
作者：
Lee, Kok-Meng;Li, Qiang;Daley, Wayne
颜色信息在基于视觉的特征检测中非常有用，特别是对于食品加工应用，其中颜色可变性通常会使基于灰度的机器视觉算法难以或不可能使用。本文提出了一种由两部分组成的彩色机器视觉算法。第一种方法是创建一个人工颜色对比作为预过滤器，旨在突出目标，同时抑制其周围环境。第二种方法是基于统计的快速有界盒（SFBB），它利用主成分分析技术从一组训练数据中对颜色空间中的目标特征进行表征，从而准确有效地进行颜色分类。我们。在食品加工应用的背景下评估算法，并通过将提出的解决方案与两种常用的颜色分类算法进行比较，检查颜色表征对计算效率的影响；神经网络分类器与支持向量机。三种方法的比较表明，基于统计的快速有界盒相对容易训练，效率高；因为有足够的训练数据，所以它是有效的，不需要任何额外的优化步骤；这些优点使SFBB成为涉及活体和/或自然物体的高速自动化的理想选择。从业人员注意：自然物体的可变性通常比制成品高出几个数量级，这仍然是一个挑战。因此，今天大多数天然产品检验问题的解决方案仍然让人类参与其中。影响彩色机器视觉检测目标成功率的因素之一是其对颜色的表征能力。当不相关的特征在颜色空间中非常接近目标时，这对经验丰富的操作员来说可能不会构成重大问题，但它们会以噪声的形式出现，往往会导致错误检测。本文举例说明了该算法在食品加工应用中具有代表性的自动化问题的适用性。实验表明，人工颜色对比和基于统计的快速有界盒方法通过降低目标像素和噪声像素的标准差，扩大特征簇在颜色空间中的分离，更紧密地表征特征颜色与背景的关系，可以显著提高检测成功率。本文提出的算法有几个优点，包括训练简单和快速分类，因为只执行了三次简单的矩形边界检查。
Color information is useful in vision-based feature detection, particularly for food processing applications where color variability often renders grayscale-based machine-vision algorithms that are difficult or impossible to work with. This paper presents a color machine vision algorithm that consists of two components. The first creates an artificial color contrast as a prefilter that aims at highlighting the target while suppressing its surroundings. The second, referred to here as the statistically based fast bounded box (SFBB), utilizes the principal component analysis technique to characterize target features in color space from a set of training data so that the color classification can be performed accurately and efficiently. We. evaluate the algorithm in the context of food processing applications and examine the effects of the color characterization on computational efficiency by comparing the proposed solution against two commonly used color classification algorithms; a neural-network classifier and the support vector machine. Comparison among the three methods demonstrates that statistically based fast bounded box is relatively easy to train, efficient; and effective since with sufficient training data, it does not require any additional optimization steps; these advantages make SFBB an ideal candidate for high-speed automation involving live and/or natural objects.Note to Practitioners-Variability in natural objects is usually several orders of magnitude higher than that for manufactured goods and has remained a challenge. As a result, most solutions to inspection problems of natural products today still have humans in the loop. One of the factors influencing the success rate of color machine vision in detecting a target is its ability to characterize colors. When unrelated features are very close to the target in the color space, which may not pose a significant problem to an experienced operator, they appear as noise and often result in false detection. This paper illustrates the applicability of the algorithm with a number of representative automation problems in the context of food processing applications. As demonstrated experimentally, the artificial color contrast and statistically based fast bounded box methods can significantly improve the success rate of the detection by reducing the standard deviation of both the target and noise pixels, enlarging the separation between feature clusters in color space, and more tightly characterize the feature color from its background. The algorithm presented here has several advantages, including simplicity in training and fast classification, since only three simple checks of rectangular bounds are performed.