Fuzzy methods for automated inspection of food products

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI:10.1109/NAFIPS.1999.781826

V. Davidson, T. Chu, J. Ryks

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引用次数: 13

Abstract

Automated product inspection is of considerable interest to food manufacturers since human inspectors currently perform a substantial amount of on-line inspection. At a low-level of information processing, machine vision offers advantages of objective and consistent assessment. However machine vision systems are frequently used for grading and quality control. In these applications, it is necessary to integrate a number of physical features to make an inference about overall quality that is consistent with consumer judgements. The work presented in this paper focuses on quality assessment of chocolate chip cookies based solely on visual features. Digital images were used to define physical characteristics of cookies produced on a commercial bakery line. Consumers were asked to rate typical cookies on a line scale. A number of fuzzy systems were developed to make quality control decisions based on features extracted from digital images. Results from two fuzzy systems are compared to consumer results from a validation test.

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食品自动检验的模糊方法

自动化产品检测是食品制造商非常感兴趣的，因为人类检查员目前执行大量的在线检测。在信息处理的底层，机器视觉提供了客观一致的评估优势。然而，机器视觉系统经常用于分级和质量控制。在这些应用中，有必要集成许多物理特征，以对与消费者判断一致的总体质量做出推断。本文提出的工作重点是基于视觉特征的巧克力片饼干的质量评估。使用数字图像来定义商业面包店生产线上生产的饼干的物理特性。消费者被要求对典型的饼干进行评分。基于从数字图像中提取的特征，开发了许多模糊系统来进行质量控制决策。将两个模糊系统的结果与验证测试的消费者结果进行比较。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)

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