An adaptive image processing system based on incremental learning for industrial applications

Yongheng Wang, M. Weyrich
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引用次数: 7

Abstract

Machine learning has been applied in image processing system for object recognition, inspection and measurement. It assumes that the provided training objects are representative enough to the real objects. However in real application, new (unlearned) objects always emerge over time, which may deviate from the trained (learned) objects. The conventional image processing system using machine learning is not able to learn and then recognize these new objects. In this paper, an incremental learning based image processing system is presented. The overall system consists of three layers: execution, learning and user. The conventional image processing system is constructed in execution layer. In learning layer, adviser and incremental learning are applied to generate a new classifier. The incremental learning is differentiated into different methodologies: data accumulation and ensemble learning. Through the adviser, a proper methodology can be recommended. User is able to interact with the system via user layer. Comparing to the conventional image processing system, the proposed system is robust in industrial applications, since it deals with the classification problems dynamically.
基于增量学习的自适应图像处理系统
机器学习已被应用于物体识别、检测和测量的图像处理系统中。它假定所提供的训练对象对真实对象具有足够的代表性。然而,在实际应用中,新的(未学习的)对象总是随着时间的推移而出现,这些对象可能会偏离训练过的(学习过的)对象。使用机器学习的传统图像处理系统无法学习并识别这些新物体。本文提出了一种基于增量学习的图像处理系统。整个系统由执行层、学习层和用户层组成。传统的图像处理系统是在执行层构建的。在学习层,采用顾问学习和增量学习生成新的分类器。增量学习分为数据积累和集成学习两种方法。通过顾问,可以推荐适当的方法。用户可以通过用户层与系统进行交互。与传统的图像处理系统相比,该系统动态处理分类问题,具有较强的鲁棒性。
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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