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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine
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Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

机译：使用基于内核PCA的特征融合和基于内核学习的支持向量机的自动机器人组件的低分辨率触觉图像识别

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摘要

In this paper, we propose a robust tactile sensing image recognition scheme for automatic robotic assembly. First, an image reprocessing procedure is designed to enhance the contrast of the tactile image. In the second layer, geometric features and Fourier descriptors are extracted from the image. Then, kernel principal component analysis (kernel PCA) is applied to transform the features into ones with better discriminating ability, which is the kernel PCA-based feature fusion. The transformed features are fed into the third layer for classification. In this paper, we design a classifier by combining the multiple kernel learning (MKL) algorithm and support vector machine (SVM). We also design and implement a tactile sensing array consisting of 10-by-10 sensing elements. Experimental results, carried out on real tactile images acquired by the designed tactile sensing array, show that the kernel PCA-based feature fusion can significantly improve the discriminating performance of the geometric features and Fourier descriptors. Also, the designed MKL-SVM outperforms the regular SVM in terms of recognition accuracy. The proposed recognition scheme is able to achieve a high recognition rate of over 85% for the classification of 12 commonly used metal parts in industrial applications.
机译：在本文中，我们提出了一种用于自动机器人组件的稳健触觉感测图像识别方案。首先，设计图像再处理程序以增强触觉图像的对比度。在第二层中，从图像中提取几何特征和傅立叶描述符。然后，应用内核主成分分析（内核PCA）以将功能转换为具有更好辨别能力的特征，这是基于内核PCA的特征融合。将转换的特征送入第三层以进行分类。在本文中，我们通过组合多个内核学习（MKL）算法和支持向量机（SVM）来设计分类器。我们还设计并实现由10×10感测元件组成的触觉感测阵列。实验结果，在由设计的触觉传感阵列获取的实际触觉图像上进行，表明基于内核PCA的特征融合可以显着提高几何特征和傅立叶描述符的区分性能。此外，设计的MKL-SVM在识别精度方面优于常规SVM。建议的识别方案能够实现超过85＆＃X25的高识别率;对于12种常用的工业应用中的常用金属部件的分类。

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