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首页> 外文会议>Proceedings of the 17th IASTED international conference on robotics applications ; Proceedings of the 11th IASTED international conference on biomedical engineering >ENHANCED SURFACE CLASSIFICATION FROM TACTILE DATA BY IMAGE FUSION
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ENHANCED SURFACE CLASSIFICATION FROM TACTILE DATA BY IMAGE FUSION

机译：通过图像融合从触觉数据中增强表面分类

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摘要

This work deals with recognizing surface by processing local information from their corresponding tactile images and fusing them to obtain the global pattern of surface irregularities. Tactile images are acquired while exploring surfaces with four kinds of texture patterns. Texture information is obtained from each of the images by edge detection of the region where higher amount of pressure is felt. These edge detected images are fused to obtain the pattern of surface irregularities. The fused images are classified using hierarchical multi-class Support Vector Machine which yields an accuracy of 83.334% in 0.083 seconds. It is observed that the classification accuracy is enhanced by image fusion than that obtained by concatenating features of each component image which formed the fused images in the former case. When noise is gradually added to the features, the classifier shows an accuracy of 75% even when SNR is 8dBW, indicating the robustness of the classifier. Also, the performance of the algorithm is tested by adding white Gaussian noise to the raw images. Finally, McNemar Test validates the results. Thus, the algorithm can be integrated into a tactile-sensing system in real-time scenario for identifying surfaces based on texture.
机译：这项工作是通过处理来自其相应触觉图像的局部信息并将其融合以获得表面不规则性的整体模式来识别表面的。在探索具有四种纹理图案的表面时获取触觉图像。通过对感觉到较大压力的区域进行边缘检测，从每个图像中获得纹理信息。这些边缘检测图像被融合以获得表面不规则图案。使用分层的多类支持向量机对融合图像进行分类，该类向量在0.083秒内产生83.334％的精度。可以看出，与在前一种情况下通过合并形成融合图像的每个组成图像的特征所获得的图像融合相比，通过图像融合可以提高分类精度。当将噪声逐渐添加到功能部件时，即使SNR为8dBW，分类器仍显示75％的准确度，表明分类器的鲁棒性。此外，通过将白高斯噪声添加到原始图像来测试算法的性能。最后，McNemar Test验证结果。因此，该算法可以实时集成到触觉系统中，以基于纹理识别表面。

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