Low-Resolution Tactile Image Recognition for Automated Robotic Assembly Using Kernel PCA-Based Feature Fusion and Multiple Kernel Learning-Based Support Vector Machine

Abstract

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.

Tactile Object Recognition for Humanoid Robots Using New Designed Piezoresistive Tactile Sensor and DCNN

“…A high-resolution tactile sensor was designed for Flex e-Skin, but the required size was a trade-off [33]. Many tactile array sensors were developed for touch sensing and Flex e-Skin [32][33][34][35][36][37][38][39][40][41][42][43][44], while the resolution was compromised. Hence, they were not suitable for developing tactile sensor arrays for high accuracy image recognition capability.…”

Robotic tactile perception of object properties: A review

“…By covariance analysis of M , the resultant eigenvector lengths, principal axis direction and shape convexity are taken as features to recognize local shapes. The kernel PCA-based feature fusion is used in [118] to fuse geometric features and Fourier descriptors (based on Fourier coefficients) to better discriminate objects. In [119], PCA is applied to reduce the dimensionality of tactile readings.…”

A Low-Light Image Enhancement Method Based on Image Degradation Model and Pure Pixel Ratio Prior

“…Owing to the insufficient incident radiance received by the scene objects, images captured in the low-light environment are prone to suffer from low visibility, such as reduced contrast, faint color, and blurred scene details. This is a major problem for most applications of computational photography and computer vision which are primarily designed for highquality inputs, including satellite imaging [1], object recognition [2], intelligent vehicles [3], etc. Therefore, unveiling the scene details hidden within the dark regions to further enhance the overall visual quality, which is often referred to as "low-light image enhancement", is highly desired and has strong implications.…”