Abstract
In this chapter, the investigated tactile data is regarded as time sequences, of which dissimilarity can be evaluated by the popular dynamic time warping method. A kernel sparse coding method is therefore developed to address the tactile data representation and classification problem. However, the naive use of sparse coding neglects the intrinsic relation between individual fingers, which simultaneously contact the object. To tackle this problem, a joint kernel sparse coding model is proposed to solve the multi-finger tactile sequence classification task. In this model, the intrinsic relations between fingers are explicitly taken into account using the joint sparse coding, which encourages all of the coding vectors to share the same sparsity support pattern. The experimental results show that the joint sparse coding achieves better performance than conventional sparse coding.
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Liu, H., Sun, F. (2018). Tactile Object Recognition Using Joint Sparse Coding. In: Robotic Tactile Perception and Understanding. Springer, Singapore. https://doi.org/10.1007/978-981-10-6171-4_3
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DOI: https://doi.org/10.1007/978-981-10-6171-4_3
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