Publications [#336659] of Vahid Tarokh

Papers Published

1. Shahrampour, S; Noshad, M; Ding, J; Tarokh, V, Online Learning for Multimodal Data Fusion with Application to Object Recognition, Ieee Transactions on Circuits and Systems Ii: Express Briefs, vol. 65 no. 9 (September, 2018), pp. 1259-1263, Institute of Electrical and Electronics Engineers (IEEE) [doi]
   (last updated on 2023/06/01)

   Abstract:
   We consider online multimodal data fusion, where the goal is to combine information from multiple modes to identify an element in a large dictionary. We address this problem in the context of object recognition by focusing on tactile sensing as one of the modes. Using a tactile glove with seven sensors, various individuals grasp different objects to obtain 7-D time series, where each component represents the pressure sequence applied to one sensor. The pressure data of all objects is stored in a dictionary as a reference. The objective is to match a streaming vector time series from grasping an unknown object to a dictionary object. We propose an algorithm that may start with prior knowledge provided by other modes. Receiving pressure data sequentially, the algorithm uses a dissimilarity metric to modify the prior and form a probability distribution over the dictionary. When the dictionary objects are dissimilar in shape, we empirically show that our algorithm recognize the unknown object even with a uniform prior. If there exists a similar object to the unknown object in the dictionary, our algorithm needs the prior from other modes to detect the unknown object. Notably, our algorithm maintains a similar performance to standard offline classification techniques, such as support vector machine, with a significantly lower computational time.