Research on dimension reduction method of target object classification based on visual touch fusion

Jiangbing Qin; Hongen Wu; Haiyong Guo; Shuang Yao; Xiang Liu; Shengfu Guan

doi:10.1117/12.2625330

12 December 2021 Research on dimension reduction method of target object classification based on visual touch fusion

Jiangbing Qin, Hongen Wu, Haiyong Guo, Shuang Yao, Xiang Liu, Shengfu Guan

Proceedings Volume 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021); 121272H (2021) https://doi.org/10.1117/12.2625330
Event: International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 2021, Qingdao, China

Abstract

With the rapid development of artificial intelligence technology, improving the identification ability of the machine and making the machine have "senses" ^[1] will further improve the working efficiency of the robot in both production and life. In the first mock exam, this paper focuses on the classification of objects under the fusion of vision and tactile features. Taking common objects as the research object, the visual and tactile information data of objects are collected. The characteristics of target objects are extracted by using image processing algorithm and tactile sensor signal principle. The redundant features are removed by using two dimensionality reduction methods: multi cluster feature selection algorithm (MCFS) and principal component analysis algorithm (PCA), and the effective features are put into support vector machine Library (SVM), two different feature dimensionality reduction methods are fused through decision level fusion to optimize the classification accuracy.

Citation Download Citation

Jiangbing Qin, Hongen Wu, Haiyong Guo, Shuang Yao, Xiang Liu, and Shengfu Guan "Research on dimension reduction method of target object classification based on visual touch fusion", Proc. SPIE 12127, International Conference on Intelligent Equipment and Special Robots (ICIESR 2021), 121272H (12 December 2021); https://doi.org/10.1117/12.2625330

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KEYWORDS

Visualization

Principal component analysis

Sensors

Feature selection

Visual analytics

Detection and tracking algorithms

Dimension reduction

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