Wood material recognition for industrial applications

Fu, Haibin; Liu, Huaping; Deng, Xinyang; Sun, Fuchun

doi:10.1080/21642583.2018.1553691

Cited by 3 publications

(1 citation statement)

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“…Detailed tactile perception of robots can be referred to Luo et al [12]. For material classification, Fu et al regarded tactile sound as a model of wood material attribute perception [13]. By tapping on different kinds of woodblocks, the sound was collected and analysed to obtain the material properties of different kinds of wood.…”

Section: Introductionmentioning

confidence: 99%

Multi‐modal broad learning for material recognition

Wang

Liu

et al. 2021

Cognitive Comp and Systems

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Material recognition plays an important role in the interaction between robots and the external environment. For example, household service robots need to replace humans in the home environment to complete housework, so they need to interact with daily necessities and obtain their material performance. Images provide rich visual information about objects; however, it is often difficult to apply when objects are not visually distinct. In addition, tactile signals can be used to capture multiple characteristics of objects, such as texture, roughness, softness, and friction, which provides another crucial way for perception. How to effectively integrate multi-modal information is an urgent problem to be addressed. Therefore, a multi-modal material recognition framework CFBRL-KCCA for target recognition tasks is proposed in the paper. The preliminary features of each model are extracted by cascading broad learning, which is combined with the kernel canonical correlation learning, considering the differences among different models of heterogeneous data. Finally, the open dataset of household objects is evaluated. The results demonstrate that the proposed fusion algorithm provides an effective strategy for material recognition.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Introductionmentioning

confidence: 99%