Learning cross‐modal visual‐tactile representation using ensembled generative adversarial networks. Issue 2 (17th May 2019)

• Record Type: Journal Article
• Authors: Li, Xinwu; Liu, Huaping; Zhou, Junfeng; Sun, FuChun
• Is Part Of: Cognitive computation and systems. Volume 1:Issue 2(2019)
• Page Start: 40
• Page End: 44
• Publication Date: 2019-05-17
• Subjects: image texture -- visual perception -- haptic interfaces -- learning (artificial intelligence) -- feature extraction -- tactile sensors -- handicapped aids; learning cross‐modal visual‐tactile representation -- ensembled generative adversarial networks -- authors study -- deep learning model -- tactile information -- different texture images -- tactile signal -- tactile sensation -- different image visual information -- corresponding tactile feedback output mode -- training model -- simple training -- tactile output -- tactile evaluation method; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2018.0014
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : In this study, the authors study a deep learning model that can convert vision into tactile information, so that different texture images can be fed back to the tactile signal close to the real tactile sensation after training and learning. This study focuses on the classification of different image visual information and its corresponding tactile feedback output mode. A training model of ensembled generative adversarial networks is proposed, which has the characteristics of simple training and stable efficiency of the result. At the same time, compared with the previous methods of judging the tactile output, in addition to subjective human perception, this study also provides an objective and quantitative evaluation system to verify the performance of the model. The experimental results show that the learning model can transform the visual information of the image into the tactile information, which is close to the real tactile sensation, and also verify the scientificity of the tactile evaluation method.