Haptic Texture Rendering of 2D Image Based on Adaptive Fractional Differential Method

Abstract

The fractional differential algorithm has a good effect on extracting image textures, but it is usually necessary to select an appropriate fractional differential order for textures of different scales, so we propose a novel approach for haptic texture rendering of two-dimensional (2D) images by using an adaptive fractional differential method. According to the fractional differential operator defined by the Grünvald–Letnikov derivative (G–L) and combined with the characteristics of human vision, we propose an adaptive fractional differential method based on the composite sub-band gradient vector of the sub-image obtained by wavelet decomposition of the image texture. We apply these extraction results to the haptic display system to reconstruct the three-dimensional (3D) texture force filed to render the texture surface of two-dimensional (2D) images. Based on this approach, we carry out the quantitative analysis of the haptic texture rendering of 2D images by using the multi-scale structural similarity (MS-SSIM) and image information entropy. Experimental results show that this method can extract the texture features well and achieve the best texture force file for 2D images.

Object surface roughness/texture recognition using machine vision enables for human-machine haptic interaction

“…Humans cannot intuitively perceive the surface texture of an object if the visual feedback is unavailable. How to transfer different surface textures to the users through tactile feedback has important academic and application value, such as the TACTICS (Way and Barner, 1997), the automatic assigning of haptic texture models (Hassan et al, 2017), and the haptic texture rendering method based on the adaptive fractional difference (Hu and Song, 2022). The feasibility of recognizing object surface features via machine vision for human-machine haptic interaction was also evidenced by the results of object texture classification and identification (Figure 5, Table 5).…”