The purpose of this study is to investigate the capability of machine vision to recognize object roughness and texture. We hypothesized that machine vision identifying object roughness/texture could be used for human-machine haptic interaction.
In this paper, we propose methods where Convolution Neural Network (CNN) features are used for feature extraction and Support Vector machine is used as classifier for texture classification. We used cross entropy as the loss function to estimate the error during training.
Such details provide a deeper understanding and appreciation for Texture Recognition Applications.
Texture classification is a fundamental challenge in computer vision, with applications ranging from medical imaging to material science and remote sensing. Below, we delve into five...
Herein, a novel texture recognition method is proposed by designing an arc-shaped soft tactile sensor and a bidirectional long short-term memory (LSTM) model with the attention mechanism.
In computer vision and object recognition applications, the extraction of texture features plays a significant role [1]. The machine learning algorithm is trained to recognize objects using texture features that are extracted from the image.
This paper presents a new texture recognition method. First, considering both sensing materials and bionic structures, based on the Ref. 19, a new magnetostrictive tactile sensor array is designed with high sensing accuracy, fast response, good repeatability, and strong adaptability.
