Autors: Neshov, N. N., Tonchev, K., Manolova, A. H.
Title: LBCNIN: Local Binary Convolution Network with Intra-Class Normalization for Texture Recognition with Applications in Tactile Internet
Keywords: ConvNeXt, deep learning, DTD, GTOS, GTOS-Mobile, KTH-TIPS-2, local binary convolution, MobileNet, ResNet, texture recognition, Tactile Internet

Abstract: Texture recognition is a pivotal task in computer vision, crucial for applications in material sciences, medicine, and agriculture. Leveraging advancements in Deep Neural Networks (DNNs), researchers seek robust methods to discern intricate patterns in images. In the context of the burgeoning Tactile Internet (TI), efficient texture recognition algorithms are essential for real-time applications. This paper introduces a method named Local Binary Convolution Network with Intra-class Normalization (LBCNIN) for texture recognition. Incorporating features from the last layer of the backbone, LBCNIN employs a non-trainable Local Binary Convolution (LBC) layer, inspired by Local Binary Patterns (LBP), without fine-tuning the backbone. The encoded feature vector is fed into a linear Support Vector Machine (SVM) for classification, serving as the only trainable component. In the context of TI, the availability of images from multiple views, such as in 3D object semantic segmentation, allows..

References

    Issue

    ELECTRONICS, vol. 13, 2024, Switzerland, https://doi.org/10.3390/electronics13152942

    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science