Results 61 to 70 of about 93,638 (313)
IEEE Access, 2021 This paper studies the use of deep-learning models (AlexNet, VggNet, ResNet) pre-trained on object categories (ImageNet) in applied texture classification problems such as plant disease detection tasks.
Stefania Barburiceanu +4 more
doaj +1 more source
Texture Classification with Neural Networks [PDF]
, 2013 Texture classification poses a well known difficulty within computer vision systems. This paper reviews a method for image segmentation based on the classification of textures using artificial neural networks. The supervised machine learning system developed here is able to recognize and distinguish among multiple feature regions within one or more ...
William Raveane +1 more
openaire +2 more sources
Advanced Engineering Materials, EarlyView.Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran +6 more
wiley +1 more source
A Completed Multi-Scale Local Statistics Pattern for Texture Classification
Image Analysis and StereologyBinary pattern methods play a vital role in extracting texture features. However, most of existing methods struggle to capture comprehensive and discriminative texture information.
Xiaochun Xu, Bin Li, Q.M. Jonathan Wu
doaj +1 more source
A Dislocation Perspective on Strength and Toughness in Ceramics
Advanced Engineering Materials, EarlyView.Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley +1 more source
AsTexNet: A Discriminative Scale-Aware Hybrid Texture Representation with Compact Embedding
Applied SciencesTexture classification remains challenging due to high inter-class similarity, scale variability, and limited labeled data. While handcrafted descriptors capture fine-grained microstructures and CNNs encode global semantics, existing hybrid approaches ...
Vandana Gupta +2 more
doaj +1 more source
Smoke Recognition and Texture Classification Using Improved Local Ternary Patterns
Jisuanji kexue yu tansuo, 2020 To improve detection rate and reduce false alarm rate for smoke recognition, this paper presents local ternary pattern based on confidence level (CLLTP), and further presents a novel multi-CLLTP (M_CLLTP) feature extraction model based on CLLTP. CLLTP is
LI Gang, YUAN Feiniu, XIA Xue, ZHANG Lin, LEI Bangjun
doaj +1 more source
Texture classification using discriminant wavelet packet subbands [PDF]
, 2002 This paper addresses the issue of selecting features from a given wavelet packet subband decomposition that are most useful for texture classification in an image.
Rajpoot, Nasir M. (Nasir Mahmood)
core
Advanced Engineering Materials, EarlyView.Grain boundary triple junctions are an essential ingredient of the microstructure of polycrystalline materials. In this study, a triple junction is observed using atomic‐resolution scanning transmission electron microscopy and characterized. Computer simulations reveal that the junction has a dislocation character that is determined by the joining ...
Tobias Brink +4 more
wiley +1 more source
SuroTex: Surrounding texture datasetMendeley Data
Data in BriefTexture analysis can be considered as one of the most important topics in the field of image processing and computer vision. However, the existing texture datasets such as KTH-TIPS, KTH-TIPS2, USPTex, DTD, and ALOT still have limitations which causes the
Muhammad Ardi Putra +2 more
doaj +1 more source