Results 261 to 270 of about 578,151 (295)

Relational Features for Texture Classification

2011
Texture features play an important role in facilitating various applications, for instance, image retrieval and object recognition. In this work, we investigate the relational features as a texture descriptor in classifying materials and visual textures from their appearance.
Wan Nural Jawahir Hj Wan Yussof, Hans Burkhardt   +1 more
openaire   +1 more source

Combining Features for Texture Analysis

2015
In the present paper we consider building feature vectors for texture analysis by combining information provided by two techniques.The first feature extraction method the Discrete Wavelet Transform is applied to the entire image. By computing the Gini index for several subimages of a given texture, we choose one that maximizes this measure.
openaire   +1 more source

On the Reliability of Computing Wigner Texture Features

Journal of Mathematical Imaging and Vision, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Svetlana Barsky, Maria Petrou
openaire   +1 more source

MORPHOLOGICAL TEXTURAL FEATURES

Particulate Science and Technology, 1989
ABSTRACT This paper describes the statistical and mathematical models for the gray level surface. Morphological Textural Features (MTFs) derived from the models are invariants. They include: • Bessel-Fourier Coefficients • Measurments of Gray Level Distributions • Rotational Symmetry • Translational Symmetry • Coarseness • Contrast • Roughness ...
N. B. HSYUNG, D. W. LUERKENS, J. K. BEDDOW   +2 more
openaire   +1 more source

Texture Defect Detection Using Invariant Textural Features

2001
In this paper we propose a novel method for the construction of invariant textural features for grey scale images. The textural features are based on an averaging over the 2D Euclidean transformation group with relational kernels. They are invariant against 2D Euclidean motion and strictly increasing grey scale transformations.
openaire   +2 more sources

Applications of Texture Features

2019
Texture is a vital visual and the emergent feature for image content explanation. The utilization of object texture is one of the utmost challenging problems in forming effective content-based image retrieval [1].
Jyotismita Chaki, Nilanjan Dey
openaire   +1 more source

Texture Feature Extraction

2019
This chapter focuses on another image feature called the texture feature. Two types of texture feature methods are discussed: traditional spatial methods and contemporary spectral methods. The chapter first introduces four spatial or handcrafted methods including Tamura, GLCM, MRF, and FD.
openaire   +1 more source

Pavement texture depth estimation using image-based multiscale features

Automation in Construction, 2022
Zihang Weng, Chenglong Liu, Difei Wu
exaly  

Wheat Fusarium Head Blight Detection Using UAV-Based Spectral and Texture Features in Optimal Window Size

Remote Sensing, 2021
Yingying Dong, Wenjiang Huang, Linyi Liu
exaly  

Deep Texture-Aware Features for Camouflaged Object Detection

IEEE Transactions on Circuits and Systems for Video Technology, 2023
Xiaowei Hu, Xuemiao Xu, Yangyang Xu
exaly