Journal ArticleDOI
Texture decomposition by harmonics extraction from higher order statistics
Yong Huang,Kap Luk Chan +1 more
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TLDR
The diagonal slice of the fourth-order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies and is proposed to use to estimate a power spectrum from which the harmonic frequencies can be easily extracted.Abstract:
In this paper, a method of harmonics extraction from Higher Order Statistics (HOS) is developed for texture decomposition. We show that the diagonal slice of the fourth-order cumulants is proportional to the autocorrelation of a related noiseless sinusoidal signal with identical frequencies. We propose to use this fourth-order cumulants slice to estimate a power spectrum from which the harmonic frequencies can be easily extracted. Hence, a texture can be decomposed into deterministic components and indeterministic components as in a unified texture model through a Wold-like decomposition procedure. The simulation and experimental results demonstrated that this method is effective for texture decomposition and it performs better than traditional lower order statistics based decomposition methods.read more
Citations
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A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques
TL;DR: This paper systematically review recent advances in surface inspection using computer vision and image processing techniques, particularly those based on texture analysis methods, to review the state-of-the-art techniques for the purposes of visual inspection and decision making schemes that are able to discriminate the features extracted from normal and defective regions.
Journal ArticleDOI
A Review of Quality Metrics for Fused Image
P. Jagalingam,Arkal Vittal Hegde +1 more
TL;DR: In this article, the authors reviewed the various quality metrics available in the literature, for assessing the quality of fused image, and evaluated the performance of the fused image by two variants such as with reference image and without reference image.
Journal ArticleDOI
Multiscale Hybrid Linear Models for Lossy Image Representation
TL;DR: The careful and extensive experimental results show that this new model gives more compact representations for a wide variety of natural images under a wide range of signal-to-noise ratios than many existing methods, including wavelets.
Journal ArticleDOI
Multi-level image thresholding using Otsu and chaotic bat algorithm
Suresh Chandra Satapathy,N. Sri Madhava Raja,Venkatesan Rajinikanth,Amira S. Ashour,Nilanjan Dey +4 more
TL;DR: In this paper, a novel chaotic bat algorithm (CBA) was proposed for multi-level thresholding in grayscale images using Otsu's between-class variance function.
Journal ArticleDOI
Visual-Based Defect Detection and Classification Approaches for Industrial Applications-A SURVEY.
Tamas Czimmermann,Gastone Ciuti,Mario Milazzo,Marcello Chiurazzi,Stefano Roccella,Calogero Maria Oddo,Paolo Dario +6 more
TL;DR: This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles, and describes artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way.
References
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Journal ArticleDOI
Statistical and structural approaches to texture
TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Book
Texture analysis
Mihran Tuceryan,Anil K. Jain +1 more
TL;DR: The geometric, random field, fractal, and signal processing models of texture are presented and major classes of texture processing such as segmentation, classification, and shape from texture are discussed.
Journal ArticleDOI
Texture analysis using gray level run lengths
TL;DR: In this paper, a set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a sets of samples representing nine terrain types.
Texture analysis using grey level run lengths
TL;DR: A set of texture features based on gray level run lengths is described, and good classification results are obtained with these features on a set of samples representing nine terrain types.