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A Fully Unsupervised Texture Segmentation Algorithm

M. F. A. Fauzi, P. H. Lewis
2003 Procedings of the British Machine Vision Conference 2003  
By fully unsupervised, we mean the algorithm does not require any knowledge of the type of texture present nor the number of textures in the image to be segmented.  ...  This paper presents a fully unsupervised texture segmentation algorithm by using a modified discrete wavelet frames decomposition and a mean shift algorithm.  ...  Conclusion In this paper, we have developed a new framework for fully unsupervised texture segmentation based on modified discrete wavelet frames and mean shift algorithm.  ... 
doi:10.5244/c.17.53 dblp:conf/bmvc/FauziL03 fatcat:finbinqvrzhwpmffyqowgqo6ma

An Immune-Inspired Approach for Unsupervised Texture Segmentation Using Wavelet Packet Transform

Karinne S. Silva, Yuzo Iano
2009 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing  
In this paper, it is described a new unsupervised approach based on wavelet packet transform for texture images segmentation.  ...  After the extraction of the features, a clustering is carried out, by using an immune-inspired algorithm called ARIA (Adaptive Radius Immune Algorithm), which is capable of preserving the density information  ...  In the next section we will review the wavelet transform and the wavelet packet decomposition.  ... 
doi:10.1109/sibgrapi.2009.30 dblp:conf/sibgrapi/SilvaI09 fatcat:lppjc54dyncmjier2sqt533qxi

An adaptive approach to unsupervised texture segmentation using M-Band wavelet transform

Mausumi Acharyya, Malay K. Kundu
2001 Signal Processing  
The M-band wavelet decomposition, which is a direct generalization of the standard 2-band wavelet decomposition is applied to the problem of an unsupervised segmentation of two texture images.  ...  The window size in turn is adaptively selected depending on the frequency content of the images. Unsupervised texture segmentation is obtained by simple K-means clustering.  ...  The problem of segmenting an image into meaningful regions based on textural cue is referred to as texture segmentation problem.  ... 
doi:10.1016/s0165-1684(00)00278-4 fatcat:jye2etmzfzhypgm53ftu4qazla

Self-supervised texture segmentation using complementary types of features

Jiebo Luo, Andreas E. Savakis
2001 Pattern Recognition  
The proposed approach leverages on the advantages of both MRSAR and wavelet features.  ...  A two-stage texture segmentation approach is proposed where an initial segmentation map is obtained through unsupervised clustering of multiresolution simultaneous autoregressive (MRSAR) features and is  ...  The proposed two-stage approach leverages on the advantages of both MRSAR and wavelet features, and incorporates an adaptive neighborhood-based spatial constraint.  ... 
doi:10.1016/s0031-3203(00)00146-1 fatcat:n7vnfrtahvalnonkg6ep2wa7vi

Whale Optimization for Wavelet-Based Unsupervised Medical Image Segmentation: Application to CT and MR Images

Thavavel Vaiyapuri, Haya Alaskar
2020 International Journal of Computational Intelligence Systems  
to cluster the extracted wavelet texture features.  ...  Further, the absence of annotated ground-truth dataset in medical field limits the advantages of the trending deep learning techniques causing several setbacks.  ...  Also, the authors would like to thank the reviewers for their constructive comments and suggestions, which have improved the quality of this paper.  ... 
doi:10.2991/ijcis.d.200625.001 fatcat:7uboxjc7lnhlnjdh4cay74v5ge

Frame representations for texture segmentation

A. Laine, Jian Fan
1996 IEEE Transactions on Image Processing  
3 We introduce a novel method of feature extraction for texture segmentation that relies on multi-channel wavelet frames and two-dimensional envelope detection.  ...  The reliability of our methods are demonstrated experimentally by quantitative evaluation on both natural and synthetic textures.  ...  Section 2 brie y reviews the framework of discrete wavelet transforms and the discrete wavelet packet transform, including corresponding variations of wavelet frames.  ... 
doi:10.1109/83.499915 pmid:18285167 fatcat:4cdlxvugtffkdj25rpembdyuhy

USIS: Unsupervised Semantic Image Synthesis [article]

George Eskandar, Mohamed Abdelsamad, Karim Armanious, Bin Yang
2021 arXiv   pre-print
Furthermore, in order to match the color and texture distribution of real images without losing high-frequency information, we propose to use whole image wavelet-based discrimination.  ...  Semantic Image Synthesis (SIS) is a subclass of image-to-image translation where a photorealistic image is synthesized from a segmentation mask. SIS has mostly been addressed as a supervised problem.  ...  the efficient expansion and transformation of existing AI modules of autonomous vehicles to new domains."  ... 
arXiv:2109.14715v1 fatcat:3dgcw4ei5nc47o4e3qo47toc4a

Automatic texture segmentation for content-based image retrieval application

Mohammad Faizal Ahmad Fauzi, Paul H. Lewis
2006 Pattern Analysis and Applications  
In this article, a brief review on texture segmentation is presented, before a novel automatic texture segmentation algorithm is developed.  ...  The algorithm is based on a modified discrete wavelet frames and the mean shift algorithm.  ...  France for use of their images.  ... 
doi:10.1007/s10044-006-0042-x fatcat:pcfxbeetsjfatgkammljg4pcsq

The Empirical Watershed Wavelet

Basile Hurat, Zariluz Alvarado, Jérôme Gilles
2020 Journal of Imaging  
We illustrate the effectiveness and the advantages of such adaptive transform, first visually on toy images, and next on both unsupervised texture segmentation and image deconvolution applications.  ...  The empirical wavelet transform is an adaptive multi-resolution analysis tool based on the idea of building filters on a data-driven partition of the Fourier domain.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/jimaging6120140 pmid:34460537 pmcid:PMC8321194 fatcat:ofn6keadszdmdi7qsjidkmere4

Supervised Classification of Very High Resolution Optical Images Using Wavelet-Based Textural Features

Olivier Regniers, Lionel Bombrun, Virginie Lafon, Christian Germain
2016 IEEE Transactions on Geoscience and Remote Sensing  
Its high adaptability and the low number of parameters to be set are other advantages of the proposed approach.  ...  In this paper, we explore the potentialities of using wavelet-based multivariate models for the classification of Very High Resolution optical images.  ...  ACKNOWLEDGMENT The authors would like to thank the Recette Thématique Utilisateur/ORFEO and ISIS program of the CNES for providing the Pléiades images used in this study.  ... 
doi:10.1109/tgrs.2016.2526078 fatcat:cgv2lpz3e5a5lif7v3zrpl3u7e

A Survey of Surface Defect Detection of Industrial Products Based on A Small Number of Labeled Data [article]

Qifan Jin
2022 arXiv   pre-print
Therefore, this paper reviews the methods of surface defect detection of industrial products based on a small number of labeled data, and this method is divided into traditional image processing-based  ...  fine-tuning, semi-supervised, weak supervised and unsupervised.  ...  Wavelet transform has the advantages of low entropy, multi-resolution characteristics, decorrelation and flexibility of base selection.  ... 
arXiv:2203.05733v1 fatcat:7imwu76dqzglvms4fivggd6r3y

Learning non-homogenous textures and the unlearning problem with application to drusen detection in retinal images

Noah Lee, Andrew F. Laine, Theodore R. Smith
2008 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro  
Applied to nonhomogenous texture discrimination, our learning method is the first approach that deals with the unlearning problem applied to the task of drusen segmentation in retinal imagery, which itself  ...  is a challenging problem due to high variability of non-homogenous texture appearance.  ...  Fig. 1 : 1 Example of different wavelet subspaces sampled from overcomplete Lemarie wavelet frames for texture based structural similarity clustering.  ... 
doi:10.1109/isbi.2008.4541221 dblp:conf/isbi/LeeLS08 fatcat:f3cgrouolvht3hdexvk4d5skoy

A Brief Survey of Color Image Preprocessing and Segmentation Techniques

Siddhartha Bhattacharyya
2011 Journal of Pattern Recognition Research  
This article presents a brief survey of the aforestated trends in color image enhancement and segmentation.  ...  Multichannel information processing from a diverse range of channel information is highly time-and space-complex owing to the variety and enormity of underlying data.  ...  Other methods have been devised for color texture segmentation in the wavelet domain [84, 85] .  ... 
doi:10.13176/11.191 fatcat:iy4md7fp3rc37osn6qpgkuviqm

Surface Corrosion Detection and Classification for Steel Alloy using Image Processing and Machine Learning

Sanjay Kumar Ahuja
2018 Helix  
Several algorithms related to colour, texture, noise, clustering, segmentation, image enhancement, wavelet transformation etc. have been used in different combinations have been developed by different  ...  This paper presents an adaptive self-learning approach for image processing based classification techniques for detecting different types of corrosion in steel.  ...  Alternatively, unsupervised machine learning or neural network based models. Both supervised and unsupervised approaches have their advantages and disadvantages.  ... 
doi:10.29042/2018-3822-3827 fatcat:7sadquzkhrhyja5daleatrutne

Unsupervised image segmentation using wavelet-domain hidden Markov models

Xiaomu Song, Guoliang Fan, Michael A. Unser, Akram Aldroubi, Andrew F. Laine
2003 Wavelets: Applications in Signal and Image Processing X  
Then, a new unsupervised segmentation approach is developed by capturing the likelihood disparity of different texture features with respect to wavelet-domain HMMs.  ...  We first review recent supervised Bayesian image segmentation algorithms using wavelet-domain HMMs.  ...  The future work will be focus on the redundant wavelet transforms to achieve more accurate characterization of the texture information and accordingly the unsupervised segmentation results.  ... 
doi:10.1117/12.507049 fatcat:bsddsiaginfn3gbfqwppiapp6e
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