Filters








18,108 Hits in 2.7 sec

Unsupervised texture segmentation using feature distributions

Timo Ojala, Matti Pietikäinen
1999 Pattern Recognition  
This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the  ...  Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions.  ...  The authors also wish to thank following persons for providing images used in this study; Richard C. Dubes, Anil K.  ... 
doi:10.1016/s0031-3203(98)00038-7 fatcat:4e7o2vr4andqdh25hzxasakmbm

Unsupervised texture segmentation using feature distributions [chapter]

Timo Ojala, Matti Pietikäinen
1997 Lecture Notes in Computer Science  
This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the  ...  Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions.  ...  The authors also wish to thank following persons for providing images used in this study; Richard C. Dubes, Anil K.  ... 
doi:10.1007/3-540-63507-6_216 fatcat:k7wleonupzd2fezc4otc5ryayy

Variational Region-Based Segmentation Using Multiple Texture Statistics

I Karoui, R Fablet, J Boucher, J Augustin
2010 IEEE Transactions on Image Processing  
Index Terms-Active regions, level sets, nonparametric distributions, supervised and unsupervised segmentation, texture similarity measure.  ...  This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation.  ...  In [17] , Gaussian distributions are used to model feature channels extracted from structure tensor, etc.  ... 
doi:10.1109/tip.2010.2071290 pmid:20813644 fatcat:xhqhgltv7ngnlmy7hagd2luwny

Experimentation on the Use of Chromaticity Features, Local Binary Pattern, and Discrete Cosine Transform in Colour Texture Analysis [chapter]

Padmapriya Nammalwar, Ovidiu Ghita, Paul F. Whelan
2003 Lecture Notes in Computer Science  
Segmentation is carried out based on an unsupervised texture segmentation method. The performance of the method is evaluated using different chromaticity features and also using the ROC curves.  ...  This paper describes a method for colour texture analysis, which performs segmentation based on colour and texture information.  ...  The distribution of LBP, contrast and the colour features are used for colour texture description. On the other hand, DCT is combined with the chrominance features for colour texture segmentation.  ... 
doi:10.1007/3-540-45103-x_26 fatcat:gedboxdsezcl7kjbth64gnojkq

Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters

Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael Bennet, Mahesan Niranjan
2012 Procedings of the British Machine Vision Conference 2012  
In this paper, local distributions of low order Gaussian Markov Random Field (GMRF) model parameters are proposed as texture features for unsupervised texture segmentation.  ...  These texture features expand the possibility of using relatively low order GMRF model parameters for segmenting fine to very large texture patterns and offer lower computational cost.  ...  Conclusions In this study the use of local distributions of biased estimates of low order GMRF model parameters as a texture feature in an unsupervised texture segmentation framework is explored.  ... 
doi:10.5244/c.26.88 dblp:conf/bmvc/DharmagunawardhanaMBN12 fatcat:5gah42rvnjfg5chzzcuppaxpoy

K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation

D.A. Clausi
2002 Pattern Recognition  
Gabor ÿlters and co-occurrence probabilities are used as texture features. ?  ...  used for clustering in image texture segmentation solutions.  ...  This is appealing to increase the segmentation accuracy, however using adaptive ÿlters would have a signiÿcant computational overhead when compared to the current linear smoothing.  ... 
doi:10.1016/s0031-3203(01)00138-8 fatcat:nhi7d3ttyvaybe3k4haepdif7a

Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features

Eliezer Emanuel Bernart, Maciel Zortea, Jacob Scharcanski, Sergio Bampi
2015 Vision Letters  
In the sequence we use stochastic texture features<br />to refine the suspicious regions.  ...  <p>This work presents a novel unsupervised method to segment skin<br />lesions in macroscopic images, grouping the pixels into three disjoint<br />categories, namely 'skin lesion', 'suspicious region'  ...  Gamma or Rayleigh), and use stochastic texture features to measure how much they deviate from randomness [2] (e.g. the regions within a skin lesion tend to be more clustered and less random than the  ... 
doi:10.15353/vsnl.v1i1.62 fatcat:4ujhh2bhebc3zelsp2zcstnyoq

A Markov random field image segmentation model for color textured images

Zoltan Kato, Ting-Chuen Pong
2006 Image and Vision Computing  
We propose a Markov random field (MRF) image segmentation model, which aims at combining color and texture features.  ...  Here, we use the perceptually uniform CIE-L*u*v* color values as color features and a set of Gabor filters as texture features.  ...  Acknowledgements This research was partially supported by the Janos Bolyai research fellowship of the Hungarian Academy of Sciences, the Table 2 Computing times and segmentation error (misclassification  ... 
doi:10.1016/j.imavis.2006.03.005 fatcat:isoa2y74dfdwdhvmbfuz32kwqi

ARCHETYPAL ANALYSIS: AN ALTERNATIVE TO CLUSTERING FOR UNSUPERVISED TEXTURE SEGMENTATION

Ismael Cabero, Irene Epifanio
2019 Image Analysis and Stereology  
Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover  ...  Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous  ...  We would also like to thank Pierre Soille for providing us with the images.  ... 
doi:10.5566/ias.2052 fatcat:g3hbzzuac5hqfln3m2iikzbdpa

A New Method For Multi-Resolution Texture Segmentation Using Gaussian Markov Random Fields

Roni Mittelman, Moshe Porat
2005 Zenodo  
This work complements [8] , by applying the feature to GMRF based semi-unsupervised texture segmentation.  ...  SEMI-UNSUPERVISED TEXTURE SEGMENTATION USING GMRF Our texture segmentation algorithm follows [2] with a few major differences described in section 3.4.  ... 
doi:10.5281/zenodo.39227 fatcat:uyrrbc6pd5h3takczdtdk4dkuu

US Image Segmentation Based on Expectation Maximization and Gabor Filter

Anita Khanna, Meenakshi Sood, Swapna Devi
2012 International Journal of Modeling and Optimization  
The approach includes three steps: decomposition of image using Gabor filters, texture feature extraction and segmentation.  ...  EM technique used after texture feature extraction is tested on many US images and results were quite satisfactory.  ...  Both supervised and unsupervised approaches are used for texture segmentation.  ... 
doi:10.7763/ijmo.2012.v2.117 fatcat:qos3m6qgl5h4djqam7apjtac5y

Unsupervised Texture Segmentation Via Applying Geodesic Active Regions To Gaborian Feature Space

Yuan He, Yupin Luo, Dongcheng Hu
2007 Zenodo  
In this paper, we propose a novel variational method for unsupervised texture segmentation. We use a Gabor filter bank to extract texture features.  ...  To avoid deforming contours directly in a vector-valued space we use a Gaussian mixture model to describe the statistical distribution of this space and get the boundary and region probabilities.  ...  Yet, we have shown that this method can be successfully applied to unsupervised texture segmentation. V. CONCLUSION In this paper, an unsupervised texture segmentation method is proposed.  ... 
doi:10.5281/zenodo.1332479 fatcat:57symo6vjjcv7cek4y22gspxeq

Unsupervised Image Segmentation Contest

Michal Haindl, Stanislav Mike
2014 2014 22nd International Conference on Pattern Recognition  
It aims to promote evaluation of unsupervised color image segmentation algorithms using publicly available data sets.  ...  The unsupervised color image segmentation competition is taking place in conjunction with the ICPR 2014 conference.  ...  FSEG A factorizaton-based texture segmenter [16] uses local spectral histograms as features. It constructs an M ×N feature matrix using M -dimensional feature vectors in an N -pixel image.  ... 
doi:10.1109/icpr.2014.264 dblp:conf/icpr/HaindlM14 fatcat:mvscr35o7rbanauplnos5l2le4

A multi-scale method for urban tree canopy clustering recognition using high-resolution image

Jian-Nong Cao, Zhenfeng Shao, Jia Guo, Bei Wang, Yuwei Dong, Pinglu Wang
2015 Optik (Stuttgart)  
of wavelet to realize the preliminary clustering segmentation, finally we apply the supervised segmentation to extract tree canopy based on clustering feature.  ...  transfer in multi-scale feature space, then differences of internal and external structure in urban tree canopy and differences of average spectral radiant intensity are used as multiscale feature space  ...  Applying coarser scale feature during texture segmentation is implemented by using larger window of feature analysis.  ... 
doi:10.1016/j.ijleo.2015.02.094 fatcat:oddrehqabnblnlzn6kotcqz4se

Discrete Mixture Models for Unsupervised Image Segmentation [chapter]

Jan Puzicha, Thomas Hofmann, Joachim M. Buhmann
1998 Mustererkennung 1998  
We demonstrate an application of this method to the unsupervised segmentation of textured images based on local empirical distributions of Gabor coe cients.  ...  Algorithms based on distributions of features have been successfully used in texture analysis 1, 7, 4 .  ...  Numerous approaches to unsupervised texture segmentation have been proposed over the past decades.  ... 
doi:10.1007/978-3-642-72282-0_13 dblp:conf/dagm/PuzichaBH98 fatcat:fm4jlfi6l5h45ip6ha4f6xv5ju
« Previous Showing results 1 — 15 out of 18,108 results