Journal ArticleDOI
Texture feature performance for image segmentation
TLDR
Results obtained show that direct feature statistics such as the Bhattacharyya distance are not appropriate evaluation criteria if texture features are used for image segmentation, and that the Haralick, Laws and Unser methods gave best overall results.About:
This article is published in Pattern Recognition.The article was published on 1990-03-01. It has received 228 citations till now. The article focuses on the topics: Scale-space segmentation & Image segmentation.read more
Citations
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Journal ArticleDOI
A comparative study of texture measures with classification based on featured distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.
Journal ArticleDOI
Unsupervised texture segmentation using Gabor filters
Anil K. Jain,Farshid Farrokhnia +1 more
TL;DR: A texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system is presented, which is based on reconstruction of the input image from the filtered images.
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
Filtering for texture classification: a comparative study
Trygve Randen,John Hakon Husoy +1 more
TL;DR: Most major filtering approaches to texture feature extraction are reviewed and a ranking of the tested approaches based on extensive experiments is presented, showing the effect of the filtering is highlighted, keeping the local energy function and the classification algorithm identical for most approaches.
Proceedings ArticleDOI
Performance evaluation of texture measures with classification based on Kullback discrimination of distributions
TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches to classification based on Kullback discrimination of sample and prototype distributions.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
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.
Journal ArticleDOI
Image Segmentation Techniques
TL;DR: There are several image segmentation techniques, some considered general purpose and some designed for specific classes of images as discussed by the authors, some of which can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid link growing scheme, centroid region growing scheme and split-and-merge scheme.
A comparative study of texture measures for terrain classification.
J. S. Weszka,A. Rosenfeld +1 more
TL;DR: Three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively; it was found that the Fouriers generally performed more poorly, while the other feature sets all performned comparably.
Journal ArticleDOI
A Comparative Study of Texture Measures for Terrain Classification
TL;DR: In this paper, three standard approaches to automatic texture classification make use of features based on the Fourier power spectrum, on second-order gray level statistics, and on first-order statistics of gray level differences, respectively.