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About 10,000 papers for “texture classification”

Dynamic texture classification

Chockalingam, Muthiah · DR-NTU (Nanyang Technological University) · 2022

Video Object Segmentation is a segment under the field of computer vision, and it recently has been garnering a lot of attention as to its applicability in solving many real-life problems. One such problem is the effective navigation of ships, by using video objects segmentation to effectively segment the target object (i.e., water) to identify clear regions of water for ships to sail through and avoid any possible obstacles on their course. The biggest problem with creating such a video object segmentation model would be the fact that water has a very tricky appearance. Its appearance and texture are highly dynamic as they change very quickly, sometimes even between frames itself, due to factors such as illumination, ripples, and waves.
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\nTherefore, this student’s aim is to analyse the existing video object segmentation methods, identify the most suitable one for dynamic texture classification (i.e., tracking object with a dynamic appearance), and test it with a dataset that is representative of the waters that ships sail through. Results are then recorded to evaluate the effectiveness of that particular model with regards to the objective of aiding a ship’s navigation.

Cited by 0

Non-Parametric Texture Synthesis Using Texture Classification

Kyle Ziga, Judy Bagchi, Jan P. Allebach et al. · Electronic Imaging · 2017

In this paper we present a method of texture synthesis which removes the need for users to set, or even understand, parameters which have an impact on the synthesized output. We accomplish this by first classifying each input texture sample into one of three texture types: regular, irregular and stochastic. We found that textures within a class were synthesized well with similar parameters. If we know the input texture class, we can provide a good starting set of parameters for the synthesis algorithm. Instead of requiring a user to manually select a set of parameters, we simply ask that the user tell us whether the synthesized texture is satisfactory or not. If the output is not satisfactory, we adjust parameters and try again until the user is happy with the output. In this implementation we use the image quilting method in [1], a texture synthesis algorithm, as well as texture classification. With small adjustments our method can be applied to other texture synthesis methods.

Cited by 1

Texture Classification and Retrieval

Gabarda, Salvador · Figshare · 2017

Matlab package of tools for texture image classification and retrieval based on a local spatial frequency analysis method. This method can be catalogued among other statistical methods for texture classification and retrieval and supply tools for feature extraction (FE) and learning through a similarity measure (SM). The FE step consists in finding the<br>discrete spatial frequency histograms of multi-scale texture images, by means of a discrete 1-D pseudo-Wigner<br>distribution (PWD), directed upon the maximum angular activity of the von Mises distribution of image entropy. The learning process (LP) step compare the space-frequency histograms by a measure based on the Sørensen–Dice index. The LP refines the spatial-frequency histograms of texture models over a massive (several hundred) set of texture images. The philosophy of the retrieval operation consists in searching the best figure for the similarity measure, among a set of model-histograms vs. a given query histogram.

Cited by 0Open access

Texture Classification by ICA

Daniela Colţuc, Thierry Fournel, Jean-Marie Becker · 2007

ICA (Independent Component Analysis) is a mathematical tool traditionally employed for source separation. In this paper, we test its ability for texture analysis, in order to provide a new texture classification method. From the multitude of the existing algorithms, we have chosen FastICA, a version based on the forth order statistics of the analyzed signal. By FastICA, a texture is decomposed in a weighted sum of components with a rather high degree of independence. Each component is further described by means of its negentropy, which is a measure of the nongaussianity. We show experimentally, that the three most nongaussian components of each analyzed texture are able to cluster the test samples.

Cited by 4

Texture-based cloud classification

Ronald M. Welch · NASA STI Repository (National Aeronautics and Space Administration) · 1988

The purpose of the 1988 ASEE Summer Program has been to broaden the application of texture-based cloud classification approaches to lower spatial resolution GOES imagery, and to design texture-based approaches for determining cloud cover over high albedo surfaces.

Cited by 0Open access

MRMRF-based Texture Classification

Lei Wang, Jun Liu, Stan Z. Li · 2007

In this paper, we present a new scheme to classify different textures. We propose a scheme to use wavelet decomposition with Markov random field modeling to classify textures which we called multiresolution MRF(MRMRF). The parameters of each Markov random field models combined with wavelet energy signatures are used as features in texture classification. The classifier here we use is nearest linear combination(NLC). Our experiments show that NLC is better than NN classifier . Thus we can analyze the textures with Markov Random Field models on different scales with the wavelet decomposition effectively. 1. Introduction Texture is one of the basic characteristics of a visible surface and it provides important information for scene interpretation and segmentation as well as image processing. Texture analysis plays an important role in computer vision and pattern recognition. To analyze and identify textures, a number of texture analysis methods have been developed. These ar...

Cited by 0

Texture Modeling and Classification

Shigeru Ando, Yasuhiro Doi · Transactions of the Society of Instrument and Control Engineers · 1978

The texture gives useful information for dividing a picture into segments, and the analysis by using this information is called a texture analysis. Here two fundamental problems will be encountered; namely, 1) how this texture is characterized or modeled, and 2) how the demands on the class separability and on the segmentation accuracy are compromised.For the first problem this paper gives a mathematical model of texture to descrive the 1st order density function of pixcell grayness and the autocorrelation function. By synthesizing a texture sample following this model, the model fitness is investigated visually.For the second problem is given a classification method which uses minimum pixcells for classification through the above mentioned mathematical model under the condition that the class separability must be greater than a given value.Simulation of this classification method resulted to provide sufficient class separability without much loss of the segmentation accuracy.

Cited by 0Open access

Mimicking human texture classification

Eva M. Van Rikxoort A, Theo E. Schouten B · 2008

In an attempt to mimic human (colorful) texture classification by a clustering algorithm three lines of research have been encountered, in which as test set 180 texture images (both their color and gray-scale equivalent) were drawn from the OuTex and VisTex databases. First, a k-means algorithm was applied with three feature vectors, based on color/gray values, four texture features, and their combination. Second, 18 participants clustered the images using a newly developed card sorting program. The mutual agreement between the participants was 57 % and 56 % and between the algorithm and the participants it was 47 % and 45%, for respectively color and gray-scale texture images. Third, in a benchmark, 30 participants judged the algorithms ’ clusters with gray-scale textures as more homogeneous then those with colored textures. However, a high interpersonal variability was present for both the color and the gray-scale clusters. So, despite the promising results, it is questionable whether average human texture classification can be mimicked (if it exists at all).

Cited by 0

Texture Classification and Retrieval

Salvador Gabarda · Figshare · 2016

Matlab package of tools for texture image classification and retrieval based on a local spatial frequency analysis method. This method can be catalogued among other statistical methods for texture classification and retrieval and supply tools for feature extraction (FE) and learning through a similarity measure (SM). The FE step consists in finding the<br>discrete spatial frequency histograms of multi-scale texture images, by means of a discrete 1-D pseudo-Wigner<br>distribution (PWD), directed upon the maximum angular activity of the von Mises distribution of image entropy. The learning process (LP) step compare the space-frequency histograms by a measure based on the Sørensen–Dice index. The LP refines the spatial-frequency histograms of texture models over a massive (several hundred) set of texture images. The philosophy of the retrieval operation consists in searching the best figure for the similarity measure, among a set of model-histograms vs. a given query histogram.

Cited by 0Open access

A texture classification framework.

Shamik Tiwari (4490185), Akhilesh Kumar Sharma (10288628), Izzatdin Abdul Aziz (17782422) et al. · Figshare · 2025

&lt;div&gt;&lt;p&gt;Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier. However, computing the performance of these methods in the presence of various degradations is always an open area of discussion. Image noise is always a dominant factor among various image degradation factors, affecting the performance of these methods and making texture classification challenging. Therefore, it is essential to investigate the interpretation of these methods in the presence of prominent degradation factors such as noise. Applications for Segmentation-Based Fractal Texture Features (SFTF) include image classification, texture generation, and medical image analysis. They are beneficial for examining textures with intricate, erratic patterns that are difficult to characterize using conventional statistical techniques accurately. This paper assesses two texture feature extraction methods based on SFTF and statistical moment-based texture features in the presence and absence of Gaussian noise. The SFTF and statistical moments-based handcrafted features are passed to a multilayer feed-forward neural network for classification. These models are evaluated on natural textures from Kylberg Texture Dataset 1.0. The results show the superiority of segmentation-based fractal analysis over other approaches. The average accuracy rates using the SFTF are 99% and 97% in the absence and presence of Gaussian noise, respectively.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Texture Classification and Retrieval

Gabarda, Salvador · Figshare · 2017

Matlab package of tools for texture image classification and retrieval based on a local spatial frequency analysis method. This method can be catalogued among other statistical methods for texture classification and retrieval and supply tools for feature extraction (FE) and learning through a similarity measure (SM). The FE step consists in finding the<br>discrete spatial frequency histograms of multi-scale texture images, by means of a discrete 1-D pseudo-Wigner<br>distribution (PWD), directed upon the maximum angular activity of the von Mises distribution of image entropy. The learning process (LP) step compare the space-frequency histograms by a measure based on the Sørensen–Dice index. The LP refines the spatial-frequency histograms of texture models over a massive (several hundred) set of texture images. The philosophy of the retrieval operation consists in searching the best figure for the similarity measure, among a set of model-histograms vs. a given query histogram.

Cited by 0Open access

Affine-invariant texture classification

Dmitry Chetverikov, Zoltán Földvári · 2002

In content-based image retrieval, a texture pattern may appear in a wide range of 3D views. Affine transformation is an approximation frequently used in practice to represent the variation of a pattern. The existing approaches to texture classification cannot cope with this variation. We use pattern regularity to define an affine-invariant feature vector and apply it to classification of structured textures under the orthographic projection, an important subset of affine transformations. The proposed approach achieves 85% accuracy for 18 patterns.

Cited by 6

CNN Soil Texture Classification

Felix M. Riese · Zenodo (CERN European Organization for Nuclear Research) · 2020

What's Changed ADD python versions 3.9 and 3.10, remove 3.6 by @felixriese in https://github.com/felixriese/CNN-SoilTextureClassification/pull/3 <strong>Full Changelog</strong>: https://github.com/felixriese/CNN-SoilTextureClassification/compare/v1.1...v1.1.1

Cited by 3Open access