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About 10,000 papers for “Image texture”

IMAGE TEXTURE

Encyclopedia of Environmental Change · 2014

Cited by 0

Image texture analyses

Atni Hiremath, Santosh · Socio-Environmental Systems Modeling · 2010

Cited by 0Open access

Image texture tools : texture synthesis, texture transfer, and plausible restoration

Harrison., Paul Francis · Figshare · 2021

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Cited by 1Open access

Image texture tools : texture synthesis, texture transfer, and plausible restoration

Harrison., Paul Francis · Monash University · 2021

This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. Monash staff and postgraduate students can use the link in the References field.

Cited by 0Open access

Texture-Based Image Segmentation

Pasáček, Václav · Brno University of Technology Digital Library (Brno University of Technology) · 2012

Image segmentation is an important step in image processing. A traditional way how to segment an image is a texture-based segmentation that uses texture features to describe image texture. In this work, Local Binary Patterns (LBP) are used for image texture representation. Texture feature is a histogram of occurences of LBP codes in a small image window. The work also aims to comparison of results of various modifications of Local Binary Patterns and their usability in the image segmentation which is done by unsupervised clustering of texture features. The Fuzzy C-Means algorithm is finally used for the clustering in this work.

Cited by 0

Texture-Preserving Image Deblurring

Chen Fen, Xiaojun Huang, Wufan Chen · IEEE Signal Processing Letters · 2010

In this letter, a two-step texture-preserving image deblurring method is proposed. This method restores the blurred image in the frequency domain to obtain a noisy result with minimal loss of signal components, followed by a modified non-local means (NLM) filter to attenuate the leaked colored noise. This modified NLM filter computes the weights not only using the self-similarity of the neighbors, but also on spatial distance from the pixel in question. Experimental results demonstrate that the proposed algorithm can effectively obtain a visually pleasant texture-preserving result with compared to the state-of-the-art total variation method.

Cited by 14

Example texture image data.

Osbert C. Zalay (8350980) · Figshare · 2020

<p>This example has an assortment of 10 texture classes taken from the CuRET, UIUC and KTH-TIPS databases, each represented by a color-coded cluster, with each cluster consisting of a variable number of sample images taken under different orientations, lighting conditions and scales. Multi-resolution Haralick and local binary pattern texture features were computed, and then principal component analysis (PCA) was performed for visualization purposes and the data plotted with respect to the first three principal component axes. PCA was not performed as part of linkage analysis.</p>

Cited by 0Open access

Image Texture Analysis - Survey

Avinash Dixit, Nagaratna P. Hegde · 2013

This paper discusses the various methods used to analyze the texture property of an image. Texture analysis is broadly classified into three categories: Pixel based, local feature based and Region based. Pixel based method uses grey level co occurrence matrices, difference histogram and energy measures and Local Binary Patterns(LBP) Local feature based method uses edges of local features and generalization of co occurrence matrices. Region based method uses region growing and topographic models.

Cited by 19

Browsing Texture Image Databases

Suryani Lim, Lianping Chen, Guojun Lu et al. · 2005

The MPEG-7 standard defines two types of texture features: texture retrieval descriptor (TRD) for retrieval and texture browsing descriptor (TBD)for browsing. The retrieval process is straightforward but it is unclear how one could use TBD for browsing. This paper describes two methods of generating layouts for browsing a texture image database. The layouts are then subject to quantitative and qualitative evaluations. The experiments showed that: (1) only some features of TBD are appropriate for browsing, (2) once the inappropriate features are removed TBD is good for browsing only if the textures are structured, (3) the layouts generated usingTRDare more suitable for browsing.

Cited by 2

Detecting Image Spam Using Image Texture Features

Basheer Al‐Duwairi, Ismail M. Khater, Omar Al‐Jarrah · International Journal for Information Security Research · 2013

Filtering image email spam is considered to be a challenging problem because spammers keep modifying the images being used in their campaigns by employing different obfuscation techniques. Therefore, preventing text recognition using Optical Character Recognition (OCR) tools and imposing additional challenges in filtering such type of spam.

Cited by 25Open access

Texture image analysis and texture classification methods-a review

Armi, Laleh, Fekri-Ershad, Shervan · Figshare · 2021

Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately. Texture analysis methods usually are classified into four categories: statistical methods, structural, model-based and transform- based methods. This paper discusses the various methods used for texture or analysis in details. New researches shows the power of combinational methods for texture analysis, which can't be in specific category. This paper provides a review on well known combination methods in a specific section with details. This paper counts advantages and disadvantages of well-known texture image descriptors in the result part. Main focus in all of the survived methods is on discrimination performance, computational complexity and resistance to challenges such as noise, rotation, etc. A brief review is also made on the common classifiers used for texture image classification. Also, a survey on texture image benchmark datasets is included.

Cited by 0Open access

Texture image analysis and texture classification methods-a review

Armi, Laleh, Fekri-Ershad, Shervan · Figshare · 2021

Tactile texture refers to the tangible feel of a surface and visual texture refers to see the shape or contents of the image. In the image processing, the texture can be defined as a function of spatial variation of the brightness intensity of the pixels. Texture is the main term used to define objects or concepts of a given image. Texture analysis plays an important role in computer vision cases such as object recognition, surface defect detection, pattern recognition, medical image analysis, etc. Since now many approaches have been proposed to describe texture images accurately. Texture analysis methods usually are classified into four categories: statistical methods, structural, model-based and transform- based methods. This paper discusses the various methods used for texture or analysis in details. New researches shows the power of combinational methods for texture analysis, which can't be in specific category. This paper provides a review on well known combination methods in a specific section with details. This paper counts advantages and disadvantages of well-known texture image descriptors in the result part. Main focus in all of the survived methods is on discrimination performance, computational complexity and resistance to challenges such as noise, rotation, etc. A brief review is also made on the common classifiers used for texture image classification. Also, a survey on texture image benchmark datasets is included.

Cited by 0Open access

Image-Driven Procedural Texture Specification

Eric Bourque Gregory · 2001

In this paper we describe an approach to the automated specification of procedural textures to be used in rendering, based on representative samples. Procedural textures exhibit many advantages over traditional surface texturing techniques, but unfortunately finding the correct procedural texture and appropriate parameters to create the desired texture can be a daunting task for even the most experienced computer graphics artists. The method we propose here, which we refer to as image-based procedural texturing, allows the specification of the desired texture in the form of a digital input image. From this sample texture, a corresponding procedural texture is found which produces a texture which is perceptually similar to the input sample.

Cited by 0