Image texture

An image texture is a set of metrics calculated in image processing designed to quantify the perceived texture of an image. Image Texture gives us information about the spatial arrangement of color or intensities in an image or selected region of an image. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in Segmentation (image processing) or classification of images. (Wikipedia.org)






Conferences related to Image texture

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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE


OCEANS 2020 - SINGAPORE

An OCEANS conference is a major forum for scientists, engineers, and end-users throughout the world to present and discuss the latest research results, ideas, developments, and applications in all areas of oceanic science and engineering. Each conference has a specific theme chosen by the conference technical program committee. All papers presented at the conference are subsequently archived in the IEEE Xplore online database. The OCEANS conference comprises a scientific program with oral and poster presentations, and a state of the art exhibition in the field of ocean engineering and marine technology. In addition, each conference can have tutorials, workshops, panel discussions, technical tours, awards ceremonies, receptions, and other professional and social activities.

  • OCEANS 2005 - EUROPE

  • OCEANS 2006 - ASIA PACIFIC

  • OCEANS 2007 - EUROPE

    The theme 'Marine Challenges: Coastline to Deep Sea' focuses on the significant challenges, from the shallowest waters around our coasts to the deepest subsea trenches, that face marine, subsea and oceanic engineers in their drive to understand the complexities of the world's oceans.

  • OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean

  • OCEANS 2009 - EUROPE

  • OCEANS 2010 IEEE - Sydney

  • OCEANS 2011 - SPAIN

    All Oceans related technologies.

  • OCEANS 2012 - YEOSU

    The OCEANS conferences covers four days with tutorials, exhibits and three days of parallel tracks that address all aspects of oceanic engineering.

  • OCEANS 2013 - NORWAY

    Ocean related technologies. Program includes tutorials, three days of technical papers and a concurrent exhibition. Student poster competition.

  • OCEANS 2014 - TAIPEI

    The OCEANS conference covers all aspects of ocean engineering from physics aspects through development and operation of undersea vehicles and equipment.

  • OCEANS 2015 - Genova

    The Marine Technology Society and the Oceanic Engineering Society of IEEE cosponsor a joint annual conference and exposition on ocean science, engineering and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 50-200 exhibits.

  • OCEANS 2016 - Shanghai

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorial on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2017 - Aberdeen

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town hall meetings on policy and governmental process.

  • 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO)

    The conference scope is to provide a thematic umbrella for researchers working in OCEAN engineering and related fields across the world to discuss the problems and potential long term solutions that concernnot only the oceans in Asian pacific region, but the world ocean in general.

  • OCEANS 2019 - Marseille

    Research, Development, and Operations pertaining to the Oceans


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)

The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2002 1st IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2002)

  • 2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2004)

  • 2006 IEEE 3rd International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2006)

  • 2007 IEEE 4th International Symposium on Biomedical Imaging: Macro to Nano (ISBI 2007)

  • 2008 IEEE 5th International Symposium on Biomedical Imaging (ISBI 2008)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2009 IEEE 6th International Symposium on Biomedical Imaging (ISBI 2009)

    Algorithmic, mathematical and computational aspects of biomedical imaging, from nano- to macroscale. Topics of interest include image formation and reconstruction, computational and statistical image processing and analysis, dynamic imaging, visualization, image quality assessment, and physical, biological and statistical modeling. Molecular, cellular, anatomical and functional imaging modalities and applications.

  • 2010 IEEE 7th International Symposium on Biomedical Imaging (ISBI 2010)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2011 IEEE 8th International Symposium on Biomedical Imaging (ISBI 2011)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2012 IEEE 9th International Symposium on Biomedical Imaging (ISBI 2012)

    To serve the biological, biomedical, bioengineering, bioimaging, and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2013 IEEE 10th International Symposium on Biomedical Imaging (ISBI 2013)

    To serve the biological, biomedical, bioengineering, bioimaging and other technical communities through a quality program of presentations and papers on the foundation, application, development, and use of biomedical imaging.

  • 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2014 will be the eleventh meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2015 will be the 12th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2014 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI 2016)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forumfor the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2016 willbe the thirteenth meeting in this series. The previous meetings have played a leading role in facilitatinginteraction between researchers in medical and biological imaging. The 2016 meeting will continue thistradition of fostering crossfertilization among different imaging communities and contributing to an integrativeapproach to biomedical imaging across all scales of observation.

  • 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2017 will be the 14th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2017 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2018 will be the 15th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2018 meeting will continue this tradition of fostering crossfertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.

  • 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

    The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging.ISBI 2019 will be the 16th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2019 meeting will continue this tradition of fostering cross fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.



Periodicals related to Image texture

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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computer Graphics and Applications, IEEE

IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics. From specific algorithms to full system implementations, CG&A offers a strong combination of peer-reviewed feature articles and refereed departments, including news and product announcements. Special Applications sidebars relate research stories to commercial development. Cover stories focus on creative applications of the technology by an artist or ...


Dielectrics and Electrical Insulation, IEEE Transactions on

Electrical insulation common to the design and construction of components and equipment for use in electric and electronic circuits and distribution systems at all frequencies.



Most published Xplore authors for Image texture

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Xplore Articles related to Image texture

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Analyzing the bidirectional texture function

Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1998

The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis features that ...


Affine correlation

Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 1998

We propose a method for maximising the affine correlation between images. The method is more global in its search than for example steepest descent based methods. In a first approximation, there is no need to compute any derivatives and it is shown that the results are very good. The method is based on certain changes of coordinates in the images ...


The analysis and recognition of real-world textures in three dimensions

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as foreshortening, local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis textures ...


Texture fusion and feature selection applied to SAR imagery

IEEE Transactions on Geoscience and Remote Sensing, 1997

The discrimination ability of four different methods for texture computation in ERS SAR imagery is examined and compared. Feature selection methodology and discriminant analysis are applied to find the optimal combination of texture features. By combining features derived from different texture models, the classification accuracy increased significantly.


A Texture Extraction Method Based on Local Binary Pattern Operator

2006 6th World Congress on Intelligent Control and Automation, 2006

Most of image texture extraction methods are of high computational complexity, which hardly restricts their application in image processing fields. This paper proposes an improved local binary pattern operator to extract the image texture features. In the algorithm, single pixel is replaced with homogeneous object obtained by an object-oriented image segmentation method to exact image texture. At the same time, ...



Educational Resources on Image texture

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IEEE-USA E-Books

  • Analyzing the bidirectional texture function

    The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis features that are required to represent the space of observed textures for a surface as a function of ranges of illumination and viewing angles. Basis textures are represented using multiband correlation functions. We study properties of the dimensionality surface for real materials using the Columbia Utrecht Reflectance and Texture (CUReT) database. The analysis shows that the dependence of the dimensionality surface on ranges of illumination and viewing angles is approximately linear with a slope dependent on the complexity of the sample.

  • Affine correlation

    We propose a method for maximising the affine correlation between images. The method is more global in its search than for example steepest descent based methods. In a first approximation, there is no need to compute any derivatives and it is shown that the results are very good. The method is based on certain changes of coordinates in the images and extensive use of the fast Fourier transformation (FFT). This makes the method very fast, when implemented on a computer.

  • The analysis and recognition of real-world textures in three dimensions

    The observed image texture for a rough surface has a complex dependence on the illumination and viewing angles due to effects such as foreshortening, local shading, interreflections, and the shadowing and occlusion of surface elements. We introduce the dimensionality surface as a representation for the visual complexity of a material sample. The dimensionality surface defines the number of basis textures that are required to represent the observed textures for a sample as a function of ranges of illumination and viewing angles. Basis textures are represented using multiband correlation functions that consider both within and between color band correlations. We examine properties of the dimensionality surface for real materials using the Columbia Utrecht Reflectance and Texture (CUReT) database. The analysis shows that the dependence of the dimensionality surface on ranges of illumination and viewing angles is approximately linear with a slope that depends on the complexity of the sample. We extend the analysis to consider the problem of recognizing rough surfaces in color images obtained under unknown illumination and viewing geometry. We show, using a set of 12,505 images from 61 material samples, that the information captured by the multiband correlation model allows surfaces to be recognized over a wide range of conditions. We also show that the use of color information provides significant advantages for three-dimensional texture recognition.

  • Texture fusion and feature selection applied to SAR imagery

    The discrimination ability of four different methods for texture computation in ERS SAR imagery is examined and compared. Feature selection methodology and discriminant analysis are applied to find the optimal combination of texture features. By combining features derived from different texture models, the classification accuracy increased significantly.

  • A Texture Extraction Method Based on Local Binary Pattern Operator

    Most of image texture extraction methods are of high computational complexity, which hardly restricts their application in image processing fields. This paper proposes an improved local binary pattern operator to extract the image texture features. In the algorithm, single pixel is replaced with homogeneous object obtained by an object-oriented image segmentation method to exact image texture. At the same time, conditional probability is adopted as the parameter of image texture. The improvement makes it more valid to analysis object by fuzzy inference based image textures. In our experiments, the extracted image texture features are utilized for classifying images. The achieved good results indicate that the proposed method is faster than other methods while remaining close classification performance

  • MR image texture analysis applied to the diagnosis and tracking of Alzheimer's disease

    The authors assess the value of magnetic resonance (MR) image texture in Alzheimer's disease (AD) both as a diagnostic marker and as a measure of progression. T/sub 1/-weighted MR scans were acquired from 40 normal controls and 24 AD patients. These were split into a training set (20 controls, 40 AD) and a test set (20 controls, 14 AD). In addition, five control subjects and five AD patients were scanned repeatedly over several years. On each scan a texture feature vector was evaluated over the brain; this consisted of 260 measures derived from the spatial gray-level dependence method. A stepwise discriminant analysis was applied to the training set, to obtain a linear discriminant function. In the test set, this function yielded significantly different values for the control and AD groups (p<10/sup -4/) with only small group overlap; a classification rate of 91% was obtained. For the repeatedly scanned control subjects, the median increment in the discriminant function between successive scans of 0.12 was not significantly different from zero (p>0.05); for the repeatedly scanned AD patients the corresponding median increment of 1.4 was significantly different from zero (p<0.05). MR image texture may be a useful aid in the diagnosis and tracking of Alzheimer's disease.

  • High Resolution Satellite Image Texture for Moderate Relief Terrain Analysis

    None

  • Image Texture Classification Using Texture Spectrum and Local Binary Pattern

    None

  • Textural Analysis And Real-Time Classification of Sea-Ice Types Using Digital SAR Data

    Digital measures of synthetic-aperture radar (SAR) image texture, as well as the local approximation to the mean value of individual ice types, were used to perform discrimination and mapping of ice types. The SAR data described in this paper were gathered in March, 1979, over the Beaufort Sea as part of the Canadian SURSAT project. Digital SAR data from a 3 × 3 km area were obtained using optical processing of the signal film and digital recording of the output image. Prior to performing the textural analysis, a digital filter algorithm was developed that minimizes the effect of radar-system-generated coherent speckle and produces an image approximating local tone while preserving edge definition. This image was used in the analysis to separate image tone from image texture. The textural analysis, which included calculating the entropy and inertia of the image, indicated that first- and multiyear, smooth- and rough-ice types could be distinguished based on the textural values obtained from the data with an overall accuracy of 65 percent. This study has also considered the use of cellular operations based upon neighborhood transformations to calculate the textural values. This computation method can potentially reduce the time to compute textural features on a general-purpose computer to near real-time rates.

  • The angular orientation partition edge descriptor

    Edges are one of the most important image visual features. They are highly related with shapes and can also be representative of the image textures. Edge orientations histograms are usually very reliable descriptors suitable for image analysis, search and retrieval. In this work edges detected with Canny algorithm are described by their angular orientations. The resulting descriptor is resilient to image rotation and image translation. It is also resilient to noise. An example of automatic image semantic annotation using this description method is reported using a database with 738 images. The K Nearest Neighbor is used as classifier and the Manhattan distance is used for image similarity computation. The annotation that results with this description method is compared with the provided with other well known descriptors. These examples show that a reliable high level automatic description based in the semantic content can be extracted.



Standards related to Image texture

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No standards are currently tagged "Image texture"