Conferences related to Image segmentation

Back to Top

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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

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


2016 IEEE International Conference on Image Processing (ICIP)

Signal processing, image processing, biomedical imaging, multimedia, video, multidemensional.


2013 21st International Conference on Geoinformatics

GIS in Regional Economic Development and Environmental Protection under Globalization


2013 IEEE International Conference on Computer Vision (ICCV)

Latest research in Computer Vision, including medical imaging, surveillance, tracking, 3D vision, and vision-based graphics.

  • 2011 IEEE International Conference on Computer Vision (ICCV)

    The International Conference on Computer Vision is the prime event in the area of computer vision. High-quality papers are accepted on both theoretical foundations and practical applications. It covers subfields like object recognition, 3D acquisition and modeling, computational photography, tracking and gesture analysis, and image filtering and enhancement.

  • 2009 IEEE 12th International Conference on Computer Vision (ICCV)

    Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and Tracking Stereo and Structure from Motion Image-Based Modeling Physics-Based Modeling Statistical Methods and Learning in Vision Video Surveillance and Monitoring Object, Event and Scene Recognition Vision-Based Graphics Image and Video Retrieval Performance Evaluation Applications


2012 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)

The Southwest Symposium on Image Analysis and Interpretation (SSIAI) is a biennial conference dedicated to all aspects of computational analysis and interpretation of images and video. SSIAI brings together researchers and practitioners in academia, industry, and government to share and discuss the latest advances in this field.

  • 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)

    The Southwest Symposium on Image Analysis and Interpretation (SSIAI) is a biennial conference dedicated to all aspects of computational analysis and interpretation of images and video. SSIAI brings together researchers and practitioners in academia, industry, and government to share and discuss the latest advances in this field. SSIAI 2010 will be held at the spectacular Omni Austin Downtown Hotel in Austin, Texas USA. The symposium seeks original contributions reporting novel research directions, results,

  • 2008 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI)

    Computational image and video analysis and interpretation continues to be an exciting and dynamic research area. SSIAI-2008 will bring together researchers and practitioners to share and discuss the latest advances in this field. The biennial symposium seeks original contributions reporting novel research directions and exploratory applications.


More Conferences

Periodicals related to Image segmentation

Back to Top

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.


Image Processing, IEEE Transactions on

Signal-processing aspects of image processing, imaging systems, and image scanning, display, and printing. Includes theory, algorithms, and architectures for image coding, filtering, enhancement, restoration, segmentation, and motion estimation; image formation in tomography, radar, sonar, geophysics, astronomy, microscopy, and crystallography; image scanning, digital half-toning and display, andcolor reproduction.


Information Technology in Biomedicine, IEEE Transactions on

Telemedicine, teleradiology, telepathology, telemonitoring, telediagnostics, 3D animations in health care, health information networks, clinical information systems, virtual reality applications in medicine, broadband technologies, and global information infrastructure design for health care.


Pattern Analysis and Machine Intelligence, IEEE Transactions on

Statistical and structural pattern recognition; image analysis; computational models of vision; computer vision systems; enhancement, restoration, segmentation, feature extraction, shape and texture analysis; applications of pattern analysis in medicine, industry, government, and the arts and sciences; artificial intelligence, knowledge representation, logical and probabilistic inference, learning, speech recognition, character and text recognition, syntactic and semantic processing, understanding natural language, expert systems, ...



Most published Xplore authors for Image segmentation

Back to Top

Xplore Articles related to Image segmentation

Back to Top

A Model for Indexing Videos and Still Images from the Moroccan Cultural Heritage

Ibrahima Mbaye; Rachid Oulad Haj Thami; Jose Martinez 2005 IEEE 7th Workshop on Multimedia Signal Processing, 2005

In this paper, we propose a flexible (meta) model for indexing videos, applied to the Moroccan cultural heritage. The model is rather generic, able to describe documentaries, advertisements, movies, and other video types. Our goal is to develop a tool for navigating into a joint database of video and related images, based both on visual contents and manual annotations. We ...


Improved color and intensity patch segmentation for human full-body and body-parts detection and tracking

Hai-Wen Chen; Mike McGurr Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, 2014

This paper presents a new way for detection and tracking of human full-body and body-parts (head, torso, arms, and legs) with color and intensity patch segmentation. The original R, G, and B are transformed to H (hue), S (saturation), and V (value) domain, as well as to Y, I, and Q for the NTSC system. With the help of morphological ...


QR code detection using convolutional neural networks

Tzu-Han Chou; Chuan-Sheng Ho; Yan-Fu Kuo Advanced Robotics and Intelligent Systems (ARIS), 2015 International Conference on, 2015

Barcodes have been long used for data storage. Detecting and locating barcodes in images of complex background is an essential yet challenging step in the process of automatic barcode reading. This work proposed an algorithm that localizes and segments two-dimensional quick response (QR) barcodes. The localization involved a convolutional neural network that could detect partial QR barcodes. Majority voting was ...


P5C-3 Field Simulation Parameters Design for Realistic Statistical Parameters of Radio - Frequency Ultrasound Images

H. Liebgott; O. Bernard; C. Cachard; D. Friboulet Ultrasonics Symposium, 2007. IEEE, 2007

In this paper we present a study for designing realistic ultrasound image simulations from a statistical point of view. Indeed it is extremely important to be able to compute realistic simulated images for validating segmentation or classification methods based on the statistics of ultrasound images. The statistics of the radio frequency (RF) signals is modeled by a distribution called K-RF ...


Robust color image segmentation based on mean shift and marker-controlled watershed algorithm

Chen Pan; Cong-Xun Zheng; Hao-Jun Wang Machine Learning and Cybernetics, 2003 International Conference on, 2003

A new method for color image segmentation is presented. It combines mean shift with watershed algorithm to get robust results. First, mean shift procedure is used to find the highest density regions which correspond to clusters centered on the modes (local maxima) of the underlying probability distribution in the feature space. The principal component of each significant color is extracted ...


More Xplore Articles

Educational Resources on Image segmentation

Back to Top

eLearning

A Model for Indexing Videos and Still Images from the Moroccan Cultural Heritage

Ibrahima Mbaye; Rachid Oulad Haj Thami; Jose Martinez 2005 IEEE 7th Workshop on Multimedia Signal Processing, 2005

In this paper, we propose a flexible (meta) model for indexing videos, applied to the Moroccan cultural heritage. The model is rather generic, able to describe documentaries, advertisements, movies, and other video types. Our goal is to develop a tool for navigating into a joint database of video and related images, based both on visual contents and manual annotations. We ...


Improved color and intensity patch segmentation for human full-body and body-parts detection and tracking

Hai-Wen Chen; Mike McGurr Advanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on, 2014

This paper presents a new way for detection and tracking of human full-body and body-parts (head, torso, arms, and legs) with color and intensity patch segmentation. The original R, G, and B are transformed to H (hue), S (saturation), and V (value) domain, as well as to Y, I, and Q for the NTSC system. With the help of morphological ...


QR code detection using convolutional neural networks

Tzu-Han Chou; Chuan-Sheng Ho; Yan-Fu Kuo Advanced Robotics and Intelligent Systems (ARIS), 2015 International Conference on, 2015

Barcodes have been long used for data storage. Detecting and locating barcodes in images of complex background is an essential yet challenging step in the process of automatic barcode reading. This work proposed an algorithm that localizes and segments two-dimensional quick response (QR) barcodes. The localization involved a convolutional neural network that could detect partial QR barcodes. Majority voting was ...


P5C-3 Field Simulation Parameters Design for Realistic Statistical Parameters of Radio - Frequency Ultrasound Images

H. Liebgott; O. Bernard; C. Cachard; D. Friboulet Ultrasonics Symposium, 2007. IEEE, 2007

In this paper we present a study for designing realistic ultrasound image simulations from a statistical point of view. Indeed it is extremely important to be able to compute realistic simulated images for validating segmentation or classification methods based on the statistics of ultrasound images. The statistics of the radio frequency (RF) signals is modeled by a distribution called K-RF ...


Robust color image segmentation based on mean shift and marker-controlled watershed algorithm

Chen Pan; Cong-Xun Zheng; Hao-Jun Wang Machine Learning and Cybernetics, 2003 International Conference on, 2003

A new method for color image segmentation is presented. It combines mean shift with watershed algorithm to get robust results. First, mean shift procedure is used to find the highest density regions which correspond to clusters centered on the modes (local maxima) of the underlying probability distribution in the feature space. The principal component of each significant color is extracted ...


More eLearning Resources

IEEE.tv Videos

No IEEE.tv Videos are currently tagged "Image segmentation"

IEEE-USA E-Books

  • Recognition of 3D Objects in Aerial Images Based on Generic Models

    Availability of actual three-dimensional data for geo-information systems has become of great importance for an increasing number of tasks. Since the acquisition of such data is mainly done with the help of semi-automatic tools so far, a large research program called "Semantic Modeling" was started 3 years ago with the aim of improving image interpretation by incorporating application domain knowledge represented by explicit models. In our sub- project we apply CLP for the recognition of 3D objects (i.e. buildings) in aerial images. Logic programming constitutes the platform for the representation of image and object models and the control strategy of the reasoning process. Generic 3D models (constructive solid geometry (CSG), augmented by constraints) are applied to represent the large number of different building types on the one hand. Image segmentation results in features of different classes, giving a symbolic 2D image description on the other hand. In order to match object models to image data, a third kind of model (aspect graph) is used, bridging the gap between the 3D volumetric and 2D image data. Such aspect graphs are transformed to CLP clauses, and matching is done by solving the resp. CSP. Our current prototype is based on ECLIPSE and extends the built-in CLP(FD) solver to cope with complex objects.

  • Herding for Structured Prediction

    This chapter contains sections titled: 8.1 Introduction, 8.2 Integrating Local Models Using Herding, 8.3 Application: Image Segmentation, 8.4 Application: Go Game Prediction, 8.5 Conclusion, 8.6 References

  • Segmentation of Brain Magnetic Resonance Images

    This chapter presents the application of different rough-fuzzy clustering algorithms for segmentation of brain magnetic resonance (MR) images. One of the important issues of the partitive-clustering-algorithm-based brain MR image segmentation method is the selection of initial prototypes of different classes or categories. The concept of discriminant analysis, based on the maximization of class separability, is used to circumvent the initialization and local minima problems of the partitive clustering algorithms. The chapter first deals with the pixel classification problem, and then gives an overview of the feature extraction techniques employed in segmentation of brain MR images, along with the initialization method of c-means algorithm based on the maximization of class separability. It presents implementation details, experimental results, and a comparison among different c-means algorithms. The algorithms compared are hard c-means (HCM), fuzzy c-means (FCM), possibilistic c-means (PCM), FPCM, rough c-means (RCM), and rough-fuzzy c-means (RFCM). fuzzy set theory; image classification; image segmentation; magnetic resonance imaging; pattern clustering; rough set theory

  • Image Segmentation

    This chapter contains sections titled: Introduction Intensity-based Segmentation Region-based Segmentation Watershed Segmentation Tutorial 15.1: Image Thresholding Problems

  • Image Segmentation

    This chapter contains sections titled: Edge-Based Image Segmentation Pixel-Based Direct Classification Methods Region-Based Segmentation Advanced Segmentation Methods Exercises References

  • Image Segmentation and Representation

    This chapter contains sections titled: Image Thresholding Edge, Line, and Point Detection Region Based Segmentation Image Representation

  • Fundamental Limitations of Spectral Clustering

    Spectral clustering methods are common graph-based approaches to clustering of data. Spectral clustering algorithms typically start from local information encoded in a weighted graph on the data and cluster according to the global eigenvectors of the corresponding (normalized) similarity matrix. One contribution of this paper is to present fundamental limitations of this general local to global approach. We show that based only on local information, the normalized cut functional is not a suitable measure for the quality of clustering. Further, even with a suitable similarity measure, we show that the first few eigenvectors of such adjacency matrices cannot successfully cluster datasets that contain structures at different scales of size and density. Based on these findings, a second contribution of this paper is a novel diffusion based measure to evaluate the coherence of individual clusters. Our measure can be used in conjunction with any bottom-up graph- based clustering method, it is scale-free and can determine coherent clusters at all scales. We present both synthetic examples and real image segmentation problems where various spectral clustering algorithms fail. In contrast, using this coherence measure finds the expected clusters at all scales.

  • Soft Learning Vector Quantization and Clustering Algorithms Based on Reformulation

    This chapter contains sections titled: Introduction Clustering Algorithms Reformulating Fuzzy Clustering Generalized Reformulation Function Constructing Reformulation Functions: Generator Functions Constructing Admissible Generator Functions From Generator Functions to LVQ and Clustering Algorithms Soft LVQ and Clustering Algorithms Based on Nonlinear Generator Functions Initialization of Soft LVQ and Clustering Algorithms Magnetic Resonance Image Segmentation Conclusions This chapter contains sections titled: Acknowledgments References

  • No title

    The sequel to the popular lecture book entitled Biomedical Image Analysis: Tracking, this book on Biomedical Image Analysis: Segmentation tackles the challenging task of segmenting biological and medical images. The problem of partitioning multidimensional biomedical data into meaningful regions is perhaps the main roadblock in the automation of biomedical image analysis. Whether the modality of choice is MRI, PET, ultrasound, SPECT, CT, or one of a myriad of microscopy platforms, image segmentation is a vital step in analyzing the constituent biological or medical targets. This book provides a state-of-the-art, comprehensive look at biomedical image segmentation that is accessible to well-equipped undergraduates, graduate students, and research professionals in the biology, biomedical, medical, and engineering fields. Active model methods that have emerged in the last few years are a focus of the book, including parametric active contour and active surface models, active shape models and geometric active contours that adapt to the image topology. Additionally, Biomedical Image Analysis: Segmentation details attractive new methods that use graph theory in segmentation of biomedical imagery. Finally, the use of exciting new scale space tools in biomedical image analysis is reported. Table of Contents: Introduction / Parametric Active Contours / Active Contours in a Bayesian Framework / Geometric Active Contours / Segmentation with Graph Algorithms / Scale-Space Image Filtering for Segmentation

  • Simplifying Mixture Models through Function Approximation

    Finite mixture model is a powerful tool in many statistical learning problems. In this paper, we propose a general, structure-preserving approach to reduce its model complexity, which can bring significant computational benefits in many applications. The basic idea is to group the original mixture components into compact clusters, and then minimize an upper bound on the approximation error between the original and simplified models. By adopting the L2 norm as the distance measure between mixture models, we can derive closed-form solutions that are more robust and reliable than using the KL-based distance measure. Moreover, the complexity of our algorithm is only linear in the sample size and dimensionality. Experiments on density estimation and clustering-based image segmentation demonstrate its outstanding performance in terms of both speed and accuracy.



Standards related to Image segmentation

Back to Top

No standards are currently tagged "Image segmentation"


Jobs related to Image segmentation

Back to Top