Conferences related to Image classification

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (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 full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting

The joint meeting is intended to provide an international forum for the exchange of information on state of the art research in the area of antennas and propagation, electromagnetic engineering and radio science


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.

  • 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.

  • 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.

  • 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.

  • 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)

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

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

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


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 International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Image classification

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automation Science and Engineering, IEEE Transactions on

The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


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.


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Most published Xplore authors for Image classification

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Xplore Articles related to Image classification

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Session: pattern recognition

1988 IEEE International Symposium on Information Theory., 1988

The following topics are dealt with: directed divergence characterisation; rotation-invariant image classifiers; error rate smoothing; feature ordering and stopping rules.<<ETX>>


Application of remote sensing/GIS in the analysis of hill fire impact on vegetation resources in Hong Kong

Proceedings of IGARSS '93 - IEEE International Geoscience and Remote Sensing Symposium, 1993

Hill fires have been a severe threat to vegetation resources in country parks of Hong Kong particularly during dry hill fire seasons. The study analyzes the impact of hill fires on vegetation resources in country parks of Hong Kong using remote sensing and GIS techniques. Image classification is used to map vegetation types with SPOT multispectral and DEM data. A ...


Context-based multiscale classification of document images using wavelet coefficient distributions

IEEE Transactions on Image Processing, 2000

In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as ...


An Improved Remote Sensing Image Classification Based on K-Means Using HSV Color Feature

2014 Tenth International Conference on Computational Intelligence and Security, 2014

An improved classification method based on KMeans using HSV color feature is introduced in this paper. It is implemented by extracting three color features (hue, saturation, value) for K-Means clustering. Compared with the traditional K-Means clustering, the experimental results turn out that our proposed method is better than K-Means in classification accuracy and performance.


Research and analysis of hyperspectral remote sensing image classification algorithms

2016 Chinese Control and Decision Conference (CCDC), 2016

Hyperspectral remote sensing technology is applied to many fields because of its super-multiband, high resolution and vast information. Its classification technology is a research hotspot now. Abundant information is not utilized fully in traditional remote sensing image classification method; so many improved algorithms are disappeared in order to enhance efficiency, accuracy and intelligence. The hyperspectral remote sensing image processing flow ...


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Educational Resources on Image classification

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IEEE.tv Videos

Solving Sparse Representation for Image Classification using Quantum D-Wave 2X Machine - IEEE Rebooting Computing 2017
Computing Based on Material Training: Application to Binary Classification Problems - IEEE Rebooting Computing 2017
ICASSP 2012 Plenary-Dr. Stephane Mallat
Hamid R Tizhoosh - Fuzzy Image Processing
Zohara Cohen AMA EMBS Individualized Health
P2020 Establishing Image Quality Standards for Automotive
Tapping the Computing Power of the Unconscious Brain
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
CPIQ Update and the Case for Image Quality Standards in Automotive
Collaborative Filtering II
Learning with Kernels for Streams of Structured Data
Parallelized Linear Classification with Volumetric Chemical Perceptrons - Jacob Rosenstein - ICRC 2018
Broadband IQ, Image Reject, and Single Sideband Mixers: MicroApps 2015 - Marki Microwave
IEEE Low-Power Image Recognition Challenge (LPIRC)
Low Power Image Recognition: The Challenge Continues
Unconventional Superconductivity: From History to Mystery
Robotics History: Narratives and Networks Oral Histories: Ray Jarvis
Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition - Abbas Rahimi: 2016 International Conference on Rebooting Computing
Robotics History: Narratives and Networks Oral Histories: Minoru Asada
My Computer Speaks Colors! Fuzzy Color Spaces for Image Understanding, Description and Retrieval

IEEE-USA E-Books

  • Session: pattern recognition

    The following topics are dealt with: directed divergence characterisation; rotation-invariant image classifiers; error rate smoothing; feature ordering and stopping rules.<<ETX>>

  • Application of remote sensing/GIS in the analysis of hill fire impact on vegetation resources in Hong Kong

    Hill fires have been a severe threat to vegetation resources in country parks of Hong Kong particularly during dry hill fire seasons. The study analyzes the impact of hill fires on vegetation resources in country parks of Hong Kong using remote sensing and GIS techniques. Image classification is used to map vegetation types with SPOT multispectral and DEM data. A spatial database is developed incorporating multidate SPOT HRV data, DEM, multitemporal maps of hill fire records, drainage, park trails and roads, park facilities and vegetation maps. Overlay is used to examine the association of hill fires, various vegetation resources, topography and other environmental attributes. A logistic model is used to identify fire prone sites.<<ETX>>

  • Context-based multiscale classification of document images using wavelet coefficient distributions

    In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, and graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.

  • An Improved Remote Sensing Image Classification Based on K-Means Using HSV Color Feature

    An improved classification method based on KMeans using HSV color feature is introduced in this paper. It is implemented by extracting three color features (hue, saturation, value) for K-Means clustering. Compared with the traditional K-Means clustering, the experimental results turn out that our proposed method is better than K-Means in classification accuracy and performance.

  • Research and analysis of hyperspectral remote sensing image classification algorithms

    Hyperspectral remote sensing technology is applied to many fields because of its super-multiband, high resolution and vast information. Its classification technology is a research hotspot now. Abundant information is not utilized fully in traditional remote sensing image classification method; so many improved algorithms are disappeared in order to enhance efficiency, accuracy and intelligence. The hyperspectral remote sensing image processing flow is introduced. Merit, demerits and development tendency of classification method are clarified.

  • Image Classification Using No-balance Binary Tree Relevance Vector Machine

    Nowadays, Image classification method has been widely researched in the world. In this paper, we prepare four building categories for database. Firstly we use the Gabor filter for image processing to extract the image features, and then divide the images to different subregions for histogram-based Gabor features. At last, for image classification, Support Vector Machine (SVM) and Relevance Vector Machine (RVM) are known to outperform classical supervised classification algorithms. SVM has excellent performance to solve binary classification problems. RVM could be more sparsity than SVM. A new method based on relevance vector machine- No-balance Binary Tree Relevance Vector Machine (NBBTRVM) is proposed to define a class in this classification task. NBBTRVM could do a good performance according to our experiment results.

  • Research on data augmentation for image classification based on convolution neural networks

    The performance of deep convolution neural networks will be further enhanced with the expansion of the training data set. For the image classification tasks, it is necessary to expand the insufficient training image samples through various data augmentation methods. This paper explores the impact of various data augmentation methods on image classification tasks with deep convolution Neural network, in which Alexnet is employed as the pre-training network model and a subset of CIFAR10 and ImageNet (10 categories) are selected as the original data set. The data augmentation methods used in this paper include: GAN/WGAN, Flipping, Cropping, Shifting, PCA jittering, Color jittering, Noise, Rotation, and some combinations. Experimental results show that, under the same condition of multiple increasing, the performance evaluation on small-scale data sets is more obvious, the four individual methods (Cropping, Flipping, WGAN, Rotation) perform generally better than others, and some appropriate combination methods are slightly more effective than the individuals.

  • Citizen science in support of remote sensing research

    Remote sensing has much to gain from citizen sensing. This is particularly evident in relation to the provision of ground reference data for use in the training and testing stages of supervised image classification analyses used to generate thematic maps from remotely sensed data. Citizens are able to provide data over large geographical areas inexpensively, addressing potential problems connected with ground data samples and authoritative good practices. The great potential of citizen sensing is, however, constrained by concerns, notably with the quality of the data generated. This paper provides an overview of some of the key issues in citizen sensing to support thematic mapping from remote sensing. It highlights especially some of the ways that citizen sensing can aid remote sensing studies as a source of ground reference data.

  • Image Texture Classification Using Texture Spectrum and Local Binary Pattern

    None

  • DPA: a deterministic approach to the MAP problem

    Deterministic pseudo-annealing (DPA) is a new deterministic optimization method for finding the maximum a posteriori (MAP) labeling in a Markov random field, in which the probability of a tentative labeling is extended to a merit function on continuous labelings. This function is made convex by changing its definition domain. This unambiguous maximization problem is solved, and the solution is followed down to the original domain, yielding a good, if suboptimal, solution to the original labeling assignment problem. The performance of DPA is analyzed on randomly weighted graphs.<<ETX>>



Standards related to Image classification

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