Conferences related to Histograms

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2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)

conference on automatic analysis, recognition, and applications of human face and body gesture

  • 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes: advances in fundamental computer vision, pattern recognition and computer graphics; machine learning techniques relevant to face, gesture, and body motion; new algorithms and applications. The conference presents research that advances the state-of-the-art in these and related areas, leading to new capabilities in various application domains.

  • 2015 IEEE 11th International Conference on Automatic Face & Gesture Recognition (FG 2015)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes: advances in fundamental computer vision, pattern recognition and computer graphics; machine learning techniques relevant to face, gesture, and body motion; new algorithms and applications. The conference presents research that advances the state-of-the-art in these and related areas, leading to new capabilities in various application domains.

  • 2013 10th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2013)

    The IEEE conference on Automatic Face and Gesture Recognition is the premier international forum for research in image and video- based face, gesture, and body movement recognition. Its broad scope includes advances in fundamental computer vision, pattern recognition, computer graphics, and machine learning techniques relevant to face, gesture, and body action, new algorithms, and analysis of specific applications. The program will be single- track with poster sessions. Submissions will be rigorously reviewed and should clearly make the case for a documented improvement over the existing state of the art.

  • 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011)

    FG is the premier international forum for research and technology advances in image and video-based detection, modeling, and recognition of human faces and activity.

  • 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2008)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for state of the art image and video-based biometric gesture and body movement recognition including face Recognition/Analysis (tracking/detection, recognition, expression analysis, 3D analysis) gesture Recognition/Analysis (gesture interpretation, head tracking, arm/limb and body analysis/tracking), Body Motion Analysis (human motion analysis, gait recognition, 3d movement and gait analysis), etc.

  • 2006 7th International Conference on Automatic Face & Gesture Recognition (FG 2006)


2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2018 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions


2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

AVSS 2018 addresses underlying theory, methods, systems, and applications of video and signal based surveillance.


2018 24th International Conference on Pattern Recognition (ICPR)

ICPR will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields

  • 2016 23rd International Conference on Pattern Recognition (ICPR)

    ICPR'2016 will be an international forum for discussions on recent advances in the fields of Pattern Recognition, Machine Learning and Computer Vision, and on applications of these technologies in various fields.

  • 2014 22nd International Conference on Pattern Recognition (ICPR)

    ICPR 2014 will be an international forum for discussions on recent advances in the fields of Pattern Recognition; Machine Learning and Computer Vision; and on applications of these technologies in various fields.

  • 2012 21st International Conference on Pattern Recognition (ICPR)

    ICPR is the largest international conference which covers pattern recognition, computer vision, signal processing, and machine learning and their applications. This has been organized every two years by main sponsorship of IAPR, and has recently been with the technical sponsorship of IEEE-CS. The related research fields are also covered by many societies of IEEE including IEEE-CS, therefore the technical sponsorship of IEEE-CS will provide huge benefit to a lot of members of IEEE. Archiving into IEEE Xplore will also provide significant benefit to the all members of IEEE.

  • 2010 20th International Conference on Pattern Recognition (ICPR)

    ICPR 2010 will be an international forum for discussions on recent advances in the fields of Computer Vision; Pattern Recognition and Machine Learning; Signal, Speech, Image and Video Processing; Biometrics and Human Computer Interaction; Multimedia and Document Analysis, Processing and Retrieval; Medical Imaging and Visualization.

  • 2008 19th International Conferences on Pattern Recognition (ICPR)

    The ICPR 2008 will be an international forum for discussions on recent advances in the fields of Computer vision, Pattern recognition (theory, methods and algorithms), Image, speech and signal analysis, Multimedia and video analysis, Biometrics, Document analysis, and Bioinformatics and biomedical applications.

  • 2002 16th International Conference on Pattern Recognition


2018 25th IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE Signal Processing Society, is the premier forum for the presentation of technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. ICIP 2018, the 25th in the series that has been held annually since 1994, brings together leading engineers and scientists in image and video processing from around the world.


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Periodicals related to Histograms

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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.


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-- ...


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


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Most published Xplore authors for Histograms

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Xplore Articles related to Histograms

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Noise modeling for smoothing the colour histogram

[{u'author_order': 1, u'affiliation': u'Dept. of Electronic and Electrical Engineering, University of Surrey, Guildford GU2 5XH, United Kingdom', u'full_name': u'L. Shafarenko'}, {u'author_order': 2, u'affiliation': u'Dept. of Electronic and Electrical Engineering, University of Surrey, Guildford GU2 5XH, United Kingdom', u'full_name': u'M. Petrou'}, {u'author_order': 3, u'affiliation': u'Dept. of Electronic and Electrical Engineering, University of Surrey, Guildford GU2 5XH, United Kingdom', u'full_name': u'J. Kittler'}] 1996 8th European Signal Processing Conference (EUSIPCO 1996), 1996

In this paper we present a segmentation algorithm for colour images that uses the watershed algorithm to segment either the 2D or the 3D colour histogram of an image. For compliance with the way humans perceive colour, this segmentation has to take place in a perceptually uniform colour space like the Luv space. To avoid oversegmentation, the watershed algorithm has ...


Scintillation caused by the ionosphere with non-Gaussian statistics of irregularities

[{u'author_order': 1, u'affiliation': u'Space Research Centre, Polish Academy of Science, Warsaw, Poland.', u'full_name': u'A. W. Wernik'}, {u'author_order': 2, u'affiliation': u'Space Research Centre, Polish Academy of Science, Warsaw, Poland.', u'full_name': u'M. Grzesiak'}] Radio Science, 2011

In situ measurements indicate that the probability distribution function (pdf) of plasma density fluctuations on scales of importance to scintillation is far from the Gaussian and resemble the Laplace (double exponential) distribution. Radio wave propagation in the ionosphere with irregularities subject to the Gaussian and Laplace pdf has been modeled using the multiple two-dimensional phase screen method. The Chapman altitude ...


Automatic enhancement of chest radiography using-Retinex processing

[{u'author_order': 1, u'full_name': u'N. Iqbal'}] International Multi Topic Conference, 2002. Abstracts. INMIC 2002., 2002

None


IEEE Draft Standard on Transitions, Pulses, and Related Waveforms

[] IEEE P181/D02, November 2010, 2010

This standard presents approximately 100 terms, and their definitions, for accurately and precisely describing the waveforms of pulse signals and the process of measuring pulse signals. Algorithms are provided for computing the values of defined terms that describe measurable parameters of the waveform, such as transition duration, state level, pulse amplitude, and waveform aberrations. These analysis algorithms are applicable to ...


Quantization and Coding

[{u'author_order': 1, u'full_name': u'John W. Leis'}] Communication Systems Principles Using MATLAB, None

This chapter discusses principles of scalar quantization and explains the operation of a vector quantization. It explores the principles of minimum‐redundancy code word assignment and provides the important algorithm classes for lossless. The chapter also explains several image compression approaches, including the Discrete Cosine Transform. It helps the reader to understand the basic approach to waveform and parametric speech encoding ...


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Educational Resources on Histograms

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eLearning

No eLearning Articles are currently tagged "Histograms"

IEEE-USA E-Books

  • Quantization and Coding

    This chapter discusses principles of scalar quantization and explains the operation of a vector quantization. It explores the principles of minimum‐redundancy code word assignment and provides the important algorithm classes for lossless. The chapter also explains several image compression approaches, including the Discrete Cosine Transform. It helps the reader to understand the basic approach to waveform and parametric speech encoding and then explains the advantages and disadvantages of each. The chapter also explores the key requirements for audio encoders and the building blocks that go to make up an audio encoding system. It reviews some of the notions of probability, which are useful in modeling errors in communication channels, as well as difference equations, which are used extensively in signal encoding. The chapter gives an overview of coding: Image Coding; Source Coding; and Speech and Audio Coding. It also discusses digital channel capacity.

  • Applications in Computer Vision, Image Retrieval and Robotics

    In this chapter, we begin to switch our focus from the visual attention modelling of Chapters 3-6 to the applications of these models. In Chapter 7, we first introduce the conventional engineering methods for object detection and recognition in Section 7.1. Then attention modelling combined with object detection and recognition for natural scenes is presented in Section 7.2. Since satellite images are different from natural images, in Section 7.3 we introduce the attention assisted object detection and recognition for satellite images. Section 7.4 presents image retrieval via visual attention. Another application of visual attention is presented finally for robots. This chapter does not try to introduce all aspects and works related to computer vision, image retrieval and robotics based on visual attention, but only demonstrates some typical methods of combining visual attention with conventional engineering methods. Readers can infer other aspects from these introduced applications.

  • Statistics and Probability

    This chapter contains sections titled:Descriptive Statistics: Population, Sample, Central Tendency, and DispersionProbabilityRandom VariableInferential StatisticsReview Questions

  • Input Modeling and Output Analysis

    None

  • Correlator-Based Maximum Likelihood Detection

    This chapter investigates the statistical properties of additive white Gaussian noise (AWGN) in the vector space. It implements a correlation-based maximum likelihood detector. The chapter provides step-by-step code exercises and instructions to implement execution sequences. In the m-file, one generates rt for the case where only the AWGN is received and replace the original received signal rt saved in st_and_rt.mat. In this case the sample length of rt is set to 100,000 times L, which is the sample length of 4-ary symbols. The chapter investigates the effect of the orthogonal basis vectors on the noise vector. If the basis vectors in the vector space are mutually orthogonal, then the elements of the Gaussian noise vector in the vector space are independent of one another. The chapter is designed to help teach and understand communication systems using a classroom-tested, active learning approach.

  • Probability and Random Variables

    This chapter reviews uniform and Gaussian random variables (RVs). It describes the empirical probability density function (PDF) of RVs and provides its comparison with the theoretical PDF. Using MATLAB functions such as random(), rand(), and randn(), the authors generate various kinds of RVs. Although the built-in function histogram() is convenient for generating the empirical distribution, the chapter provides the detailed steps to obtain the distribution to gain an in-depth understating of the PDF concept. The MATLAB function randn, every time it is invoked, generates a sample of the Gaussian RV with zero mean and unit variance. The mean and the variance are calculated using numerical integration. The chapter also discusses Rayleigh fading model, which is one of the commonly encountered fading channel models in wireless communications. The chapter is designed to help teach and understand communication systems using a classroom-tested, active learning approach.

  • More Spatial Domain Features

    This chapter contains sections titled: * The Difference Matrix * Image Quality Measures * Colour Images * Experiment and Comparison

  • Feature Extraction for Multimedia Analysis

    None

  • Calibration Techniques

    This chapter contains sections titled: * Calibrated Features * JPEG Calibration * Calibration by Downsampling * Calibration in General * Progressive Randomisation

  • Error Estimation for Discrete Classification

    The study of error estimation for discrete classifiers is a fertile topic, as analytical characterizations of performance are often possible due to the simplicity of the problem. This chapter provides the definitions and simple properties of the main error estimators for the discrete histogram rule, which is the most important example of a discrete classification rule. The error estimators discussed in the chapter include resubstitution error, leave-one- out error, cross-validation error, and bootstrap error estimator. A detailed analytical study of small-sample performance in terms of bias, deviation variance, RMS, and correlation coefficient between true and estimated errors is presented. The chapter illustrates the exact bias, deviation standard deviation, and RMS of resubstitution and leave-one-out, plotted as functions of the number of bins. For comparison, Monte-Carlo estimates of 10 repetitions of 4-fold cross-validation are also plotted. The chapter also presents a complete enumeration approach and analyses large-sample performance.



Standards related to Histograms

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