Conferences related to Histograms

Back to Top

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.


More Conferences

Periodicals related to Histograms

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.


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, ...


More Periodicals

Most published Xplore authors for Histograms

Back to Top

Xplore Articles related to Histograms

Back to Top

A novel network model based ICA filter for face recognition

[{u'author_order': 1, u'affiliation': u'School of Computer Science, Chengdu University of Information Technology, Chengdu, China, 610225', u'full_name': u'Yongqing Zhang'}, {u'author_order': 2, u'affiliation': u'School of Computer Science, Sichuan University, Chengdu, China, 610065', u'full_name': u'Tianyu Geng'}, {u'author_order': 3, u'affiliation': u'College of information engineering, Sichuan Agricultural University, Yaan 625014, P.R. China', u'full_name': u'Ying Cai'}] 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), None

Despite the great success of deep learning convolution networks, researchers are not yet clear about its feature learning mechanism and optimal network configuration. In this paper, we present a cascaded linear convolution network based on ICA filters, termed ICANet. ICANet mainly includes three parts: convolution layer, binary hash and block histogram. The results show that ICANet has a very good ...


An algorithm of key-frame extraction based on adaptive threshold detection of multi-features

[{u'author_order': 1, u'affiliation': u'Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, China', u'full_name': u'Min Huang'}, {u'author_order': 2, u'affiliation': u'Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, China', u'full_name': u'Huazhong Shu'}, {u'author_order': 3, u'affiliation': u'School of Computer and Communication Engineering, Zhengzhou University of Light Industry, China', u'full_name': u'Jing Jiang'}] 2009 International Conference on Test and Measurement, None

Key frame extraction is an important component of content-based video retrieval and directly influences on the efficiency of video retrieval. Nowadays some problems are existed in the algorithms of key-frame extraction, such as features selected singly, choosing threshold value difficultly and so on. This paper proposes a new key-frame extraction method which based on adaptive threshold detection of multi-features. First, ...


Unsupervised image segmentation based on the anisotropic texture information

[{u'author_order': 1, u'affiliation': u"Inst. of Intelligent Inf. Process., Xidian Univ., Xi'an, China", u'full_name': u'Cong Lin'}, {u'author_order': 2, u'affiliation': u"Inst. of Intelligent Inf. Process., Xidian Univ., Xi'an, China", u'full_name': u'Sha Yuheng'}, {u'author_order': 3, u'affiliation': u"Inst. of Intelligent Inf. Process., Xidian Univ., Xi'an, China", u'full_name': u'Hou Biao'}, {u'author_order': 4, u'affiliation': u"Inst. of Intelligent Inf. Process., Xidian Univ., Xi'an, China", u'full_name': u'Jiao Licheng'}] Image and Graphics (ICIG'04), Third International Conference on, None

Based on the anisotropic characteristic of brushlet, a new feature named directional texture histogram in brushlet domain is presented, which represents the local anisotropic information in image. A novel unsupervised image segmentation method via local directional texture histogram in the brushlet domain is developed. The segmentation results of synthetic mosaics, aerial photo and synthetic aperture radar (SAR) image show that ...


A New Model of Nature Images Based on Generalized Gaussian Distribution

[{u'author_order': 1, u'affiliation': u'Dept. of Inf. Sci., Univ. of Inf. Eng., Zhengzhou', u'full_name': u'Xu Mankun'}, {u'author_order': 2, u'affiliation': u'Dept. of Inf. Sci., Univ. of Inf. Eng., Zhengzhou', u'full_name': u'Li Tianyun'}, {u'author_order': 3, u'affiliation': u'Dept. of Inf. Sci., Univ. of Inf. Eng., Zhengzhou', u'full_name': u'Ping Xijian'}] 2009 WRI International Conference on Communications and Mobile Computing, None

We propose a new statistical model of nature images named 2D joint differential image histogram (JDIH). To simulate this model, we define a kind of 2D generalized Gaussian distribution (GGD) symmetrically in every direction by extending the 1D GGD function. The 2D JDIH and its estimated parameters can efficiently measure the image's inner and inter correlations of local areas in ...


Estimation of KL Divergence: Optimal Minimax Rate

[{u'author_order': 1, u'affiliation': u'ECE Department, Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL, USA', u'full_name': u'Yuheng Bu'}, {u'author_order': 2, u'affiliation': u'ECE Department, Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL, USA', u'full_name': u'Shaofeng Zou'}, {u'author_order': 3, u'affiliation': u'Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY, USA', u'full_name': u'Yingbin Liang'}, {u'author_order': 4, u'affiliation': u'ECE Department, Coordinated Science Laboratory, University of Illinois at Urbana–Champaign, Urbana, IL, USA', u'full_name': u'Venugopal V. Veeravalli'}] IEEE Transactions on Information Theory, 2018

The problem of estimating the Kullback-Leibler divergence D(P∥Q) between two unknown distributions P and Q is studied, under the assumption that the alphabet size k of the distributions can scale to infinity. The estimation is based on m independent samples drawn from P and n independent samples drawn from Q. It is first shown that there does not exist any ...


More Xplore Articles

Educational Resources on Histograms

Back to Top

eLearning

No eLearning Articles are currently tagged "Histograms"

IEEE-USA E-Books

  • 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.

  • Calibration Techniques

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

  • 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.

  • More Spatial Domain Features

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

  • Lane Detection and Tracking Problems in Lane Departure Warning Systems

    The chapter concerns the solutions of lane detection (LD) and lane tracking (LT) problems that are relevant in the implementation of lane departure warning systems (LDWSs), a kind of advanced driver assistance systems (ADASs) finalized to warn the driver that an imminent and possibly unintentional lane departure is taking place. The proposed solutions are based on simple image processing algorithms that work on the frames of a video stream of the oncoming road sections taken by a camera mounted on the front windshield of the vehicle. LD algorithms are finalized to identify the stripes demarcating the lane within a single frame, whereas LT algorithms try to track the demarcating stripes in subsequent frames of the stream. Final simulations on the software simulator CarSim 8 are also provided at the end of the chapter under realistic driving scenarios.

  • Histogram Analysis

    This chapter contains sections titled: * Early Histogram Analysis * Notation * Additive Independent Noise * Multi-dimensional Histograms * Experiment and Comparison

  • Image Clustering and Retrieval using MPEG‐7

    MPEG‐7 has been designed to cater to all encompassing multimedia applications including image, video, audio, and animation. A comprehensive set of audio‐visual tools have been provided to describe multimedia elements in machine readable form. These tools are expected to help multimedia applications in efficient searching, browsing, and filtering of digital media data. We have developed a multimodal image framework, which uses MPEG‐7 color descriptors as low‐level image features and combines the text annotations to create multimodal image representations for image clustering and retrieval applications.

  • Self-Organization in Image Retrieval

    This chapter provides a comprehensive study on modern approaches in the area of image indexing and retrieval on the use of Self-Organization as a core enabling technology. It begins with the development of Content-based image retrieval (CBIR) systems, which includes the implementation of a radial basis function (RBF) based relevance feedback (RF) method. The chapter presents automatic and semiautomatic methods in multimedia retrieval, using the pseudo- RF for minimizing user interaction in a retrieval process. It introduces a framework for a novel extension of the self-organizing tree map (SOTM) for hierarchical clustering, the Directed SOTM (DSOTM). It demonstrates an optimized architecture for an automatic retrieval system based on collaboration between the DSOTM and the Genetic Algorithm (GA). A study on the feasibility of the proposed feature weight detection scheme in conjunction with the DSOTM, SOTM, and self-organizing feature map (SOFM) classifier techniques is presented.

  • 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.

  • 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.



Standards related to Histograms

Back to Top

No standards are currently tagged "Histograms"


Jobs related to Histograms

Back to Top