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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Face spoofing detection using local binary patterns and Fisher Score

[{u'author_order': 1, u'affiliation': u'Laboratory of LAGE, University of Ouargla, Algeria', u'full_name': u'Azeddine Benlamoudi'}, {u'author_order': 2, u'affiliation': u'Laboratory of LAGE, University of Ouargla, Algeria', u'full_name': u'Djamel Samai'}, {u'author_order': 3, u'affiliation': u'Laboratory of LESIA, University of Biskra, Algeria', u'full_name': u'Abdelkrim Ouafi'}, {u'author_order': 4, u'affiliation': u'Laboratory of LESIA, University of Biskra, Algeria', u'full_name': u'Salah Eddine Bekhouche'}, {u'author_order': 5, u'affiliation': u'LAMIH, UMR CNRS 8201 UVHC, University of Valenciennes, France', u'full_name': u'Abdelmalik Taleb-Ahmed'}, {u'author_order': 6, u'affiliation': u'Center for Machine Vision Research, University of Oulu, Finland', u'full_name': u'Abdenour Hadid'}] 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), None

Todays biometric systems are vulnerable to spoof attacks made by non-real faces. The problem is when a person shows in front of camera a print photo or a picture from cell phone. We study in this paper an anti-spoofing solution for distinguishing between 'live' and 'fake' faces. In our approach we used overlapping block LBP operator to extract features in ...


Texture classification based low order local binary pattern for face recognition

[{u'author_order': 1, u'affiliation': u'Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, R.O.C', u'full_name': u'Ching-Te Chiu'}, {u'author_order': 2, u'affiliation': u'Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, R.O.C', u'full_name': u'Cyuan-Jhe Wu'}] 2011 18th IEEE International Conference on Image Processing, None

Local Binary Pattern (LBP) represents a circular derivative pattern generated by the concatenation of the binary gradient directions. However, the pattern fails to extract more detailed information such as texture feature contained in the input object. In this paper, we propose a texture classification based low order LBP for face recognition. With the texture feature, we could apply this method ...


Action Recognition in Motion Capture Data Using a Bag of Postures Approach

[{u'author_order': 1, u'affiliation': u'Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece', u'full_name': u'Ioannis Kapsouras'}, {u'author_order': 2, u'affiliation': u'Dept. of Inf., Aristotle Univ. of Thessaloniki, Thessaloniki, Greece', u'full_name': u'Nikos Nikolaidis'}] 2014 22nd International Conference on Pattern Recognition, None

In this paper we introduce a novel method for movement recognition in motion capture data. A movement is regarded as a combination of basic movement patterns, the so-called dynemes. Initially a K-means variant that takes into account the periodic nature of angular data is applied on training data to discover the most discriminative dynemes. Each frame is then assigned to ...


Impact of channel fading on mobility management in heterogeneous networks

[{u'author_order': 1, u'affiliation': u'Electrical and Computer Engineering, Florida International University, Miami, USA', u'full_name': u'Karthik Vasudeva'}, {u'author_order': 2, u'affiliation': u'Dresden University of Technology, Germany', u'full_name': u'Meryem \u015eimsek'}, {u'author_order': 3, u'affiliation': u'Bell Laboratories, Alcatel-Lucent, Ireland', u'full_name': u'David L\xf3pez-P\xe9rez'}, {u'author_order': 4, u'affiliation': u'Electrical and Computer Engineering, Florida International University, Miami, USA', u'full_name': u'\u0130smail G\xfcven\xe7'}] 2015 IEEE International Conference on Communication Workshop (ICCW), None

Handover decisions and mobility management in modern communication networks are carried out using signal measurements taken at user equipment (UE), obtained from neighboring base stations. Therefore, time and frequency selective characteristics of the radio propagation channel can seriously degrade the handover performance. In this paper, we investigate the impact of channel fading on the handover failure performance in densely deployed ...


Research and implementation of a real time approach to lip detection in video sequences

[{u'author_order': 1, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Jian-Ming Zhang'}, {u'author_order': 2, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Liang-Min Wang'}, {u'author_order': 3, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'De-Jiao Niu'}, {u'author_order': 4, u'affiliation': u'Sch. of Comput., JiangSu Univ., Zhenjiang, China', u'full_name': u'Yong-Zhao Zhan'}] Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693), None

Locating the lip in video sequences is one of the primary steps of the automatic lipreading system. In this paper a new approach to lip detection, which is based on Red Exclusion and Fisher transform, is presented. In this approach, firstly, we locate face region with skin-color model and motion correlation, then trisect the face image and take into account ...


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Educational Resources on Histograms

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eLearning

No eLearning Articles are currently tagged "Histograms"

IEEE-USA E-Books

  • Histogram Analysis

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

  • Calibration Techniques

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

  • 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.

  • 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.

  • 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

  • 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.

  • 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.

  • 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.

  • 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.



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