Conferences related to Histograms

Back to Top

2016 IEEE International Conference on Image Processing (ICIP)

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


2012 8th International Conference on Natural Computation (ICNC)

ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering and more.

  • 2011 Seventh International Conference on Natural Computation (ICNC)

    ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering, and more.

  • 2010 Sixth International Conference on Natural Computation (ICNC)

    ICNC is an international forum on intelligent systems inspired from nature, particularly neural, biological, and nonlinear systems, with applications to signal processing, communications, biomedical engineering, and more.


2006 International Conference on Machine Learning and Cybernetics (ICMLC)



Periodicals related to Histograms

Back to Top

Medical Imaging, IEEE Transactions on

Imaging methods applied to living organisms with emphasis on innovative approaches that use emerging technologies supported by rigorous physical and mathematical analysis and quantitative evaluation of performance.


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, ...


Signal Processing Letters, IEEE

Rapid dissemination of new results in signal processing world-wide.


Visualization and Computer Graphics, IEEE Transactions on

Specific topics include, but are not limited to: a) visualization techniques and methodologies; b) visualization systems and software; c) volume visulaization; d) flow visualization; e) information visualization; f) multivariate visualization; g) modeling and surfaces; h) rendering techniques and methodologies; i) graphics systems and software; j) animation and simulation; k) user interfaces; l) virtual reality; m) visual programming and program visualization; ...



Most published Xplore authors for Histograms

Back to Top

Xplore Articles related to Histograms

Back to Top

A complementary local feature descriptor for face identification

Jonghyun Choi; William Robson Schwartz; Huimin Guo; Larry S. Davis 2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012

In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric- Pairs of Pixels (CCS-POP). It concatenates the ...


Performance of MPEG-7 edge histogram descriptor in face recognition using Principal Component Analysis

Shafin Rahman; Sheikh Motahar Naim; Abdullah Al Farooq; Md. Monirul Islam 2010 13th International Conference on Computer and Information Technology (ICCIT), 2010

Face recognition is considered as a high dimensionality problem. To handle high dimensionality, a numerous methods have been proposed in literature. In this paper, we propose a novel face recognition method that efficiently solves that problem using MPEG-7 edge histogram descriptor. To the authors' knowledge, this is the first attempt to use edge histogram descriptor in face recognition. Although MPEG-7 ...


An Enhanced Queries Scheduler for query processing over a cloud environment

Eman A. Maghawry; Rasha M. Ismail; Nagwa L. Badr; M. F. Tolba 2014 9th International Conference on Computer Engineering & Systems (ICCES), 2014

Due to the existence of the "database as a service" (DaaS) model on a cloud computing environment, several challenges have been made, such as query scheduling. Using an efficient query scheduler can improve the queries response time submitted from various clients in a DaaS model. Scheduling the queries in a cost aware way has an economic impact on the service ...


Group context learning for event recognition

Yimeng Zhang; Weina Ge; Ming-Ching Chang; Xiaoming Liu 2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012

We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, following, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted ...


A modified histogram bit synchronization algorithm for GNSS receivers

Sichao Li; Jinhai Sun; Jinhai Li; Yuepeng Yan The 2nd International Conference on Information Science and Engineering, 2010

This paper modifies the histogram bit synchronization algorithm. The expressions on probability of both synchronization and false alarm of histogram method have been deduced, and the quantity of measured bits as well as thresholds with best performance is acquired. Compared with results from relational literature, the average time of bit synchronization has almost been shortened by 40%.


More Xplore Articles

Educational Resources on Histograms

Back to Top

eLearning

A complementary local feature descriptor for face identification

Jonghyun Choi; William Robson Schwartz; Huimin Guo; Larry S. Davis 2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012

In many descriptors, spatial intensity transforms are often packed into a histogram or encoded into binary strings to be insensitive to local misalignment and compact. Discriminative information, however, might be lost during the process as a trade-off. To capture the lost pixel-wise local information, we propose a new feature descriptor, Circular Center Symmetric- Pairs of Pixels (CCS-POP). It concatenates the ...


Performance of MPEG-7 edge histogram descriptor in face recognition using Principal Component Analysis

Shafin Rahman; Sheikh Motahar Naim; Abdullah Al Farooq; Md. Monirul Islam 2010 13th International Conference on Computer and Information Technology (ICCIT), 2010

Face recognition is considered as a high dimensionality problem. To handle high dimensionality, a numerous methods have been proposed in literature. In this paper, we propose a novel face recognition method that efficiently solves that problem using MPEG-7 edge histogram descriptor. To the authors' knowledge, this is the first attempt to use edge histogram descriptor in face recognition. Although MPEG-7 ...


An Enhanced Queries Scheduler for query processing over a cloud environment

Eman A. Maghawry; Rasha M. Ismail; Nagwa L. Badr; M. F. Tolba 2014 9th International Conference on Computer Engineering & Systems (ICCES), 2014

Due to the existence of the "database as a service" (DaaS) model on a cloud computing environment, several challenges have been made, such as query scheduling. Using an efficient query scheduler can improve the queries response time submitted from various clients in a DaaS model. Scheduling the queries in a cost aware way has an economic impact on the service ...


Group context learning for event recognition

Yimeng Zhang; Weina Ge; Ming-Ching Chang; Xiaoming Liu 2012 IEEE Workshop on the Applications of Computer Vision (WACV), 2012

We address the problem of group-level event recognition from videos. The events of interest are defined based on the motion and interaction of members in a group over time. Example events include group formation, dispersion, following, chasing, flanking, and fighting. To recognize these complex group events, we propose a novel approach that learns the group-level scenario context from automatically extracted ...


A modified histogram bit synchronization algorithm for GNSS receivers

Sichao Li; Jinhai Sun; Jinhai Li; Yuepeng Yan The 2nd International Conference on Information Science and Engineering, 2010

This paper modifies the histogram bit synchronization algorithm. The expressions on probability of both synchronization and false alarm of histogram method have been deduced, and the quantity of measured bits as well as thresholds with best performance is acquired. Compared with results from relational literature, the average time of bit synchronization has almost been shortened by 40%.


More eLearning Resources

IEEE-USA E-Books

  • Independence and Probability Density Functions

    This chapter contains sections titled: Introduction, Histograms, Histograms and Probability Density Functions, The Central Limit Theorem, Cumulative Density Functions, Moments: Mean, Variance, Skewness and Kurtosis, Independence and Correlation, Uncorrelated Pendulums, Summary

  • BER Statistical Measurements

    This chapter contains sections titled: Introduction Sampling and Statistical Histograms Statistical Sampling for BER Estimating BER, Q-Factor, and SNR The BER Circuit Performance of the BER Circuit Other Performance Metrics References Standards

  • No title

    Outlier (or anomaly) detection is a very broad field which has been studied in the context of a large number of research areas like statistics, data mining, sensor networks, environmental science, distributed systems, spatio-temporal mining, etc. Initial research in outlier detection focused on time series- based outliers (in statistics). Since then, outlier detection has been studied on a large variety of data types including high-dimensional data, uncertain data, stream data, network data, time series data, spatial data, and spatio- temporal data. While there have been many tutorials and surveys for general outlier detection, we focus on outlier detection for temporal data in this book. A large number of applications generate temporal datasets. For example, in our everyday life, various kinds of records like credit, personnel, financial, judicial, medical, etc., are all temporal. This stresses the need for an organized and detailed study of outliers with respect to such temporal data. In the past decade, there has been a lot of research on various forms of temporal data including consecutive data snapshots, series of data snapshots and data streams. Besides the initial work on time series, researchers have focused on rich forms of data including multiple data streams, spatio-temporal data, network data, community distribution data, etc. Compared to general outlier detection, techniques for temporal outlier detection are very different. In this book, we will present an organized picture of both recent and past research in temporal outlier detection. We start with the basics and then ramp up the reader to the main ideas in state-of-the-art outlier detection techniques. We motivate the importance of temporal outlier detection and brief the challenges beyond usual outlier detection. Then, we list down a taxonomy of proposed techniques for temporal outlier detection. Such techniques broadly include statistical techniques (like AR models, Markov models, histograms, neura networks), distance- and density-based approaches, grouping-based approaches (clustering, community detection), network-based approaches, and spatio-temporal outlier detection approaches. We summarize by presenting a wide collection of applications where temporal outlier detection techniques have been applied to discover interesting outliers.

  • Histogram Analysis

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

  • Generate Quality Graphs

    This chapter covers the most effective ways to display data in various graphical forms. It also showcases the kinds of visuals that engineers, scientists, and technical experts use daily. Identifying the typical design traps that ensnare even the most well-intentioned professionals can help us avoid creating junk and, instead, propel us toward elegance and simplicity. Whether the presenter is creating a plot, a chart, or a table, there are a few aspects of design that can ensure success for the visual. These basic tenets can save embarrassment and audience frustration if applied well during the planning process. Establishing a baseline of best practices for pie charts enables avoiding many of the typical mistakes that can happen with this simple chart type. Bar charts and histograms appear frequently in engineering, scientific, and technical presentations and papers because they provide visual comparison that the user can digest quickly and easily.

  • No title

    <p>Over the last decade, differential privacy (DP) has emerged as the de facto standard privacy notion for research in privacy-preserving data analysis and publishing. The DP notion offers strong privacy guarantee and has been applied to many data analysis tasks.</p><p>This Synthesis Lecture is the first of two volumes on differential privacy. This lecture differs from the existing books and surveys on differential privacy in that we take an approach balancing theory and practice. We focus on empirical accuracy performances of algorithms rather than asymptotic accuracy guarantees. At the same time, we try to explain why these algorithms have those empirical accuracy performances. We also take a balanced approach regarding the semantic meanings of differential privacy, explaining both its strong guarantees and its limitations.</p><p>We start by inspecting the definition and basic properties of DP, and the main primitives for achieving DP. Then, w give a detailed discussion on the the semantic privacy guarantee provided by DP and the caveats when applying DP. Next, we review the state of the art mechanisms for publishing histograms for low-dimensional datasets, mechanisms for conducting machine learning tasks such as classification, regression, and clustering, and mechanisms for publishing information to answer marginal queries for high-dimensional datasets. Finally, we explain the sparse vector technique, including the many errors that have been made in the literature using it.</p><p>The planned Volume 2 will cover usage of DP in other settings, including high-dimensional datasets, graph datasets, local setting, location privacy, and so on. We will also discuss various relaxations of DP.</p>

  • Inserting Illustrations into Reports

    Illustrations appear mostly in longer, more formal reports, such as analyses, feasibility studies, proposals, and investigation or evaluation reports. For selecting and designing the most effective illustration for a given situation, three issues need to be clarified: which the kind of illustration (tables, graphs, bar charts, histograms, surface charts, pie charts, flow charts, and photographs) can best illustrate the particular feature or characteristic for the readers to comprehend; whether the illustration is simply used by the readers to gain a visual impression of an aspect being discussed, or can it be used to extract information; and is the illustration referred to only once to explain a point, or is it referred to several times in the report. Computer software can be used, with care, for illustration preparation. This chapter describes various ways to present information graphically, and explains what type of information is best suited for each.

  • Appendix D: Calculation of Shell Frequency Distribution

    This chapter includes the following topics: Partial Histograms Partial Histograms for General Cost Functions Frequencies of Shells

  • Beyond Parametrics

    This chapter contains sections titled: 13.1 Introduction, 13.2 CUP: Unions of Parametric Models, 13.3 Universal Codes Based on Histograms, 13.4 Nonparametric Redundancy, 13.5 Gaussian Process Regression, 13.6 Conclusion and Further Reading

  • Fast Discriminative Visual Codebooks using Randomized Clustering Forests

    Some of the most effective recent methods for content-based image classification work by extracting dense or sparse local image descriptors, quantizing them according to a coding rule such as k-means vector quantization, accumulating histograms of the resulting "visual word" codes over the image, and classifying these with a conventional classifier such as an SVM. Large numbers of descriptors and large codebooks are needed for good results and this becomes slow using k-means. We introduce Extremely Randomized Clustering Forests -- ensembles of randomly created clustering trees -- and show that these provide more accurate results, much faster training and testing and good resistance to background clutter in several state-of-the-art image classification tasks.



Standards related to Histograms

Back to Top

No standards are currently tagged "Histograms"


Jobs related to Histograms

Back to Top