Conferences related to Pattern Recognition

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2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)

The conference program will consist of plenary lectures, symposia, workshops andinvitedsessions of the latest significant findings and developments in all the major fields ofbiomedical engineering.Submitted papers will be peer reviewed. Accepted high quality paperswill be presented in oral and postersessions, will appear in the Conference Proceedings and willbe indexed in PubMed/MEDLINE & IEEE Xplore


2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.


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 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

FUZZ-IEEE is the top leading conferencein the area of theory and applications of fuzzy logic.


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Periodicals related to Pattern Recognition

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Advanced Packaging, IEEE Transactions on

The IEEE Transactions on Advanced Packaging has its focus on the modeling, design, and analysis of advanced electronic, photonic, sensors, and MEMS packaging.


Antennas and Wireless Propagation Letters, IEEE

IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


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 I: Regular Papers, 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.


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Most published Xplore authors for Pattern Recognition

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Xplore Articles related to Pattern Recognition

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The design and implementation of dip arrow plot pattern recognition system

[1988 Proceedings] 9th International Conference on Pattern Recognition, 1988

Dip logging is a method of well logging. From the raw data obtained by dip logging, a dip arrow plot can be obtained using the relation comparison method of pattern recognition. The data tend to be very much scattered and random. The design principle and method of an expert dip arrow plot pattern recognition system are described. It incorporates a ...


Statistical subpixel pattern recognition by histograms

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 1992

A new statistical pattern recognition method has been developed for detection, recognition or measurement of patterns which are (much) smaller than the measure of the elementary pixel windows in the image screen. In this measurement the gray-level histogram of the objects examined is compared with the simulated histograms of different (in type or size) possible objects, and the recognition (of ...


Seeking pattern recognition principles for intelligent detection of FSK signals

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 1992

Proposes the following cascade for intelligent detection of the presence of binary frequency-shift-keying (FSK) signals corrupted by additive white Gaussian noise: (1) discrete Fourier Transform (DFT) for periodogram estimation, computed at the two modulating frequencies; (2) a specific pattern recognition algorithm in the spectral space IR/sup 2/, consisting of one of the following variants: (a) perceptron; (b) fuzzy perceptron; (c) ...


Pattern Recognition Method Using Ensembles of Regularities Found by Optimal Partitioning

2010 20th International Conference on Pattern Recognition, 2010

New pattern recognition method is considered that is based on ensembles of ”syndromes”. The developed method that is referred to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted Syndromes (SWS) method. ”Syndromes” are subregions in space of prognostic features where content of objects from one of the classes differs significantly from the same class contents ...


Transducer learning in pattern recognition

Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems, 1992

'Interpretation' is a general and interesting pattern recognition framework in which a system is considered to input object representations, and output the corresponding interpretations in terms of 'semantic messages' specifying the actions to be carried out as system's responses. From the syntactic pattern recognition viewpoint, interpretation reduces to formal transduction. The authors propose an efficient and effective algorithm to automatically ...


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Educational Resources on Pattern Recognition

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IEEE-USA E-Books

  • The design and implementation of dip arrow plot pattern recognition system

    Dip logging is a method of well logging. From the raw data obtained by dip logging, a dip arrow plot can be obtained using the relation comparison method of pattern recognition. The data tend to be very much scattered and random. The design principle and method of an expert dip arrow plot pattern recognition system are described. It incorporates a mathematical model method and an artificial intelligence technique. An angle processing algorithm and an ordered sample grouping algorithm are proposed. This system has been implemented on an IBM-PC/XT and on compatible machines. Its classification rate is 98%, obtained by processing 21 data patterns from the Zhongyuan oil field in China.<<ETX>>

  • Statistical subpixel pattern recognition by histograms

    A new statistical pattern recognition method has been developed for detection, recognition or measurement of patterns which are (much) smaller than the measure of the elementary pixel windows in the image screen. In this measurement the gray-level histogram of the objects examined is compared with the simulated histograms of different (in type or size) possible objects, and the recognition (of shape or measure) is taken on the basis of the comparison. This method does not need ultra-precise movement of the scanning sensors or any additional hardwares. Moreover, the examined pattern should be randomly distributed on the screen, or a random movement of camera (or target or both) is needed. Effect of noises are analyzed, and filtering processes are suggested in the histogram domain. Several examples of different shapes are presented through simulations and experiments.<<ETX>>

  • Seeking pattern recognition principles for intelligent detection of FSK signals

    Proposes the following cascade for intelligent detection of the presence of binary frequency-shift-keying (FSK) signals corrupted by additive white Gaussian noise: (1) discrete Fourier Transform (DFT) for periodogram estimation, computed at the two modulating frequencies; (2) a specific pattern recognition algorithm in the spectral space IR/sup 2/, consisting of one of the following variants: (a) perceptron; (b) fuzzy perceptron; (c) Bayes. The computer simulation results show the significant improvement of the proposed pattern recognition methods by comparison to the classical technique of detection theory by matched filter. The proposed paper tries to build a bridge between the worlds of communications, signal processing and pattern recognition.<<ETX>>

  • Pattern Recognition Method Using Ensembles of Regularities Found by Optimal Partitioning

    New pattern recognition method is considered that is based on ensembles of ”syndromes”. The developed method that is referred to as Multi-model statistically weighted syndromes (MSWS) is further development of earlier Statistically Weighted Syndromes (SWS) method. ”Syndromes” are subregions in space of prognostic features where content of objects from one of the classes differs significantly from the same class contents in neighboring subregions. ”Syndromes” are discussed as simple basic classifiers that are combined with the help of weighted voting procedure. Method of optimal partitioning of input features space is used for ”syndromes” searching. At that ”syndromes” are selected depending on quality of data separation and complexity of used partitioning model (partitions family). Performance of MSWS is compared with performance of SWS and alternative techniques in several applied tasks. Influence of recognition ability on characteristics of ”syndromes” selection is studied.

  • Transducer learning in pattern recognition

    'Interpretation' is a general and interesting pattern recognition framework in which a system is considered to input object representations, and output the corresponding interpretations in terms of 'semantic messages' specifying the actions to be carried out as system's responses. From the syntactic pattern recognition viewpoint, interpretation reduces to formal transduction. The authors propose an efficient and effective algorithm to automatically infer a finite state transducer from a training set of input-output examples of the interpretation problem considered. The proposed algorithm has been shown to identify an important class of transductions known as 'subsequential transductions.' Experimental results are presented showing the performance and capabilities of the proposed method.<<ETX>>

  • Pattern recognition algorithm based on cyclic codes

    Presents some new results on using the theory of error control codes in pattern recognition. For the shapes described by means of primitive strings , a recognition algorithm is proposed based on string matching. By using the polynomial cyclic code classification, it is obtained the invariance at the position of the starting point when creating the string representation.<<ETX>>

  • A pattern recognition approach to detect oil/gas reservoirs in sand/shale sediments

    A hybrid structural and statistical pattern recognition approach to detect oil/gas reservoirs in sand/shale sediments is presented in the paper. On the basis of the sand fiducial profile derived from log data and seismic data, a tree-based region-detecting method is used to detect sand layers, and a Marr's -operator-based clustering algorithm is used to find oil/gas reservoirs in the detected sand layers. The ability of the approach is demonstrated by a real- data example.<<ETX>>

  • Traffic Sign Detection and Pattern Recognition Using Support Vector Machine

    A vision based vehicle guidance system must be able to detect and recognize traffic signs. Traffic sign recognition systems collect information about road signs and helps the driver to make timely decisions, making driving safer and easier. This paper deals with the detection and recognition of traffic signs from image sequences using the colour information. Colour based segmentation techniques are employed for traffic sign detection. In order to improve the performance of segmentation, we used the product of enhanced hue and saturation components. To obtain better shape classification performance, we used linear support vector machine with the distance to border features of the segmented blobs. Recognition of traffic signs are implemented using multi- classifier non-linear support vector machine with edge related pixels of interest as the feature.

  • IEE Colloquium on 'Pattern Recognition for Binary Images' (Digest No.53)

    None

  • Algorithms of the multiperspective pattern recognition

    Deals with a situation in which an object undergoes several classification tasks. Three optimal (Bayes) recognition algorithms are derived which state solutions of different optimization problems. The case of recognition with learning is considered and results of experimental investigations are presented The primary results are illustrated by simple example.<<ETX>>