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Important Dates
July 27-30, 2015
The WORLDCOMP'15
20 joint conferences


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IPCV'15 - The 19th International Conference on Image Processing, Computer Vision, & Pattern Recognition

Last modified 2014-12-16 21:41

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    You are invited to submit a paper for consideration. All accepted papers will be published in printed conference books/proceedings (ISBN) and will also be made available online. The proceedings will be indexed in science citation databases that track citation frequency/data. In addition, like prior years, extended versions of selected papers (about 35%) of the conference will appear in journals and edited research books (publishers include: Springer, Elsevier, BMC, and others; a list that includes a small subset of such books and journal special issues appear Here) ; some of these books and journal special issues have already received the top 25% downloads in their respective fields.

    The conference is composed of a number of tracks, tutorials, sessions, workshops, poster and panel discussions; all will be held simultaneously, same location and dates: July 27-30, 2015.

    Topics of interest include, but are not limited to, the following:

            Prologue: The broad area of Imaging Science is a field that is mainly concerned with the generation, collection, analysis, modification, and visualization of images. The field is multidisciplinary in that it includes topics that are traditionally covered in computer science, physics, mathematics, electrical engineering, AI, psychology and information theory where computer science acts as the topical bridge between all such diverse areas (for a formal definition of Imaging Science, refer to relevant wiki pages.) From the computer science perspective, the core of Imaging Science includes the following three intertwined computer science fields, namely: Image Processing, Computer Vision, and Pattern Recognition. This conference will cover the research trends in these three important areas. The list of topics of interest that appears below is not meant to be exhaustive.

      • IMAGE PROCESSING:
            (Addresses ow-level processing as well as imaging fundamentals.)

            - Software Tools for Imaging
            - Image Generation, Acquisition, and Processing
            - Image-based Modeling and Algorithms
            - Mathematical Morphology
            - Image Geometry and Multi-view Geometry
            - 3D Imaging
            - Novel Noise Reduction Algorithms
            - Image Restoration
            - Enhancement Techniques
            - Segmentation Techniques
            - Motion and Tracking Algorithms and Applications
            - Watermarking Methods and Protection + Wavelet Methods
            - Image Data Structures and Databases
            - Image Compression, Coding, and Encryption
            - Video Analysis>
            - Multi-resolution Imaging Techniques
            - Performance Analysis and Evaluation
            - Multimedia Systems and Applications
            - Novel Image Processing Applications
      • COMPUTER VISION:
            (Addresses mid- to high-level processing as well as vision fundamentals.)
            >
            - Camera Networks and Vision
            - Sensors and Early Vision
            - Machine Learning Technologies for Vision
            - Image Feature Extraction
            - Cognitive and Biologically Inspired Vision
            - Object Recognition
            - Soft Computing Methods in Image Processing and Vision
            - Stereo Vision
            - Active and Robot Vision
            - Face and Gesture Recognition
            - Fuzzy and Neural Techniques in Vision
            - Medical Image Processing and Analysis
            - Novel Document Image Understanding Techniques
            - Special-purpose Machine Architectures for Vision
            - Biometric Authentication
            - Novel Vision Application and Case Studies
      • PATTERN RECOGNITION:
            (Addresses pattern recognition algorithms and methodologies that are of value to the image processing and computer vision research communities.)

            - Supervised and Un-supervised Classification Algorithms
            - Clustering Techniques
            - Dimensionality Reduction Methods in Pattern Recognition
            - Symbolic Learning
            - Ensemble Learning Algorithms
            - Parsing Algorithms
            - Bayesian Methods in Pattern Recognition and Matching
            - Statistical Pattern Recognition
            - Invariance in Pattern Recognition
            - Knowledge-based Recognition
            - Structural and Syntactic Pattern Recognition
            - Applications Including: Security, Medicine, Robotic, GIS, Remote Sensing, Industrial Inspection, Nondestructive Evaluation (or NDE), ...
            - Case studies and Emerging technologies

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