1st Edition

Logo Recognition Theory and Practice

By Jingying Chen, Lizhe Wang, Dan Chen Copyright 2012
    194 Pages 79 B/W Illustrations
    by CRC Press

    194 Pages 79 B/W Illustrations
    by CRC Press

    Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos.

    Logo Recognition: Theory and Practice is the first book to focus on logo recognition, especially under noisy conditions. Beginning with an introduction to fundamental concepts and methods in pattern and shape recognition, it surveys advances in logo recognition. The authors also propose a new logo recognition system that can be used under adverse conditions such as broken lines, added noise, and occlusion.

    The proposed system introduces a novel polygonal approximation, a robust indexing scheme, and a new line segment Hausdorff distance (LHD) matching method that can handle more distortion and transformation types than previous techniques. In the first stage, raw logos are transformed into normalized line segment maps. In the second stage, effective line pattern features are used to index the database to generate a moderate number of likely models. In the third stage, an improved LHD measure screens and generates the best matches. A comprehensive overview of logo recognition, the book also presents successful applications of the technology and suggests directions for future research.

    Shape recognition
    Proposed method
    Assumptions and input data
    Book organization

    Preliminary knowledge
    Random variable
    Expected value
    Variance and deviation
    Covariance and correlation
    Moment-generating function
    Fourier transform
    Structural and syntactic pattern recognition
    Grammar-based passing method
    Graph-based matching methods
    Neural network
    Learning process

    Review of shape recognition techniques
    2D shape recognition
    Shape representation
    Shape recognition approaches
    Logo recognition
    Statistical approach
    Syntactic/structural approach
    Neural network
    Hybrid approach
    Polygonal approximation
    Distance measure
    Hausdorff distance

    System overview
    Polygonal approximation

    Polygonal approximation
    Feature point detection overview
    Dynamic two-strip algorithm
    The proposed method
    Comparison with other methods

    Logo indexing
    Reference angle indexing (filter 1)
    Line orientation indexing (filters 2 and 3)
    Experimental results

    Logo matching
    Hausdorff distance
    Modified LHD (MLHD)
    Experimental results
    Matching results
    Degradation analysis
    Results analysis with respect to the LHD and the MHD
    Discussion and comparison with other methods

    Mobile visual search with GetFugu
    Using logo recognition for anti-phishing and Internet brand monitoring
    The LogoTrace library
    Real-time vehicle logo recognition

    Book summary
    Future work
    Book conclusion

    Appendix Test images
    Appendix Results of feature point detection




    Jingying Chen, Lizhe Wang, Dan Chen

    "I was inspired by this book project at the very beginning; now the book appears to be even a better idea when I really have it in hand. The resulting appraisal is thoughtful, creative, and comprehensive."
    —From the Foreword by Professor Xiaoli Li, College of Information Science and Technology, Beijing Normal University

    "… Overall the book is well written and easy to follow … understandable and well formulated. I recommend it to readers willing to learn about logo recognition systems and potential commercial applications of shape recognition tools."
    —Journal of Intelligent and Robotic Systems