Chen / Wang Logo Recognition

Theory and Practice
1. Auflage 2011
ISBN: 978-1-4398-4785-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 192 Seiten

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.

Zielgruppe

Researchers, engineers and IT professionals who work in the fields of pattern recognition and computer vision; graduate students studying computer science.

Autoren/Hrsg.

Chen, Jingying

Wang, Lizhe

Chen, Dan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Bildsignalverarbeitung

Inhaltsverzeichnis

Introduction
Motivation
Shape recognition
Proposed method
Objectives
Assumptions and input data
Book organization
Preliminary knowledge
Statistics
Probability
Random variable
Expected value
Variance and deviation
Covariance and correlation
Moment-generating function
Fourier transform
Structural and syntactic pattern recognition
Introduction
Grammar-based passing method
Graph-based matching methods
Neural network
Architecture
Learning process
Summary
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
Indexing
Matching
Distance measure
Hausdorff distance
Summary
System overview
Preprocessing
Polygonal approximation
Indexing
Matching
Polygonal approximation
Feature point detection overview
Dynamic two-strip algorithm
The proposed method
Results
Comparison with other methods
Summary
Logo indexing
Normalization
Indexing
Reference angle indexing (filter 1)
Line orientation indexing (filters 2 and 3)
Experimental results
Summary
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
Summary
Applications
Mobile visual search with GetFugu
Using logo recognition for anti-phishing and Internet brand monitoring
The LogoTrace library
Real-time vehicle logo recognition
Summary
Conclusion
Book summary
Contribution
Future work
Book conclusion
References
Appendix Test images
Appendix Results of feature point detection
Index