Logo Recognition：Theory and Practice

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.

IntroductionMotivationShape recognitionProposed methodObjectivesAssumptions and input dataBook organization
Preliminary knowledgeStatisticsProbabilityRandom variableExpected value Variance and deviationCovariance and correlationMoment-generating functionFourier transformStructural and syntactic pattern recognitionIntroduction Grammar-based passing methodGraph-based matching methodsNeural networkArchitectureLearning processSummary
Review of shape recognition techniques2D shape recognitionShape representationShape recognition approachesLogo recognitionStatistical approachSyntactic/structural approachNeural networkHybrid approachPolygonal approximationIndexingMatchingDistance measure Hausdorff distanceSummary
System overviewPreprocessingPolygonal approximationIndexingMatching
Polygonal approximationFeature point detection overviewDynamic two-strip algorithmThe proposed methodResultsComparison with other methodsSummary
Logo indexingNormalizationIndexingReference angle indexing (filter 1) Line orientation indexing (filters 2 and 3) Experimental resultsSummary
Logo matchingHausdorff distanceModified LHD (MLHD)Experimental resultsMatching resultsDegradation analysisResults analysis with respect to the LHD and the MHDDiscussion and comparison with other methodsSummary
ApplicationsMobile visual search with GetFuguUsing logo recognition for anti-phishing and Internet brand monitoringThe LogoTrace libraryReal-time vehicle logo recognitionSummary
ConclusionBook summaryContributionFuture workBook conclusionReferences
Appendix Test imagesAppendix Results of feature point detection
Index