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Chapter 2: Literature Review 23contain a qualitative comparison based on algorithm efficiency and descriptivecapability of the key methods presented in Section 2.3.2.4 SummaryWe would like to summarize the literature review in this section. We have presented3D shape searching as applied in diverse fields such as computer vision, mechanicalengineering, bio-informatics and bio-medical imaging. In Tables 2.1 and 2.2 wecompare the description and search efficiency of a few key methods before concludingour summary.Shape signatures are abstractions of 3D shapes and have limited discriminationcapabilities. They are application specific and hence the complexity involved inmatching and computation cannot be compared on the same domain for effectiveness.Therefore a good strategy to shape analysis would be the choice of a signature that iscomputationally efficient producing lesser false positives followed by another one thatneeds computational effort to remove those false positives. With the popularity of 3Dscanning and CAD models we emphasize the necessity of a quick and informationpreserving shape representation than a time consuming exact isomorphicrepresentation.Shape analysis has been pursued by researchers for the task of multi-modal data fusion(registration), object recognition, object visualization and compression. Most of themethods developed are bounded by an application specific heuristic constraint thatbridges the user’s notion and the computer’s notion of shape similarity. We would liketo conclude the literature survey as our knowledge base for further research anddevelopment.We discuss range data acquisition and solid modeling of mechanical parts andautomotive scenes in the next chapter with illustrative examples. We emphasize that itis important to understand and interpret the data before analysis and so introduce rangeacquisition systems and the process of 3D model creation using a range sensor.
Chapter 2: Literature Review 24Table 2.1: Qualitative comparison of 3D shape search methods with focus onalgorithm efficiency.SearchingTechniqueComputationalCostComparisonCostTest DataKey MethodsFeature (Global)IntermediateDescriptionManufacturing andProduct basedDescriptionHistogram-basedTopological GraphMethodsO ( N ) whereN is the numberof voxels underconsideration.O (V logV)in the worst casewhere V is thenumber ofvertices.O ( P ) where Pis the number ofprimitives.O ( SB ) whereS is the numberof sample pointsand B is thenumber of bins.O ( N ) whereN is the numberof voxelsconsidered.O ( F ) where Fis the number offeaturesextracted.O(R2 ) whereR is theresolution of theintermediaterepresentation.O(F2 ) whereF is the numberof featuresextracted.O ( B ) where Bis the number ofbins.Worst caseO(N3 ) whereN is the numberof nodes in thegraph.Methods spansyntheticmesh datasetsto complexreal datasets.Range andTriangleMesh realworld datasets of scenesand objects.CAD modelsofmechanicalcomponents.Simple andlowresolutionsyntheticmodels.Lowresolutionsynthetic datasets.[Elad et al., 2001][Zhang, andHebert,1999][Johnson andHebert,1999][Dorai,1996][Mukhai et al.,2002][Ankerst et al., 1999][Osada et al.,2002][Hilaga et al.,2001][Leibowitz et al.,1999][McWherter et al.,2001]
Page 1 and 2: To the Graduate Council:I am submit
Page 3 and 4: Acknowledgementsii“Experience is
Page 5 and 6: Contents1 INTRODUCTION.............
Page 7 and 8: List of TablesviTable 2.1:Qualitati
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Chapter 6: Conclusions 82complexity
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Bibliography 84BIBLIOGRAPHY
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Bibliography 86[Biermann, 2001][Bel
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Bibliography 88[Cyr and Kimia, 2001
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Bibliography 90[Gourley, 1998][Gosh
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Bibliography 92[Joshi and Chang, 19
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Bibliography 94[Meyer, 2002][Morse,
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Bibliography 96[Safar et al., 2000]
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Bibliography 98parts,” IEEE Trans
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Vita 100VITASreenivas Rangan Sukuma
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