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Chapter 6: Conclusions 816 CONCLUSIONSIn this thesis, we have described a pipeline for real-time imaging and 3D modeling ofautomotive parts and a representation scheme that would simplify the task of threatdetection for vehicle inspection. This research relies heavily on a heuristic, which wecall CVM, which is based on curvature of surfaces and their contribution to describingsurface complexity. In the previous chapters, we have reviewed research in thecomputer vision literature similar to our algorithm as a context for our contributions andpresented the supporting theory along with experimental results. We have also discussedcertain implementation issues of the algorithm. We now conclude with a brief summaryof the contributions and a short discussion on future directions.6.1 ContributionsOur research efforts were focused on the construction of a scanning mechanism thatwould be able to create 3D models of automotive components. We have used thesheet-of-light active range imaging technique for the data acquisition task andextended its capability to extract geometry of an automotive scene. We have outlinedour design efforts towards data collection and followed it up with results on 3D modelcreation and analysis of objects. We have also presented experimental results of aninformation theory-based surface shape description algorithm on the laser scanned 3Dmodels. The 3D data acquisition process to generate a dense point cloud of a particularview of an object, multiple view fusion and surface graph representation (comparableto CAD) of the models is our implementation of a pipeline that aids reverseengineering and inspection.Based on our survey and implementation of the state-of-the art algorithms for curvatureestimation on triangle meshes, we have presented a rigorous analysis on key methods.Our comparison sheds light on the errors in magnitude of curvature and also the effectof factors such as resolution and effectiveness in describing visual complexity.We hence would like to summarize the quintessence of the thesis as the definition ofCVM as the informational approach to shape description. We have used curvatureestimates at each vertex to generate probability distribution curves. With these curves,we have formulated an information theoretic based on entropy to define surface shape
Chapter 6: Conclusions 82complexity. In the spirit of Claude Shannon's definition of information, this measurereflects the amount of shape information that an object surface possesses. Objects andscenes with nearly constant curvature contain relatively low values of shapeinformation, while other objects and scenes with significant variation in curvatureexhibit fairly large values. Though our idea of using curvature as a feature fordescription is not new, our attempt to quantify the perceptual complexity of a surfaceusing information theory is. Since we are describing surfaces using our approach,occluded scenes such as the one that was obtained real-time can also be represented witha good degree of confidence towards object detection.6.2 Directions for the FutureWe feel that the process of creating 3D models has scope for improvement. Using oursystem design, it takes nearly six hours to scan, fuse and integrate multiple views intoits complete 3D triangle mesh model. We did not consider optimizing views forminimizing scan time. We can approach view planning as a sensor placement problemfor better efficiency. The solution to the problem will also enhance the under vehiclescene modeling. Our system design also has a lot of scope for improvement towardsvehicle inspection. Instead of using a conveyer belt that houses the range sensor, itwould be better to have a calibrated setup to control the relative motion. We wouldalso like to have the scanning mechanism redesigned to be robot mountable toautomate the scanning process.In the typical context of aligning range scans of an object in order to create a completemodel of that object we would like to point out the possibility of application of ouralgorithm to surface registration. Surface registration is a feature dependent process.More features improve registration. By features, here we mean unique geometricinformation. We believe that representing a range scan as a cluster of shape measuresaround a neighborhood would help us recover the rigid transformation from anotherview of the same object that has some common information (overlap). A multi-scalehierarchical informational approach should be a good start for this process.Object recognition is an extremely difficult task with most current solutions limited to avery constrained and restricted problem domain. Although we do not claimcontributions in terms of recognition as yet, we are encouraged by the results in thisthesis that it might serve as a first step in the recognition pipeline. Shapiro andStockman [Shapiro, 2001] suggest commonly used paradigms for object recognitionwhere the method chosen depends heavily on the application. They discuss twoparadigms that use part (region) relationships to move away from a geometric definitionof an object to a more symbolic one. Our algorithm benefits the creation of such asymbolic graph representation from a mesh representation. We would like to performrigorous experiments on partial graph matching for threat detection and model-based
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To the Graduate Council:I am submit
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Acknowledgementsii“Experience is
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Contents1 INTRODUCTION.............
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List of TablesviTable 2.1:Qualitati
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viiiFigure 5.2: Curvature analysis
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Chapter 1: Introduction 21.1 Motiva
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Chapter 1: Introduction 41.2 Propos
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Chapter 1: Introduction 6We then pe
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Chapter 2: Literature Review 8Shape
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Chapter 2: Literature Review 102.2.
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Chapter 3: Data Collection and Mode
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Chapter 3: Data Collection and Mode
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Page 93 and 94: Bibliography 84BIBLIOGRAPHY
Page 95 and 96: Bibliography 86[Biermann, 2001][Bel
Page 97 and 98: Bibliography 88[Cyr and Kimia, 2001
Page 99 and 100: Bibliography 90[Gourley, 1998][Gosh
Page 101 and 102: Bibliography 92[Joshi and Chang, 19
Page 103 and 104: Bibliography 94[Meyer, 2002][Morse,
Page 105 and 106: Bibliography 96[Safar et al., 2000]
Page 107 and 108: Bibliography 98parts,” IEEE Trans
Page 109: Vita 100VITASreenivas Rangan Sukuma
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