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Chapter 1: Introduction 3Another application that our research efforts target is that of under vehicle inspection.Vehicle inspection has been traditionally accomplished through security personnelwalking around a vehicle with a mirror at the end of a stick. The inspection personnelare able to view underneath a vehicle to identify weapons, bombs and other securitythreats. The mirror-on-the-stick system allows only partial coverage under a vehicle andis restricted by ambient lighting. The inspecting personnel are also at risk. As part of theSecurity Automation and Future Electromotive Robotics (SAFER) program we aim atdeveloping a robotic platform that deploys “sixth sense” sensors for threat assessment.We propose the idea of incorporating a 3D range sensor on the robotic platform. Theidea is to be able to extract the 3D geometry of the undercarriage of automobiles. Withprior manufacturer’s information on the components that make the undercarriage of thevehicle, we believe that it will be possible to identify foreign objects in the scene. Forexample in Figure 1.2 we show the robotic platform and the 3D geometry of the scenecontaining the muffler, shaft and the catalytic converter. It will not be possible to extractcomplete geometry of the undercarriage without dismantling the automobile. We henceneed a representation scheme that maps the shape sensed from the scene to the CADdescription and that is robust with occluded data.Though vehicle inspection and reverse engineering appear as different applications,they share the same processing pipeline as a computer vision task of designing asystem that can capture the geometric structure of an object and store the subsequentshape and topology information. We discuss the use of laser-based range scanners forthe extraction of 3D geometry and a curvature-based shape analysis algorithm basedon our CVM to interpret surface topology.Robotic Platform Under Vehicle Scene 3D GeometryFigure 1.2: Under vehicle inspection and surveillance.
Chapter 1: Introduction 41.2 Proposed ApproachShape analysis is an age-old research topic and has been pursued since the dawn ofimage processing and computer vision. Literature on shape extraction from intensityimages is vast and gives good insight into why vision research with intensity imageshas not been very successful. Most of the methods we have studied show promise withmore and almost complete information with 3D data. Though 3D data acquisition andprocessing is relatively new, there are a few important contributions in our context ofshape similarity and shape description all motivated by the challenge of objectrecognition. We survey the literature on shape analysis applied to intensity images andalso summarize recent and ongoing work in 3D computer vision.Computer vision systems seek to develop computer models of the real world throughprocessing of image data from sensors. In Figure 1.3(a) we present the flow diagramof our proposed approach. We begin with the data acquisition (Figure 1.3 (b)) usinglaser-based range scanners and the process of creating CAD models using thesescanners. We acquire range images using the laser-illuminated active range sensorfrom the Integrated Vision Products Inc. (IVP). A range image is a 2D matrix withvalues proportional to the distance between the sensor and the object. We acquirerange information from multiple views of the object to make sure that we havesufficient data to represent the object completely. We then transform the range datafrom the camera coordinate frame to the real world and integrate the multi-view pointclouds into a single global reference frame. We reconstruct triangle meshes from thepoint clouds and use it as our input for the shape analysis.We base our shape analysis algorithm on the part-based perception model[Stankiewicz, 2002]. With automotive components, our task is simplified because thecomponents are man-made and manufacturing limitations restrict us to smooth (mostlyplanar and cylindrical) patches. We hence propose that surface shape description ofeach of the parts and the connectivity of parts can uniquely describe the object. Indescribing surfaces and surface complexity we chose curvature to extract “shapeinformation”. We chose curvature because it is an information preserving feature,invariant to rotation and possesses an intuitively pleasing correspondence to theperceptive property of “simplicity”. We decompose the object of interest into a set ofpatches and assign a Curvature Variation Measure (CVM) to each of these patches andrepresent the object as a patch adjacency graph. Our graph representation whenextended to scenes with occlusions can still yield satisfactory results.Consider the example in Figure 1.3 again. We first decompose the triangle meshmodel into smooth patches. We show the disc brake model and decompose it into fourparts. We have shaded each of these parts with a different color. We base our surfacepatch decomposition on the definition of curvedness in [Dorai, 1996]. Curvednessidentifies sharp edges and creases.
Page 1 and 2: To the Graduate Council:I am submit
Page 3 and 4: Acknowledgementsii“Experience is
Page 5 and 6: Contents1 INTRODUCTION.............
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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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