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RESEARCH PRODUCT

Preprocessing of region of interest localization based on local surface curvature analysis for three-dimensional reconstruction with multiresolution

Wanjing LiFrank BoochsRainer SchützeMartin BöhlerFranck MarzaniYvon Voisin

subject

[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing[INFO.INFO-TS] Computer Science [cs]/Signal and Image ProcessingComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION3d scanningStereoscopyImage processing0102 computer and information sciences02 engineering and technologyIterative reconstruction[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingCurvature01 natural sciencesVideo projectorsurface curvaturelaw.invention[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingRegion of interestlaw0202 electrical engineering electronic engineering information engineeringPreprocessorComputer visionImage resolution[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingComputingMethodologies_COMPUTERGRAPHICSbusiness.industryintelligent 3D scannerGeneral EngineeringAtomic and Molecular Physics and OpticsROI localisation010201 computation theory & mathematics020201 artificial intelligence & image processingArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingadaptive pattern

description

We present an approach to integrate a preprocessing step of the region of interest ROI localization into 3-D scanners laser or ste- reoscopic. The definite objective is to make the 3-D scanner intelligent enough to localize rapidly in the scene, during the preprocessing phase, the regions with high surface curvature, so that precise scanning will be done only in these regions instead of in the whole scene. In this way, the scanning time can be largely reduced, and the results contain only per- tinent data. To test its feasibility and efficiency, we simulated the prepro- cessing process under an active stereoscopic system composed of two cameras and a video projector. The ROI localization is done in an itera- tive way. First, the video projector projects a regular point pattern in the scene, and then the pattern is modified iteratively according to the local surface curvature of each reconstructed 3-D point. Finally, the last pat- tern is used to determine the ROI. Our experiments showed that with this approach, the system is capable to localize all types of objects, including small objects with small depth. © 2009 Society of Photo-Optical Instrumentation

https://hal.archives-ouvertes.fr/hal-00637703/file/Final.pdf