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A LiDAR point cloud is 3D data which contains millions of data points represented in the form I (x, y, z) that stores the spatial coordinates and possibly RGB color information. This method of data collection is especially useful in collecting large scale scene information. The goal of this project is to develop a self-adaptive and automated methodology to extract features which effectively represent object regions, specifically man-made objects and vegetation regions. The point cloud will be initially segmented using a strip histogram grid approach. Once significant features are extracted, region refinement by surface growing will be performed. Finally after the regions of interest have been segmented a cascade classifier approach will be used for object classification.
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