<p><strong>Abstract.</strong> In this paper, we propose a new method for specifying individual tree species based on depth and curvature image creation from point cloud captured by terrestrial laser scanner and Convolutional Neural Network (CNN). Given a point cloud of an individual tree, the proposed method first extracts the subset of points corresponding to a trunk at breast-height. Then branches and leaves are removed from the extracted points by RANSAC -based circle fitting, and the depth image is created by globally fitting a cubic polynomial surface to the remaining trunk points. Furthermore, principal curvatures are estimated at each scanned point by locally fitting a quadratic surface to its neighbouring points. Depth images clearly capture the bark texture involved by its split and tear-off, but its computation is unstable and may fail to acquire bark shape in the resulting images. In contrast, curvature estimation enables stable computation of surface concavity and convexity, and thus it can well represent local geometry of bark texture in the curvature images. In comparison to the depth image, the curvature image enables accurate classification for slanted trees with many branches and leaves. We also evaluated the effectiveness of a multi-modal approach for species classification in which depth and curvature images are analysed together using CNN and support vector machine. We verified the superior performance of our proposed method for point cloud of Japanese cedar and cypress trees.</p>
Recently, meshes of engineering objects have been easily acquired by 3D laser or high-energy industrial X-ray CT scanning systems and they are widely used in product developments. For the effective use of scanned meshes in inspection, re-design, and simulation of the objects, it is important to reconstruct CAD models from the meshes. Engineering objects often exhibit Euclidean symmetries for their functionalities. Therefore, it is essential to detect such symmetries when reconstructing CAD models with compact data representations which are similar to the ones already defined in CAD systems. However, existing methods for reconstructing CAD models have not focused on detecting such symmetries. In this paper, we propose a new method that detects partial or global Euclidean symmetries, including translation, rotation, and reflection, from scanned meshes of engineering objects based on the combination of the ICP and the region growing algorithms. Our method can robustly and efficiently extract pairs of symmetric regions and their transformations under which the pair can be closely matched to each other. We demonstrate the effectiveness of the proposed method from experiments on various scanned meshes.
Product surface textures are designed to improve their aesthetic, tactile, and mechanical quality. Surface texture manufactured with microrough patterns over a wide area differs from common geometric product machining. We have proposed generating a surface texture with regular patterns by milling. Here we propose generating a random-pattern surface texture using image processing. Digital surface-texture data consists of “real” three-Dimensional (3D) machining information. Wide-area digital surface-texture data such as scattered point data, Initial Graphics Exchange Specifications (IGES), and Standard Triangulated Language (STL) require humongous memory. The complexity and area of surface texture processed to generate tool paths is limited by computational considerations and generating the tool path for a widearea surface texture is time-consuming, so we propose generating random wide-area-pattern surface texture without the need for wide-area digital texture data. Instead, this uses only wide-area image data and narrowarea digital data. A wide-area tool path is generated by image quilting, which creates a patchwork in which patches represent both image and digital data for narrow-area surface texture, reducing surface distortion for patch boundaries. This paper introduces the generation of random pattern texture and machined samples assessing patch-boundary distortion.
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