![]() ![]() Thus the code recursively improves the triangulation after adding each point to either a recursion count is reached or it is an ideal Delaunay triangulation. The goal of the code wasn't to compute the ideal triangulation, but instead produce a “good enough” solution for most practical 3D or FEM problems and do so quickly. Generating a mesh from an arbitrary set of points is where Delaunay's Triangulation proves valuable. Usually selecting a set of points is easy, For simple objects like a cylinder or sphere, the point generation leads directly to triangle generation. Whether it is to visualize a differential equation solved by finite element modeling (FEM) or to view a shape of an object in 3D, the first step is to form the triangle mesh. To form a surface that can be shaded by a graphics card, the goal is to create a list of vertexes, and a list of triplet indexes of the vertexes that form counter clockwise triangles. The code is designed to be reused such that a vertex can be used to both generate the mesh, but also be apart of other data structures and track other aspects at a given point. It is the authors opinion that computational geometry and computer vision are entering a new age where real time processing is realistic for a growing set of problems. With modern computers and modern languages, not only can simple meshes be generated in real time, the code can be written in ways easy to understand. There are faster versions, but they are large implementations and they are hard to read and modify. International Journal of Computational Vision and Robotics, 2009 Vol.1 No.1, pp.Ten years ago, computing meshes for surfaces in real time for surfaces wasn't realistic, and having a customizable source code module wasn't available either. Keywords: laser scanning range images 3D modelling structural features perspective transformation control points intensity images image fusion affine transformation computational vision optical triangulation data acquisition feature matching linear interpolation Hough transform feature extraction. This provides a high-level description of the object. Fusion of both the extracted edge maps is accomplished by affine transformation after identifying a set of control points. Hough transform technique has been implemented for the extraction of structural features from the intensity image of the object obtained under illumination light. Structural features are extracted from the draft 3-D model of the object and subsequently mapped to 2-D plane by perspective transformation. Triangle-based linear interpolation is used to obtain a 3-D mesh representing the object surface. ![]() For that purpose, the method of optical triangulation is employed for data acquisition and feature matching technique for the registration of the images. ![]() A draft 3-D view of the object is generated by registering range images obtained from scanning the object by a laser beam. ' Department of Electrical Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, IndiaĪbstract: This paper presents feature level fusion of images of an object obtained from two different imaging techniques. ' Department of Electrical Engineering, Indian Institute of Technology, Kharagpur 721302, West Bengal, India. Dutta, Alok BaruaĪddresses: Department of Electronics and Communication Engineering, National Institute of Technology, Rourkela 769008, Orissa, India. Title: Feature level fusion of range and intensity images of an objectĪuthors: Umesh C. International Journal of Computational Vision and Robotics.Inderscience Publishers - linking academia, business and industry through research Article: Feature level fusion of range and intensity images of an object Journal: International Journal of Computational Vision and Robotics (IJCVR) 2009 Vol.1 No.1 pp.2 - 33 Abstract: This paper presents feature level fusion of images of an object obtained from two different imaging techniques.
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