3D IMAGE DENOISING BY LOCAL SURFACE APPROXIMATION
By: Mohana, S
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Contributor(s): Periasamy, P. S.
Publisher: Haryana International Science Press 2016Edition: Vol. 6(2) , July-December.Description: 93-96p.Subject(s): Computer EngineeringOnline resources: Click here
In:
International journal of embedded software and open source systemsSummary: In practical applications 3D representation of objects is the emerging technique. Further quantitative analysis on these images requires denoising and it is complicated process compared to 2D images, since the edges are surfaces. this paper proposes a robust denoising algoritham based on the approximation of standard mathematical shapes like triangle, square, polygon etc. at the edge surfaces. In case of 3D images these shapes are considered as pyramid, cube, cones etc. at the surfaces of the edges. three important points in the surfaces are corner points, uertex points and surfaces points. these points are identified by the strength of the edge uoxels jump regression analysis is used to find out the group of edge uoxels. The proposed algorithm has the important properties of less computational staps inuolued in approximating the shapes and the edge features are preserved around the places where three or more edge surfaces cross.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2021-2022268 |
In practical applications 3D representation of objects is the emerging technique. Further quantitative analysis on these images requires denoising and it is complicated process compared to 2D images, since the edges are surfaces. this paper proposes a robust denoising algoritham based on the approximation of standard mathematical shapes like triangle, square, polygon etc. at the edge surfaces. In case of 3D images these shapes are considered as pyramid, cube, cones etc. at the surfaces of the edges. three important points in the surfaces are corner points, uertex points and surfaces points. these points are identified by the strength of the edge uoxels jump regression analysis is used to find out the group of edge uoxels. The proposed algorithm has the important properties of less computational staps inuolued in approximating the shapes and the edge features are preserved around the places where three or more edge surfaces cross.
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