A new technique for approximating range images with adaptive triangular meshes ensuring a user-defined approximation error is presented. This technique is based on an efficient coarse-to-fine refinement algorithm that avoids iterative optimization stages. The algorithm first maps the pixels of the given range image to 3D points defined in a curvature space. Those points are then tetrahedralized with a 3D Delaunay algorithm. Finally, an iterative process starts digging up the convex hull of the obtained tetrahedralization, progressively removing the triangles that do not fulfill the specified approximation error. This error is assessed in the original 3D space. The introduction of the aforementioned curvature space makes it possible for both convex and nonconvex object surfaces to be approximated with adaptive triangular meshes, improving thus the behavior of previous coarse-to-fine sculpturing techniques. The proposed technique is evaluated on real range images and compared to two simplification techniques that also ensure a user-defined approximation error: a fine-to-coarse approximation algorithm based on iterative optimization (Jade) and an optimization-free, fine-to-coarse algorithm (Simplification Envelopes).
This paper presents an efficient technique for linking edge points in order to generate a closed-contour representation. It is based on the consecutive use of global and local schemes. In both cases it is assumed that the original intensity image, as well as its corresponding edge map, are given as inputs to the algorithm. The global scheme computes an initial representation by connecting edge points minimizing a global measure based on spatial information (3D space). It relies on the use of graph theory and exploits the edge points' distribution through the given edge map, as well as their corresponding intensity values. At the same time spurious edge points are removed by a morphological filter. The local scheme finally generates closed contours, linking open boundaries, by using a local cost function that takes into account both spatial and topological information. Experimental results with different images, together with comparisons with a previous technique, are presented.
Conference Committee Involvement (6)
Automatic Target Recognition XXXIV
22 April 2024 | National Harbor, Maryland, United States
Automatic Target Recognition XXXIII
1 May 2023 | Orlando, Florida, United States
Automatic Target Recognition XXXII
4 April 2022 | Orlando, Florida, United States
Automatic Target Recognition XXXI
12 April 2021 | Online Only, Florida, United States
Automatic Target Recognition XXX
27 April 2020 | Online Only, California, United States
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.