scholarly journals Semialgebraic Proofs and Efficient Algorithm Design

2019 ◽  
Vol 14 (1-2) ◽  
pp. 1-221 ◽  
Author(s):  
Noah Fleming ◽  
Pravesh Kothari ◽  
Toniann Pitassi
Keyword(s):  
Efficient Algorithm ◽  
Algorithm Design

Efficient Algorithm Design for GPR Imaging of Landmines

2015 ◽  
Vol 53 (7) ◽  
pp. 4010-4021 ◽  
Author(s):  
Kyle R. Krueger ◽  
James H. McClellan ◽  
Waymond R. Scott
Keyword(s):  
Efficient Algorithm ◽  
Algorithm Design

Semialgebraic Proofs and Efficient Algorithm Design

2019 ◽  
Author(s):  
Noah Fleming ◽  
Pravesh Kothari ◽  
Toniann Pitassi
Keyword(s):  
Efficient Algorithm ◽  
Algorithm Design

scholarly journals A Simple and Efficient Algorithm Design for Improving the Infrared Tracking Accuracy of Smart Cars

2020 ◽  
Vol 166 ◽  
pp. 339-343
Author(s):  
Jing Tao Li ◽  
Zhenf Eng Li ◽  
Xiao Fan Li ◽  
Yi Jian Yang ◽  
Jie Xiao
Keyword(s):  
Efficient Algorithm ◽  
Algorithm Design ◽  
Tracking Accuracy ◽  
Smart Cars ◽  
Infrared Tracking

Computational Micrograph Registration with Sieve Processes

1996 ◽  
Vol 54 ◽  
pp. 440-441
Author(s):  
P.J. Phillips ◽  
J. Huang ◽  
S. M. Dunn
Keyword(s):  
Planar Graph ◽  
Efficient Algorithm ◽  
Spatial Organization ◽  
Spatial Arrangement ◽  
Stereo Image ◽  
Image Model ◽  
Geometric Invariants ◽  
Point Set

In this paper we present an efficient algorithm for automatically finding the correspondence between pairs of stereo micrographs, the key step in forming a stereo image. The computation burden in this problem is solving for the optimal mapping and transformation between the two micrographs. In this paper, we present a sieve algorithm for efficiently estimating the transformation and correspondence.In a sieve algorithm, a sequence of stages gradually reduce the number of transformations and correspondences that need to be examined, i.e., the analogy of sieving through the set of mappings with gradually finer meshes until the answer is found. The set of sieves is derived from an image model, here a planar graph that encodes the spatial organization of the features. In the sieve algorithm, the graph represents the spatial arrangement of objects in the image. The algorithm for finding the correspondence restricts its attention to the graph, with the correspondence being found by a combination of graph matchings, point set matching and geometric invariants.

Sign in / Sign up

Export Citation Format

Share Document