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

Efficient Moving Target Tracking Algorithm: Design and Implementation of the Sand-Table Tracking Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 1032-1039
Author(s):  
Jia Qi Li
Keyword(s):  
Target Tracking ◽  
Algorithm Design ◽  
Tracking Algorithm ◽  
Moving Target ◽  
Tracking Accuracy ◽  
Moving Target Tracking ◽  
Design And Implementation ◽  
Tracking Ability

Working on the design of a new algorithm :sand_table algorithm.The algorithm could work well in recognizing and tracking an single moving target shot by camera or in a video .The algorithm works simple with low operation cost.May used in tracking different object of many kinds.The algorithm imitate the the process of falling sands to Greatly enhance the tracking ability and tracking accuracy.

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

Tracking Algorithm Design and Comparison of High Speed High Maneuvering Target

2015 ◽  
Vol 713-715 ◽  
pp. 2053-2057
Author(s):  
Xiu Fang Wang ◽  
Jin Ye Peng ◽  
Bin Chen ◽  
Wei Qi
Keyword(s):  
Connected Graph ◽  
High Speed ◽  
Algorithm Design ◽  
Variable Structure ◽  
Multiple Model ◽  
Tracking Accuracy ◽  
Maneuvering Target ◽  
Near Space ◽  
Set Up ◽  
Fixed Structure

Aiming at the problem that the traditional tracking method cannot track high speed high maneuvering target effectively, one modified fixed structure multiple model algorithm (M-FSMM) and one modified variable structure multiple model (M-VSMM) algorithm were proposed. The Constant Velocity (CV) model, Current Statistical (CS) and Modified Coordinate Turn (MCT) model were adopted in the M-FSMM algorithm, by means of Connected Graph (CG) thinking, the model connected graph was made up by models that can describe possible motion, the connected relation was set up and model self-adapting was designed to carry out the variable structure tracking that can quickly jump between models. Monte Carlo simulation results show that the two methods can track high speed high maneuvering target effectively, the computational quantum of M-FVMM algorithm is larger but the tracking accuracy and stability are better than the M-FSMM algorithm. They can be used to track near space hypersonic targets.

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

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.

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