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Journal of Projectiles, Rockets, Missiles and Guidance >

2024 , Vol. 44 >Issue 5: 82 - 89

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2024.05.010

An Improved Shape Context Ant Colony Algorithm for Edge Scene Matching

  • LIU Peng 1 ,
  • YAN Gongmin 2 ,
  • TIAN Ye 2 ,
  • CHEN Long 2
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  • 1 National Graduate College for Engineers, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China
  • 2 School of Automation, Northwestern Polytechnical University, Xi’an 710072, Shaanxi, China;

Received date: 2024-07-23

  Online published: 2024-12-18

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Abstract

A method is proposed to partially match edge features using an improved shape context combined with ant colony algorithm to address the problem of larger scale differences and matching errors between real-time and baseline images when projectiles fly over deserts, mountains, and other areas at low altitude. To overcome the shape differences of edge features in heterogeneous images, edge neighborhood features are extracted based on the shape context algorithm. Rough matching is performed based on similarity of the edge neighborhood features to select candidate edges. Then, the position relationship and shape similarity between the candidate edges and the quasi-edge points to be matched are used to make the ants traverse all the edge points after initialization. Then, the transformed matching point pairs' distance weighted sum is used to calculate the pheromone increment, its weight is calculated from the direction constraint relationship of adjacent matching point pairs. And based on the distance relationship between adjacent matching pairs, the edge segments that do not have the correct matching relationship can be identified, when removing the matching point pairs on this segments, the weight of the corresponding matching pairs is zero. Finally, multiple iteration by multiple ants can obtain optimal matching result. Scene matching experiments are conducted on a measured infrared visible light dataset, and the results showed that the proposed algorithm is significantly better than the SIFT algorithm and can overcome the shape differences of edge features on heterogeneous images, achieve accurate matching of two edge features, and improve localization accuracy by 43.71% compared to traditional edge feature matching methods.

Key words： scene matching; edge features; shape context; ant colony algorithm

Cite this article

LIU Peng , YAN Gongmin , TIAN Ye , CHEN Long . An Improved Shape Context Ant Colony Algorithm for Edge Scene Matching[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2024 , 44(5) : 82 -89 . DOI: 10.15892/j.cnki.djzdxb.2024.05.010

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