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Spatio-Context-Based Target Tracking with Adaptive Multi-Feature Fusion for Real-World Hazy Scenes. Cognit Comput 2018. [DOI: 10.1007/s12559-018-9550-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Liu X, Zhou S, Li H, Li K. Bidirectional scale-invariant feature transform feature matching algorithms based on priority k-d tree search. INT J ADV ROBOT SYST 2016. [DOI: 10.1177/1729881416682700] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
In this article, a bidirectional feature matching algorithm and two extended algorithms based on the priority k-d tree search are presented for the image registration using scale-invariant feature transform features. When matching precision of image registration is below 50%, the discarding wrong match performance of many robust fitting methods like Random Sample Consensus (RANSAC) is poor. Therefore, improving matching precision is a significant work. Generally, a feature matching algorithm is used once in the image registration system. We propose a bidirectional algorithm that utilizes the priority k-d tree search twice to improve matching precision. There are two key steps in the bidirectional algorithm. According to the case of adopting the ratio restriction of distances in the two key steps, we further propose two extended bidirectional algorithms. Experiments demonstrate that there are some special properties of these three bidirectional algorithms, and the two extended algorithms can achieve higher precisions than previous feature matching algorithms.
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Affiliation(s)
- XiangShao Liu
- Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China
| | - Shangbo Zhou
- College of Computer Science, Chongqing University, Chongqing, China
| | - Hua Li
- College of Computer Science, Chongqing University, Chongqing, China
| | - Kun Li
- College of Computer Science, Chongqing University, Chongqing, China
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Li G, Liu ZY, Li HB, Ren P. Target Tracking Based on Biological-Like Vision Identity via Improved Sparse Representation and Particle Filtering. Cognit Comput 2016. [DOI: 10.1007/s12559-016-9410-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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