Infrared and visual image registration based on mutual information with a combined particle swarm optimization – Powell search algorithm

Zhuang, Youwen; Gao, Kun; Miu, Xianghu; Han, Lu; Gong, Xiaolin

doi:10.1016/j.ijleo.2015.09.199

Cited by 47 publications

(18 citation statements)

References 4 publications

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“…Image similarity metrics based on Mutual Information were first proposed by Viola [53] and Collignon et al [54]. Since then, they have been extensively used in the field of medical imaging [41,46,52,55] and, more recently, in remote sensing [39,42,44,56].…”

Section: Mutual Informationmentioning

confidence: 99%

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

et al. 2020

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Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires.

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Section: Mutual Informationmentioning

confidence: 99%

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

et al. 2020

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“…However the convergence rate decreased when reaching a near optimal solution, which will stop the optimizing. In the past few decades, many researches are attempted to improve the performance of PSO from the following aspects: 1) Improving value of the inertia weights [11][12][13][14]; 2) Increasing the diversity of PSO [15][16][17]; 3) Hybrid PSO with other intelligent optimization algorithm [18,19].…”

Section: The Proposed Social Emotional Pso (Sepso)mentioning

confidence: 99%

“…Then, according to the emotions, individuals take decision to guide their next behavior for a better social evaluation. Moreover, the emotions of individuals are evaluated with their emotional indexes [18,19].…”

Section: The Proposed Social Emotional Pso (Sepso)mentioning

confidence: 99%

“…PSO algorithm is one powerful and widely used swarm intelligence technique 19), and it is easy to implement. Although the original PSO is very simple with only a few parameters to adjust, it provides better performance in computing speed, computing accuracy, and memory size compared with other methods such as machine learning, neural network learning, and genetic computation.…”

Section: Introductionmentioning

confidence: 99%

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Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Zheng

Liao

2016

Optik

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“…PSO algorithm had been used to obtain registration parameter that is close to the global minimum. Powell search had been used to find a more precision registration parameter [22]. K.Aghajani et al proposed a robust image registration method based on total variation regularization under complex illumination changes.…”

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confidence: 99%

Comparative Analysis of Image Registration for Printed Circuit Boards

Kaur¹,

Kaur²,

Kaur³

2017

IJET

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Abstract-Image registration is a process of overlaying of two or more images taken at different times, different viewpoints and from different sensors. This paper aims to present comparative analysis of different image registration techniques for automated inspection of Printed Circuit Boards. In the automated inspection of Printed circuit Boards using image subtraction, it is essential to match the size and orientation of two images (reference and test image). The major goal of this paper is to provide a comparative analysis of image registration techniques used for automated inspection of Printed circuit boards. Correlation coefficient is using as a similarity measure for compare the result on quantitative basis. The results show the qualitative as well as quantitative analysis of the proposed work.

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Infrared and visual image registration based on mutual information with a combined particle swarm optimization – Powell search algorithm

Cited by 47 publications

References 4 publications

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

Image Similarity Metrics Suitable for Infrared Video Stabilization during Active Wildfire Monitoring: A Comparative Analysis

Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization

Comparative Analysis of Image Registration for Printed Circuit Boards

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