Anisotropic infrared background prediction method

Qiang, 张强 Zhang; Jingju, 蔡敬菊 Cai; Qiheng, 张启衡 Zhang

doi:10.3788/hplpb20122402.0301

Cited by 3 publications

(1 citation statement)

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“…If the step size is too small, some target information will be smoothed out just like the smooth background. Zhang and Ling respectively improved the diffusion functions and proposed new diffusion functions for background modeling of weak small targets under a smooth background [17], [18]. These diffusion functions achieved good results in those scenes.…”

Section: ) Background Constraint Optimization Modelmentioning

confidence: 99%

Infrared Object Detection Based on Improved Twist Tensor Model

Feng,

Qin,

et al. 2024

IEEE Access

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Infrared target detection holds significant importance in automatic target search and tracking system under complex background. The conventional structural tensor models have not harnessed the full potential of spatio-temporal domain information in sequence scenes, and the strong edge contours in the image often lead to false alarms. In order to tackle this problem, we propose an improved twist tensor model based on the optimization of background constraints. Firstly, we propose a diffusion function according to the gradient difference between the target and backgrounds, which preserves the target signal to a great extent. Secondly, the spatio-temporal information of the sequence images is used to construct the twist tensor objective constraint optimization function, and the improved twist tensor effectively distinguishes the sparse components of the target and the low-rank components of the background. Finally, the optimized model is solved using ADMM to obtain the final target signal. Eight sequence images and nine comparison methods are performed for experimental validation, experiment results demonstrate that the proposed algorithm can effectively suppress the complex background while retaining the target signal well.INDEX TERMS complex background; twist tensor; background constraint; object detection I. INTRODUCTION

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Section: ) Background Constraint Optimization Modelmentioning

confidence: 99%

Infrared Object Detection Based on Improved Twist Tensor Model

Feng,

Qin,

et al. 2024

IEEE Access

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Dim and Small Target Detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

Fan

Qin

et al. 2023

IEEE Photonics J.

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Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

Fan

Guo

et al. 2020

Mathematical Problems in Engineering

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In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

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Anisotropic infrared background prediction method

Cited by 3 publications

References 0 publications

Infrared Object Detection Based on Improved Twist Tensor Model

Infrared Object Detection Based on Improved Twist Tensor Model

Dim and Small Target Detection Based on Spatio-Temporal Filtering and High-Order Energy Estimation

Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

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