Real-time image registration of the multi-detectors mosaic imaging system

Xinhua, 王新华 Wang; Wei, 黄玮 Huang; Ji-hong, 欧阳继红 Ouyang

doi:10.3788/co.20150802.0211

Cited by 7 publications

(2 citation statements)

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“…As these two sensors are more common, at present, small UAVs are usually equipped with infrared and visible sensors for target detection or tracking. Using the above principles can make it easier for small UAVs to lock the target [3]. However, due to differences in the time period, distance, shooting angle, etc., of UAV aerial photography, the images obtained by multi-source sensors may not be strictly aligned due to the existence of translation, rotation, scaling, and other spatial transformation relationships.…”

Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Zhang

Lin

et al. 2022

Electronics

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Image registration is the base of subsequent image processing and has been widely utilized in computer vision. Aiming at the differences in the resolution, spectrum, and viewpoint of infrared and visible images, and in order to accurately register infrared and visible images, an automatic robust infrared and visible image registration algorithm, based on a deep convolutional network, was proposed. In order to precisely search and locate the feature points, a deep convolutional network is introduced, which solves the problem that a large number of feature points can still be extracted when the pixels of the infrared image are not clear. Then, in order to achieve accurate feature point matching, a rough-to-fine matching algorithm is designed. The rough matching is obtained by location orientation scale transform Euclidean distance, and then, the fine matching is performed based on the update global optimization, and finally, the image registration is realized. Experimental results show that the proposed algorithm has better robustness and accuracy than several advanced registration algorithms.

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Section: Introductionmentioning

confidence: 99%

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Zhang

Lin

et al. 2022

Electronics

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“…The detected feature points have high stability, able to adapt to complex environments. Therefore, the SIFT algorithm is applied to the detection of the back of the car image, which can realize the detection of components better [1][2].…”

Section: Introductionmentioning

confidence: 99%

A New Car Seat Detection Method

Li¹,

Zhou²,

Zhang³

2018

Proceedings of the 2017 4th International Conference on Machinery, Materials and Computer (MACMC 2017)

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Abstract. In order to detect the products of car seat, propose a new car seat detection method in this paper. First, the car seat image is normalized to 256 pixels *256 pixels image. Then extract SIFT (scale invariant feature transform) feature points and match the points. According to the position of two matched points, the matching results are divided into two categories. One is vertical match point; the other is tilt matching point. Compare the number of the two categories. When the vertical match points are more than the tilt matching points, the answer is correct. Otherwise the answer is wrong. Experimental results show that for the detection of three different types of car seats, the detection accuracy is higher than 98%.

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Three Cameras Car Seat Images Mosaic Based on Improved SIFT Method

Zhu

Xiao

2017

Advances in Intelligent Systems and Computing

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Real-time image registration of the multi-detectors mosaic imaging system

Cited by 7 publications

References 0 publications

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

Infrared and Visible Image Registration Based on Automatic Robust Algorithm

A New Car Seat Detection Method

Three Cameras Car Seat Images Mosaic Based on Improved SIFT Method

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