Target location is a fundamental application in aerial image process. In this work, a fast normalized cross correlation algorithm is proposed for the application of target location in aerial image. Firstly, normalized cross correlation has been proved equivalent to Euclidean distance. In the search step, the target template and the corresponding window of base image are projected to a set of mutually orthonormal vectors for calculating the lower bound of the distance, where the windows with too large distance relative to the target template will be rejected in this step. Finally, the directly normalized cross correlation calculating is applied to the rest windows of base image to achieve the final correct location of target. The experimental results show that compared with traditional method, the proposed method significantly improved the computational complexity without sacrificing the spatial resolution or the accuracy of the match result.
KEYWORDS: Target detection, Detection and tracking algorithms, Signal to noise ratio, Stars, Point spread functions, Aerospace engineering, Image processing, Space reconnaissance, Nonlinear filtering, Infrared detectors
In this paper, aiming at the small target detection problem in the infrared image sequence, we propose a small target detection method based on maximum likelihood estimation and NNLoG spot detection operator. Compared with the traditional method, our proposed method can partially solve the nonlinear motion of the small target in image sequence. The real target trajectory is approximated by polynomial to enhance the signal to noise ratio of target. To validate the proposed method, we create eight experiments to simulate. The experiment result shows that our method is very valuable for small target detection.
KEYWORDS: Target detection, Image segmentation, Signal to noise ratio, Detection and tracking algorithms, Clouds, Linear filtering, Image processing, Digital filtering, Signal detection, Image processing algorithms and systems
In this paper, a recursive higher order statistics algorithm is proposed for small target detection in temporal domain. Firstly, the background of image sequence is normalized. Then, the higher order statistics are recursively solved in image sequence to obtain the feature image. Finally, the feature image is segmented with threshold to detect the small target. To validate the algorithm proposed in this paper, five simulated and one semi-simulation image sequences are created. The ROC curves are employed for evaluation of experimental results. Experiment results show that our method is very effective for small target detection.
The temporal profile of the small target in the sky background and the disadvantage of traditional method are analyzed. Then we propose a novel method of small target detection based on image sequence in complex sky background. The illumination intensity is normalized firstly in image sequence. Then, the temporal matrix is decomposed using SVD in both horizontal and vertical direction. The small target is extracted in these two directions. Then, add these two images. Finally, segment the image with threshold. Obtain the trajectory of the small target in image sequence. The simulations show that our method is robust and effective.
Video stabilization is a critical step for improving the quality of videos captured by unmanned aerial vehicles. However, the complicated scenarios in the video and the need for instantaneously presenting a stabilized image posed significant challenges to the existing methods. In this work, an instantaneous video stabilization method for unmanned aerial vehicles is proposed. This new approach serves several purposes: smoothing the video motion in both two-dimensional and three-dimensional (3-D) scenes, decreasing the lags in response, and instantaneously providing the stabilized image to users. For each input frame, our approach regenerates four short motion trajectories by applying interframe transformations to the four corners of the image rectangle. An adaptive filter is then performed to smooth motion trajectories and suppress the lags in response simultaneously. Finally, at the stage of image composition, the quality of image is considered for selecting a visually plausible stabilized video. Experiments show that our approach can stabilize various videos without the need for user interaction or costly 3-D reconstruction, and it works as an instant-process for videos from an online source.
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