This paper proposes a significant moving target detection method combining time difference and optical flow field filter for scenes with complex backgrounds such as streets and traffic intersections. Firstly, the gradation change region of the sequence image is obtained by using the cumulative frame difference, then the Lucas-Kanade optical flow algorithm is introduced to obtain the optical flow field of the gray-scale change region. Finally, the optical flow time filter is used to filter the interference motion in the image background. The method can not only detect the motion area quickly and accurately, but also distinguish the spurious interference motion from the salient target (such as people, cars, etc.). So it detects the moving target in a complex background easily.
The paper introduces a new electronic image stabilization approach based on block matching. It chooses some fixed points which form a grid at the present frame and find out the corresponding points at the next frame. The better points are found out by the center-reservation algorithm. We can obtain the global motion estimation parameters from local motion parameters. Then the stable image sequence can be obtained by compensating image frames. Experimental results show that the algorithm can stabilize image sequence robustly and effectively.
Restoring blurred image,as one of the hot issues in the field of image processing,has important significance in improving the image quality. In recent years, a variety of methods for removing motion blur of an image have been proposed, but most of the algorithms are too complex and not applied. This paper educes an efficient algorithm for motion-blurred image. According the temporal profile of the infrared detector, point spread function (PFS) of the uniform linear motion-blurred image is discussed. The profile of the PSF is acquired by iterative Weiner-filter. Experimental results show that the method has accurate and applied performance for infrared blurred image got from actual system.
Main task of the infrared search and track system is analyzing and identifying targets of airspace. But first this is needed to detect all targets in infrared image. Therefore, the multi-target detection algorithms are studied and we propose an effective multi-target detection method. Firstly, an improved morphological operator is designed based on airspace background and target traits of infrared image. Background is weakened but targets are enhanced when infrared image is processed by the gray morphological filter. Then, potential targets are found by the maximum local sum algorithm. Finally, true targets are affirmed based on data association of sequence images. The infrared images got from long-wavelength infrared camera are processed with the method of the paper. Experiment results show that the method can detect targets in infrared image quickly and accurately.
Restoring motion-blurred image is the key technologies in the opto-electronic detection system. The imaging sensors such as CCD and infrared imaging sensor, which are mounted on the motion platforms, quickly move together with the platforms of high speed. As a result, the images become blur. The image degradation will cause great trouble for the succeeding jobs such as objects detection, target recognition and tracking. So the motion-blurred images must be restoration before detecting motion targets in the subsequent images. On the demand of the real weapon task, in order to deal with targets in the complex background, this dissertation uses the new theories in the field of image processing and computer vision to research the new technology of motion deblurring and motion detection. The principle content is as follows: 1) When the prior knowledge about degradation function is unknown, the uniform motion blurred images are restored. At first, the blur parameters, including the motion blur extent and direction of PSF(point spread function), are estimated individually in domain of logarithmic frequency. The direction of PSF is calculated by extracting the central light line of the spectrum, and the extent is computed by minimizing the correction between the fourier spectrum of the blurred image and a detecting function. Moreover, in order to remove the strip in the deblurred image, windows technique is employed in the algorithm, which makes the deblurred image clear. 2) According to the principle of infrared image non-uniform exposure, a new restoration model for infrared blurred images is developed. The fitting of infrared image non-uniform exposure curve is performed by experiment data. The blurred images are restored by the fitting curve.
For easily viewing and operating in The IRST system, it is necessary to montage the 360°panoramic image orderly and
correctly. The paper introduces a fast Panoramic mosaic method for the Infrared Search and Track (IRST) system. First of
all, zero position in azimuth is determined from position sensor. Then relative position of every image is obtained by the
position sensor. Next, the accurate position of image is calculated by integral time of the IR camera. Thus, the panoramic
image mosaic are montaged. This method works more quickly and accurately. The innovative point is obtaining accurate
position information of image making use of position sensor and integral time of the IR camera.
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