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Since the height of aerial image shot is much lower than the height of satellite image shot, this may lead to the differences of the projection sizes and directions between every shooting location, which will result in serious picture distortion in most cases. In addition, the degree of distortion of the image edge region is much more serious than that in the center region, a distortion correction method based on Lagrange interpolation of polar coordinates has been proposed by this paper. We use this method to interpolate the single pixel in polar coordinates system. The pixel coordinate expression will be reconverted from polar coordinates to Cartesian coordinates after we corrected the pixel according the consequence of interpolation. And finally this method will be used to correct distortion pixel-by-pixel throughout the aerial image. The experiment results show that the corrected aerial image distortion does not exceed 3%, which proves that this algorithm can not only correct distortion image effectively, but also has a wide applicability compared with the traditional distortion correction methods, such as making use of the Direct Linear Transformation to obtain the distortion matrix.

A radar warning situation map(RWSM)is proposed in this article.This map can measure the distance,bearing,heading,velocity of the object and show these information on the monitor through the panorama and the radar map.The system obtained the video and the orientation of the object by the visible light detector.It calculated the image and the motion of the tracking target through the image processing algorithm and the pattern recognition method.The panorama technology was combined to form the RWSM.The laser range finder was replaced by the digital image processing and recognition technology to estimate the distance of the target very well.The ship experiment results showed that the RWSM can well present the posture of the targets,and the system can track the objects accurately and steadily.The cost of the system has been deduced greatly by the method.

To overcome the affine transformation,especially great viewpoint changing between the real-time image and the reference image,an anti-viewpoint changing image matching algorithm based on SURF is proposed to bridge the gap of the original one,which may lead to failed matching for the lack of correct matched pairs.The proposed algorithm simulated possible perspective transformations from the original image,generated a set of simulated image sequences,and extracted SURF feature points in the sequence images.Then,the feature points of the simulated image sequence were mapped to the reference image.Finally,the matching point pairs are obtained by the method of the euclidean distance criterion.Experiment results show that the proposed algorithm increases the matching pairs of the feature points,and overcomes the influence of viewpoint change(more than 60°) in image matching.

A grid-based histogram of the orientation gradient feature and visualization techinique were used in object tracking algorithm to solve the problem of tracking objects moving in surveillance video.The histogram of orientation gradient based on grid was designed as the local feature to describe the object.And it can apperceive the abnormalities of objects such as occlusion,disturbance and deformation etc.The visualization was used to real-time observe the changes of the characteistics through the changes of the local feature.The weights of the sub-model was adjusted adaptively to keep the stability of the tracking algorithm.The experimental results show that the proposed local feature can perceive the abnormity correctly,and can impove the robustness and stability of the tracking algorithm.The self-perception matching algorithm based on this local feature can tracking object accurately and stably in surveillance video.

For the problem of small target detection in infrared image,a method based on the local saliency is proposed.The feature on the local gray-scale of small targets in infrared image is analyzed,and a criterion is proposed to check the feature of peak value.Based on the criterion to check peak value and the characteristic of small target on time domain,a fast algorithm is designed.Firstly,local max points are selected and the follow-up computing is limited to these points to reduce the computation.Then peak values are checked based on the decline of gray-scale.Finally,false targets caused by noise are removed based on the continuity between frames.Experiments show that this algorithm has a high processing speed,and can effectively filter out the random noise in the image.

In order to solve the problem of detecting small dim targets in infrared images with the complicated air background,an improved algorithm based on image complexity and orientation gradient is put forward.The information entropy reflects the complexity of the image.The weighted information entropy with the variance and the change rate of local pixels suppresses both the inside and the edge of clouds.Since the image of the complexity is obtained,targets can be extracted by the means of multi-degree and multi-orientation gradient under the circumstance that the complexity of the background is higher than that of the targets.Experiments indicate that the algorithm can detect small dim targets effectively with complicated cloud background.

Aiming at the problem that traditional Meanshift algorithm can not guarantee tracking accuracy in certain interference or occlusion case and background pixel in object model will induce localization error,a improved targets tracking algorithm based on Meanshift algorithm was proposed.Firstly,the object model is improved by introducing weights which is decided by divisional degree between object and background,and then the weight was used in object model to reduce the localization error of object tracking.Secondly,the target in the tracking window is divided into a number of fragments,the improved Mean Shift algorithm is used in every fragments separately.Finally,the match degree of each fragment is computed and the fragment with the highest match degree is involved to achieve the whole target's coordinates and to avoid the influence of occluded fragments on the object location.Experimental results show that the new method can effectively and accurately track moving target in the cluttering background.

To overcome the problem that traditional Mean-Shift based tracking method is easy to fail because of the rigid tracking window with the changing of object scales,a new algorithm to estimate the scale of the tracking window is proposed.It divides the template image and the current frame image into several concentric cirques.After calculating the comparability measurement of corresponding cirques between the template and the current image,it ascertains the bandwidth parameter of current frame.Finally,Kalman filter is adopt to finish the update of the template scale and the stable object tracking is realized.Experiments prove that the proposed algorithm is able to renew the template accurately when the scale of the object changes,and the tracking reliability is improved compared to the traditional Mean-Shift based tracking method.

When the images collected from camera have perspective distortion,it can affect the following work such as feature extraction and object recognition due to the influence of perspective projection.When rectifying these distorted images,perspective transformation can change the relative position of inner points,while affine transformation cannot.This article will improve the Hough transformation to find the lines that hold the four points which perspective transformation needs,and then use the perspective transformation to rectify the distortion based on the imaging principle of cameras.Emulation experiment results from Matlab reveal that the algorithm can effectively rectify the distorted images and achieve our expectation.This algorithm is easy,feasible and robust,and can be applied to many fields including object recognition.