We proposed a saliency based algorithm to detect the ground mobile targets, such as plane and vehicle, in the images. The algorithm combines the bottom-up and top-down mode to detect the targets. Firstly, in the bottom-up mode, the algorithm extracts the low level image features, such as intensity, standard deviation and Gabor features, to calculate the difference between the current pixel and the pixel around it and use the difference as the bottom-up saliency of the pixel; Then, the algorithm extracts the HOG features of the plane and vehicle targets to train a SVM classifier, which can learn the high level knowledge of the target. When testing on a new image, the classifier uses the knowledge to predict the possibility of appearance of the target as the top-down saliency of each position of the image; Finally, we combine the two saliency maps to get the final saliency map and use it to detect targets in the image. Experiments show that the algorithm can effectively detect the ground mobile targets robustly in complex backgrounds.
As one of widely applied nonlinear smoothing filtering methods, median filter is quite effective for removing salt-andpepper noise and impulsive noise while maintaining image edge information without blurring its boundaries, but its computation load is the maximal drawback while applied in real-time processing systems. In order to solve the issue, researchers have proposed many effective fast algorithms and published many papers. However most of the algorithms are based on sorting operations so as to make real-time implementation difficult. In this paper considering the large scale Boolean calculation function and convenient shift operation which are two of the advantages of FPGA(Field Programmable Gate Array), we proposed a novel median value finding algorithm without sorting, which can find the median value effectively and its performing time almost keeps changeless despite how large the filter radius is. Based on the algorithm, a real-time median filter has been realized. A lot of tests demonstrate the validity and correctness of proposed algorithm.
This paper presents a novel infrared and visual image registration method based on phase grouping and mutual information of gradient orientation. The method is specially designed for infrared image navigation, which is different from familiar multi-sensor image registration methods in the field of remote sensing. The central idea is to firstly extract common salient structural features from visual and infrared images through phase grouping, then registering infrared image to visual image and estimating the exterior parameters of the infrared camera. Two subjects are involved in this reports: (1) In order to estimate image gradient orientation accurately, a new method based on Leguerre-Gauss filter is presented. Then the image are segmented by grouping of pixels based on their gradient orientations and ling support regions are extracted as common salient structural features from infrared and visual images of the same ground scene. (2)In order for registering infrared and visual image, coordinate systems are constructed, coordinate transformations are formularized, and the new similarity measures based on orientation mutual information is presented. Quantitative evaluations on real and simulated image data reviews that the proposed method can provide registration results with improved robustness and accuracy.
This paper presents a novel image feature extraction algorithm based on multiple ant colonies cooperation. Firstly, a low resolution version of the input image is created using Gaussian pyramid algorithm, and two ant colonies are spread on the source image and low resolution image respectively. The ant colony on the low resolution image uses phase congruency as its inspiration information, while the ant colony on the source image uses gradient magnitude as its inspiration information. These two ant colonies cooperate to extract salient image features through sharing a same pheromone matrix. After the optimization process, image features are detected based on thresholding the pheromone matrix. Since gradient magnitude and phase congruency of the input image are used as inspiration information of the ant colonies, our algorithm shows higher intelligence and is capable of acquiring more complete and meaningful image features than other simpler edge detectors.
The task of small target detection is to extract the small targets from the background image including clutter, noise and jitter background, so it is difficult to deal with. In this paper, after analyzing infrared small targets, noise and clutter model, we use a small window median filter to estimate the infrared background. Then using background cancelling method, that is, subtracting the estimated background from the source image, the resident image can be obtained. Finally, an adaptive threshold is used to segment the residual image to obtain the potential targets. Considering the computational load, the two-dimensional filter is simplified into a one-dimensional filter. Experimental results show that the algorithm achieved good performance and satisfy the requirement of real-time processing of large size infrared image.
Temperature is an important feature of infrared targets. However, due to the attenuation and distortion parameters in radiation transmission process are unknown, precise temperature measurement is a difficult task. In this paper, a modified Dual-Band Ratio (DBR) temperature measurement method for remote target is proposed. The method is based on a new presented variation derived from the temperature change process named Dual-Band Differential Ratio (DBDR). Firstly, the temperature of the target is estimated by the traditional DBR method, and then a correction using DBDR information is carried out to improve the measurement accuracy. Experiment results showed that the proposed method can improve the temperature measurement accuracy and it could also be carried out without any prior information about the target.
The study of moving target detection has high research value and wide developing perspective. Considering of real-time detection of typical moving ground targets, a novel algorithm is proposed, which is based on background estimation via using Gaussian mixture model and reference background frame updating. Firstly the image gray of the target and background is supposed to obey Gaussian distribution, then the whole image is divided into three Gaussian distribution and estimated to form the reference image, finally detection results can be obtained via subtracting the reference image from current frame image. At the mean time the reference image is updated with time to keep the adaptability of the background image. Experimental results show that the algorithm is effective for moving ground targets such as vehicle.
Electronic digital image stabilization technique plays important roles in video surveillance or object acquisition.
Researchers have presented many useful algorithms, which can be classified to three kinds: gray based methods,
transformation based methods and feature based methods. When scenario is simple or flat, feature based methods
sometimes have imperfect results. Transformation based methods usually accompany large computation cost and high
computation complexity. Here we presented an algorithm based on gray projection which divided the whole image into
four sub-regions: the upper one, the bottom one, the left one and the right one. For making the translation estimation
easier, a central region is also chosen. Then the gray projections of the five sub-regions were counted. From the five pairs
of gray projections five group offsets including rotation and translation were obtained via cross correlation between
current frame and reference frame gray projections. Then according to the above offsets, the required parameters can be
estimated. The expected translation parameters(x axis offset and y axis offset) can be estimated via the offsets from the
central region image pair, the rotation angle can be calculated from the left four groups offsets. Finally, Kalman filter was
adopted to compute the compensation. Test results show that the algorithm has good estimation performance with less
than one pixel translation error and 10 percent rotation error. Based on this kind of gray projection algorithm, a real-time
electronic digital image stabilization system has been designed and implemented. System tests demonstrate the system
performance reaches the expected aim.