Inspired by a recent algorithm on clustering, we proposed an improved algorithm which combines the Davies Bouldin criterion to obtain the right number of the cluster centers automatically and output the right clusters. Davies-Bouldin criterion can describe the intra-class scatter and inter-class deviation value of the clustering result. In our algorithm, we first calculate the density and the distance of the sample points, which contain the information of the density distribution leading to the right clusters; Then, we choose two thresholds of the density and the distance to obtain the maximum number of the cluster centers; Finally, our algorithm automatically searches the right number of cluster centers through calculating the Davies-Bouldin value of every clustering result and choose the one which has the minimum Davies-Bouldin value. Experiments show that our algorithm can not only output the right clustering result when the sample points are disturbed and with special density distribution, but can also obtain the right number of the cluster centers automatically.
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.
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.
The description of local surface features is a critical step in surface matching and object recognition. We present a descriptor for three-dimensional shapes based on the bispectrum of spherical harmonics (BSH). First, points in a support region of a feature point are used to construct a local reference frame, and a histogram is formed by accumulating the points falling within each bin in the support region. Second, spherical harmonic coefficients of the histogram and its bispectrum are calculated. Finally, the feature descriptor is obtained via principal component analysis. We tested our BSH descriptor on public datasets and compared its performance with that of several existing methods. The results of our experiments show that the proposed descriptor outperforms other methods under various noise levels and mesh resolutions.
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.
After a deep study of the principle of infrared polarization imaging detection, the infrared polarization information of target and background is modeled. Considering the partial polarized light can be obtained by the superposition of natural light (unpolarized light) and linearly polarized component while ignoring the component of circularly polarized light, and combing with the degree of polarization (DOLP) and the angle of polarization (AOP), the infrared polarization information is expressed by the multiplying of an intensity factor by a polarization factor. What we have modeled not only can be used to analyze the infrared polarization information visually and profoundly, but also make the extraction of polarized features convenient. Then, faced with different application fields and based on the model, a target information enhancement program is proposed, which is achieved by extracting a linear polarization component in a certain polarized direction. This program greatly improves the contrast between target and background, and can be applied in target detection or identification, especially for camouflage or stealth target. At last, we preliminarily tested the proposed enhancement method exploiting infrared polarization images obtained indoor and outdoor, which demonstrates the effectiveness of the enhancement program.
It is a difficult point to detect and recognize artificial targets under the disturbance of the complex ground clutter when remote sensing and detection to the earth. Using the different polarization information between artificial object and natural scenery, the ability to distinguish artificial targets from natural scenery can be promoted effectively. On account that the differences of polarization characteristics is an important factor in designing the target recognition method, this paper focuses attention on the application of remote sensing and reconnaissance and makes detailed research on the long wave infrared polarization characteristics of several typical metallic targets, such as aluminum plate and iron plate and the aluminum plate that be coated with black paint or yellow green camouflage. Then, the changing rules of the degree and angle of the long wave infrared polarization changing with the measurement temperature are analyzed and researched. Work of this paper lays the theoretical foundation for the design of remote sensing and detection system based on the infrared polarization information in the future.
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.
The information of range between missile and targets is important not only to missile controlling component, but also to
automatic target recognition, so studying the technique of passive ranging from infrared images has important theoretic
and practical meanings. Here we tried to get the range between guided missile and target and help to identify targets or
dodge a hit. The issue of distance between missile and target is currently a hot and difficult research content. As all know,
infrared imaging detector can not range so that it restricts the functions of the guided information processing system
based on infrared images. In order to break through the technical puzzle, we investigated the principle of the infrared
imaging, after analysing the imaging geometric relationship between the guided missile and the target, we brought
forward the method of passive ranging based on equivalent area and provided mathematical analytic formulas.
Validating Experiments demonstrate that the presented method has good effect, the lowest relative error can reach 10%
in some circumstances.
The background noise of the images from passive sensors normally is non-Gaussian, it is strong relativity in column direction. This paper will present an IR target's detection method using difference filter based on space difference to deal with such image data. From the simulations, we can find that this method is effective for the correlative background.
This paper will not only discuss the precision tracking using segmentation in infrared (IR) images, but also describe how to avoid using the a priori information in implementing the precision tracking with segmentation. The method presented above is not limited to single target case, it can be extended to multiple target tracking, too. For the two cases, we have done Monte Carlo simulations. The considerable simulation tests show that this tracking method is successful not only in recognizing and tracking targets automatically but also in tracking the specified target. Though the noise background is complicated, they all have good effect and high precision.
Large power stations are usually chosen as the target for missiles guided by IR seeker. This paper presents an aimpoint finding algorithm. The high light blob of boiler and straight lines of chimneys or water cooling towers are taken as the reference feature objects for locating and keeping the aimpoint form long to short distance. Some experiment results are given to show the validity of our method.
In this paper, we will discuss the issue of recognizing the runway target in IR images and choosing the aimpoint. At first, we detect out the target from complicated background, Then we use tow characteristics to recognize it and choose the optimal aimpoint. Some results are presented. From the experiment, our method operates successfully.
A method of visual-infrared sensor fusion for target recognition is described in the paper. The fusion system introduced by Huntsberger are discussed in detail. The six type of bimodal neurons are created and a three layer neural network for integrating two inputs from different sensors is introduced. Some experiments are shown.
On the topic of the passive position location and tracking, some people have published many papers. In this paper, we will discuss the passive position location and tracking based on the extended Kalman filter. In our experiments, the watching stations are stationary, but the targets are moving. We make the two watching stations work together at first, and obtain the original values for the extended Kalman filter. When the estimating or filtering error of Kalman filter is small enough, then we can make tow stations work separately. So each station can at least track one target. In the end of this paper, we give out some simulation experiment results, from which we can verify the method we proposed.
The accurate form of image noise is helpful for noise- suppressing. The random process of 1/f noise is analyzed with wavelet functions and a Maximum Likelihood method is employed for finding the accurate power spectral density of 1/f noise. The signal is estimated effectively from noise image.
This paper will introduce an approach to eliminate the image quivering, that is called phase correlation approach. The method can effectively measure the pixel offsets and correct it. Its accuracy is up to subpixel. Through the processing, we can use energy accumulation method to detect out the dim point target easily.
In this paper, we introduce a neural network recognition method, MENN (minimum error neural network) method, in target recognition. From the target gray sequences, we can extract some useful characteristics. Then we use these features as the input data of the MENN classifier. By these characteristics, using the MENN classifier we can easily pick out the true targets from the candidate target sequences. MENN recognition method can not only pick out the true target and reject the false targets, but it also gets rid of the baits. Therefore, it has high reliability. Moreover, it has many advantages, for example, its training is a one pass process, its test process is not only simple but also straightforward, and its calculation is simple, etc. On account of those advantages, MENN recognition method is adaptive to the need of realtime processing.
This paper deals with the detection of dim point targets in infrared images. Dim point targets detection is always a difficulty in information processing. Researchers have proposed many effective methods in this aspect, this paper no longer mentions them but will introduce a new method. Whereas difference method has obtained good results in 1D signal processing, this paper manages to apply it to 2D signal processing, that is to say, dim point targets detection in infrared images of low SNR.