A particle filter for object tracking based on multi-region sampling is proposed to solve the problems of degeneracy phenomenon and particle impoverishment introduced by traditional particle filter algorithm. The proposed method uses some overlapping sub-regions to divide the target model, and each sub-region corresponds to a sampling windows. The true state of target can be estimated by the confidence of each sub-region. The complementary and stage uniqueness of sub-region can guarantee the validity of particles and the quality of state-space. Thereby, the accuracy of object tracking is improved. Experimental results show that the proposed method relieves effectively the sample degradation and poverty problems, improves the efficiency of particles, and is robust to pose, illumination and partial occlusion in the complex background.

In order to solve the challenging problems of recognizing object in the angles changing and occlusion, a novel multi-angle algorithm is proposed by combining the Gabor feature and shared LIOP (Local Intensity Order Pattern) feature during the changing poses. First of all, the input image is filtered by a 2D Gabor filter in 4 directions and 16 scales to obtain 64 groups of characteristic response map. And then the scale and translation invariant feature can be derived from computing the maximum response value among the adjacent scales and position. Secondly, the geometric transformation algorithm is utilized to gain the shared LIOP feature under different perspectives. Thirdly, for reducing the time complexity, the dimension of combined features is reduced by the principal component analysis. At last, the calculated feature is trained and learned in SVM for the detecting model. Two standard test databases, Caltech 101 and UIUC car, are introduced to evaluate the accuracy and robustness of the proposed algorithm. The experimental results show that the average precision on two standard databases reach 92.1% and 95.4% high respectively, indicating the excellent performance of the proposed algorithm in recognizing object under various scales and angles.

The difficulty of person re-identification is that the same person images in different cameras are significantly different, which is difficult to stably describe the images by a single feature, while the fusion by a variety of features can’t distribute their weights exactly. To solve the problems, a person re-identification algorithm based on multi-kernel support vector machine by multi-instance learning is proposed. Firstly, the blocked color features in HSV space and local features of SIFT from the same people image under different cameras are extracted, and the bag of words are constructed to SIFT features. Both of them are taken as two instances and encapsulated as a bag especially. Secondly, the multi-kernel support vector machine model is optimized, the bags are trained by the linear fusion kernel between Gaussian and polynomial, and then the optimal weighting ratio is obtained by multi-instance learning. Finally, this algorithm is tested on the VIPeR dataset, the accuracy rate of recognition is an average accuracy of ten times experiments, and expressed by CMC curves. At the same time, the matching result of the sample is also sorted. The experiments show that the robustness and recognition rate of this algorithm achieve the same and even better results while compared with several state of the art algorithms.

This paper derives the rate equations for mirror tracking system with unconventional sensor-to-gimbal orientation and a complete decoupled equation is given. Furthermore, a simplified equation and a linearized equation with first order Taylor expansion and rounding are given to be implemented under digital controller. Simulation results show the validity and effectiveness of the given equations.

Using satellite imagery for cumulus cloud detection has an important significance for preventing meteorological disasters. Support Vector Machine (SVM), which can seek the best compromise between the complexity of the model and the learning ability based on finite sample information, is expected to play a role in the cumulus cloud detection. However, the traditional SVM is very sensitive to the samples of noise and outlier, and doesn’t possess the skill of fuzzy treatment, which doesn’t meet the fuzzy and uneven characteristics of satellite imagery and the complex and diverse cloud patterns. In order to solve the problem of SVM, this paper introduces Fuzzy Support Vector Machine (FSVM) and defines the range-rate of the distances from the adjacent samples to the class center, based on the distribution characteristics of training samples. Then, on the basis of the range-rate, we weed out the possible noises and outliers of training set and overcome the shortcoming that the affinity FSVM is susceptible to noises and outliers at the time of calculating the radius of smallest hyper-sphere, so as to make the obtained membership better reflect the variance of different sample sets. The experimental results show that, for FY2D satellite imageries, extracting 8-d spectral features from different channels, compared with traditional SVM and affinity FSVM, the accuracies of cumulus cloud detection based on the proposed method increase respectively by about 2% and 1%. The proposed method owns stronger adaptability and noise robustness, and can make better effect on early warning disastrous weather such as thunderstorm.

A method of calibrating radial distortion for fish-eye lens based on collinear constraint is proposed. The method only needs a single image which contains at least two sets of parallel lines in 3D world. Under single parameter division model, these lines are imaged as circular arcs in the distorted image. These circular arcs imaged from the same set of parallel lines will intersect at two common points, and thus their centers are collinear. Under this constraint, the calculation of parameters of the single parameter division model is then proposed, which can be solved by using the Levenberg-Marquardt algorithm effectively. Experimental results of both synthetic and real images show that the proposed method is simple, robust and accurate.

A type of improved DNA genetic algorithm is proposed, which is used to solve the shortcomings like slow rate of convergence and easy precocity. The algorithm has increased the population diversity by using DNA encoding mode of the base complementarities, avoided falling into the local extremum. The replacement adaptive crossover operator and codon mutation operator are designed based on the DNA molecule operation, so it improves the genetic algorithm's search capability, and increases the convergence speed and efficiency. The algorithm is adopted to seek the best threshold of two-dimensional Arimoto. At last, the image is segmented. It is proved that this improved DNA genetic algorithm has improved segmentation validity by both the ordinary image and medical image.

In order to overcome the shortcoming of previous S-shaped curve global dimming algorithm for face image, an improved S-shaped curve global dimming algorithm is proposed. The images containing faces are first figured out based on several format kinds of signals. The S-shaped curve is then established to compensate the LC pixels. Simulation results show that the improved algorithm can detect face image accurately and increase the displayed image quality effectively. Prototype testing results show that the image quality of LCD TV has improved significantly and the average display energy consumption decreases by 21.28%.

For a structure design of a space camera’s reflector, the reflector’s characteristic is big rectangle and big caliber. Based on optimization material, analyze rectangle reflector’s support characteristic, study reflector’s support theory, put forward a new flexible support based on half kinematics theory, namely a complex support form that three point on backside and three accessorial point, and analyze statics, dynamics and thermal characteristics by computer. It is tested that the project is reasonable and suitable for the big rectangle and big caliber reflector. We settle a difficult problem of the big rectangle and big caliber reflector’s support.

In order to realize some ammunition exploding at preconcerted height to achieve the optimal destructive effects, a kind of height and angle finding system was put forward based on laser multi-detecting module. This system could calculate the flying height, pitch angle and roll angle of missile with use of the un-coplanar narrow view field laser module measuring the distance between ground and missile. Multi-variable Taylor formula was applied to analyze the height-finding and angle-measurement error induced by the range-finding error. The analysis results indicate that the error meets the required standard for some air-to-surface ammunition.

A fast calibration method for structured-light parameters of 3D measurement system based on the planar target is proposed. This method, based on photogrammetry technology, using the code point making planar target, obtains the target plane light images from the camera. After the affine transformation and resection according to the codes point’s image coordinates and target coordinates, the camera coordinate of light plane calibration point can be got quickly and easily, then calibration completed after the plane fitting. Experiments results indicate that this method has high accuracy, the RMS of light plane fitting is better than 0.07 mm, and the average relative error is better than 0.33%. Furthermore, this method is also suitable for many other modes structured light with high generality and efficient, flexible and suitable for field calibration.

For the spectral detection of weak targets in space in daytime, the spectral radiation characteristics of not only the target itself, but also the skylight background surrounding the target must be known. In this paper, Lowtran software is adopted to analyze the spectral radiation characteristics of the skylight background, and a skylight background measurement equipment is developed to detect the real spectral radiation characteristic of the skylight background in a certain region in western China. The measurement results may provide useful information for the spectral detection of target in space. (Then the measurement results of it are given, which will lay the foundations of the further research of the spectral detection.)

In Brillouin lidar system based on the Rayleigh-Brillouin scattering (RBS) and Fabry-Perot (F-P) etalon, the spectral broadening of F-P etalon could produce measurement error of spectrum characteristics. A fitting procedure based on the combination of the dual-parameter fitting and single-parameter fitting for removing the broadening effect is proposed in this paper. Firstly, based on the analysis of the broadening effect of F-P etalon on the spectrum, a dual-parameter Levenberg-Marquardt (L-M) algorithm fitting is proposed and the effects of the initial values of the fitted parameters and the iteration steps in the fitting are analyzed. Then to deal with one parameter’s floating, an extra single-parameter fitting procedure is proposed to improve the measurement accuracy. Experiments show that the fitted results can be stable in the magnitude of MHz and the measurement accuracy of temperature can be improved to 0.1 ℃.

The rigorous coupled wave analysis is used to analyze the spatial spectrums filtering of light coupling in the metal-dielectric multilayer metamaterials. The band pass window and transmission amplitude of spatial spectrums in the Al/MgF2 metamaterials are carefully investigated for different unit thickness, filling ratio, film loss and pairs of Al/MgF2 multilayers. It is demonstrated that the band pass window is mainly determined by the unite thickness and filling ratio, while the transmission amplitude is mainly determined by the imaginary loss and pairs of film materials. This research has potential application in the superresolution imaging lithography, surface plamons structured illumination, ultrathin surface microscopy and bio sensing etc.