Zhihui Lei

Multi-modal image matching based on local frequency information

This paper challenges the issue of matching between multi-modal images with similar physical structures but different appearances. To emphasize the common structural information while suppressing the illumination and sensor-dependent information between multi-modal images, two image representations namely Mean Local Phase Angle (MLPA) and Frequency Spread Phase Congruency (FSPC) are proposed by using local frequency information in Log-Gabor wavelet transformation space. A confidence-aided similarity (CAS) that consists of a confidence component and a similarity component is designed to establish the correspondence between multi-modal images. The two representations are both invariant to contrast reversal and non-homogeneous illumination variation, and without any derivative or thresholding operation. The CAS that integrates MLPA with FSPC tightly instead of treating them separately can more weight the common structures emphasized by FSPC, and therefore further eliminate the influence of different sensor properties. We demonstrate the accuracy and robustness of our method by comparing it with those popular methods of multi-modal image matching. Experimental results show that our method improves the traditional multi-modal image matching, and can work robustly even in quite challenging situations (e.g. SAR & optical image).

Smeared star spot location estimation using directional integral method

Image smearing significantly affects the accuracy of attitude determination of most star sensors. To ensure the accuracy and reliability of a star sensor under image smearing conditions, a novel directional integral method is presented for high-precision star spot location estimation to improve the accuracy of attitude determination. Simulations based on the orbit data of the challenging mini-satellite payload satellite were performed. Simulation results demonstrated that the proposed method exhibits high performance and good robustness, which indicates that the method can be applied effectively.

A Simple and Flexible Calibration Method of Non-overlapping Camera Rig

A simple and flexible method for non-overlapping camera rig calibration that includes camera calibration and relative poses calibration is presented. The proposed algorithm gives the solutions of the cameras parameters and the relative poses simultaneously by using nonlinear optimization. Firstly, the intrinsic and extrinsic parameters of each camera in the rig are estimated individually. Then, a linear solution derived from hand-eye calibration scheme is proposed to compute an initial estimate of the relative poses inside the camera rig. Finally, combined non-linear refinement of all parameters is performed, which optimizes the intrinsic parameters, the extrinsic parameters and relative poses of the coupled camera at the same time. We develop and test a novel approach for calibrating the parameters of non-overlapping camera rig using camera calibration and hand-eye calibration method. The method is designed inter alia for the purpose of deformation measurement using the calibrated rig. Compared the camera calibration with hand-eye calibration separately, our joint calibration is more convenient in practice application. Experimental data shows our algorithm is feasible and effective.

Videometric terminal guidance method and system for UAV accurate landing

We present a videometric method and system to implement terminal guidance for Unmanned Aerial Vehicle(UAV) accurate landing. In the videometric system, two calibrated cameras attached to the ground are used, and a calibration method in which at least 5 control points are applied is developed to calibrate the inner and exterior parameters of the cameras. Cameras with 850nm spectral filter are used to recognize a 850nm LED target fixed on the UAV which can highlight itself in images with complicated background. NNLOG (normalized negative laplacian of gaussian) operator is developed for automatic target detection and tracking. Finally, 3-D position of the UAV with high accuracy can be calculated and transfered to control system to direct UAV accurate landing. The videometric system can work in the rate of 50Hz. Many real flight and static accuracy experiments demonstrate the correctness and veracity of the method proposed in this paper, and they also indicate the reliability and robustness of the system proposed in this paper. The static accuracy experiment results show that the deviation is less-than 10cm when target is far from the cameras and lessthan 2cm in 100m region. The real flight experiment results show that the deviation from DGPS is less-than 20cm. The system implement in this paper won the first prize in the AVIC Cup-International UAV Innovation Grand Prix, and it is the only one that achieved UAV accurate landing without GPS or DGPS.

The small target detection based on maximum likelihood estimation and spot detection operator

In this paper, aiming at the small target detection problem in the infrared image sequence, we propose a small target detection method based on maximum likelihood estimation and NNLoG spot detection operator. Compared with the traditional method, our proposed method can partially solve the nonlinear motion of the small target in image sequence. The real target trajectory is approximated by polynomial to enhance the signal to noise ratio of target. To validate the proposed method, we create eight experiments to simulate. The experiment result shows that our method is very valuable for small target detection.

Full-parameter vision navigation based on scene matching for aircrafts

Vision navigation based on scene matching between real-time images and a reference image has many advantages over the commonly used inertial navigation system (INS), such as no cumulative measurement errors for long-endurance flight. Recent developments in vision navigation are mainly used for partial navigation parameters measurements, such as the position and the relative velocity, which cannot meet the requirements of completely autonomous navigation. We present the concept, principle and method of full-parameter vision navigation (FPVN) based on scene matching. The proposed method can obtain the three-dimensional (3D) position and attitude angles of an aircraft by the scene matching for multiple feature points instead of a single point in existing vision navigations. Thus, FPVN can achieve the geodetic position coordinates and attitude angles of the aircraft and then the velocity vector, attitude angular velocity and acceleration can be derived by the time differentials, which provide a full set of navigation parameters for aircrafts and unmanned aerial vehicles (UAVs). The method can be combined with the INS and the cumulative errors of the INS can be corrected using the measurements of FPVN rather than satellite navigation systems. The approach provides a completely autonomous and accurate navigation method for long-endurance flight without the help of satellites.

Change detection by local illumination compensation using local binary pattern

Abstract. Approaches designed thus far for illumination invariant change detection are generally based on illumination compensation (IC) or illumination invariant descriptors. However, IC cannot handle local illumination variation, and is prone to sacrifice discriminability; illumination invariant descriptors cannot work very robustly in a textureless scenario. To address these problems, we present a novel change detection method by combining local binary pattern (LBP) with local illumination compensation (LIC). Although both LBP and LIC have disadvantages themselves, their independence enables mutual compensation of their disadvantages. Through a reasonable compositional strategy that makes best use of the advantages and bypasses the disadvantages, the proposed method can efficiently handle not only global but also local illumination variation in both textured and texture-less scenario. Experimental results using many real and synthetic images clearly justify our method.

Camera series and parallel networks for deformation measurements of large scale structures

Videometrics is a technique for measuring displacement, deformation and motion with features of precision, multifunctional, automation and real time measurement etc. Videometrics with camera networks is a fast developed area for deformation measurements of large scale structures. Conventional camera network is parallel network where cameras are independent each other and the relations among the cameras are calibrated from their target images. In recent years, we proposed and developed two kinds of videometrics with camera series networks where cameras are connected each other in series and relations among the cameras can be relayed one by one for the deformation measurements of large and super large scale structures. In this paper, our research work in both the camera series and parallel networks for the deformation measurements of large scale structures are overviewed and some new development are introduced. First, our proposed methods of camera series networks are introduced, including the pose-relay videometrics with camera series and the displacement-relay videometrics with camera series. Then our work of large scale structure deformation measurement by camera parallel networks is overviewed. Videometrics with various types of camera networks has the broad prospect of undertaking automatic, long-term and continuous measurement for deformation in engineering projects such as wind turbine blades, ship, railroad beds, and bridges.

Detection of small targets against complex sky background based on bidirectional SVD decomposition temporal filters

The temporal profile of the small target in the sky background and the disadvantage of traditional method are analyzed. Then we propose a novel method of small target detection based on image sequence in complex sky background. The illumination intensity is normalized firstly in image sequence. Then, the temporal matrix is decomposed using SVD in both horizontal and vertical direction. The small target is extracted in these two directions. Then, add these two images. Finally, segment the image with threshold. Obtain the trajectory of the small target in image sequence. The simulations show that our method is robust and effective.

Disturbance-rejecting method for cooperative object pose estimation from binocular images

A disturbance-rejecting method for measuring a cooperative objects pose from binocular images is presented. The presented method optimizes the parameters of the objects pose and modifies some measure system parameters simultaneously by bundle adjustment based on initial values. Experiments data show that the method converges quickly and stably, gives accurate results and dos not demand accurate initial values. Especially, when the measure system parameters are disturbed, this method still gives accurate results.