Traditional image object detection algorithm based on local feature is sensitive to rotation and occlusion; meanwhile, it also obtains low detection precision and speed in many cases. In order to improve the performance of this algorithm, a new image objects detection method applying objects’ local feature to sparse representation theory was introduced. Employing supervised random tree method to learn local features of sample images, a dictionary could be formed. The combination of sub-image blocks of test image and well trained dictionary in first stage could predict the location of the object in the test image, in this way it could obtain a sparse representation of the test image as well as the object detection goal. The experimental results demonstrate that the proposed method achieves robust detection results in rotation, occlusion condition and intricate background. What’s more, the method obtains higher detection precision and speed.

To solve the low efficiency and precision of the traditional methods, a near-duplicate image detection algorithm based on Bag-of-words and Hash coding was proposed in this paper. Firstly, a 500-dimensional feature vector was used to represent an image by Bag-of-words; secondly, feature dimension was reduced by Principal Component Analysis (PCA) and Scale-Invariant Feature Transform (SIFT) and features were encoded by Hash coding; finally, dynamic distance metric was used to detect near-duplicate images. The experimental results show that the algorithm based on Bag-of-words and Hash coding is feasible in detecting near-duplicate images. This algorithm can achieve a good balance between precision and recall rate: the precision rate can reach 90%-95%, and entire recall rate can reach 70%-80%.

In order to solve the problems that scene matching cannot be applied to navigation and Inertial Navigation System (INS) pose information cannot be effectively used, a new approach based on the digital terrain elevation model was proposed to estimate the absolute position and pose for aerial vehicle. Firstly, the stereo pairs were captured by the airborne camera system and the information of flight speed was acquired by sensors; secondly, the terrain information under the vehicle was obtained by modifying the dual camera motion model; finally, a method used to match the on-board terrain elevation model data based on the rigid-body constraint of the 3D reconstruction results was proposed, which was used to estimate the position and pose of the vehicle in the world coordinate system. The simulation results demonstrate that the improved dual camera motion model achieves better precision, making the estimation of position and pose in the world coordinate system more effective and efficient.

A designed goal for the general testing system on the basis of classified the network equipment testing was put forword.Through analyzed this goal,several key problems to be settled were proposed,and then answers to each question was put forward.After combined these answers,the final designed model for the testing system was proposed.At last,test items that could be tested in this system were introduced.After the practical testing,a comprehensive assessment was given.