Concerning the problems of slow convergence and low accuracy of software quality prediction model based on Back Propagation Neural Network （BPNN）， a Software Quality Prediction method based on BPNN optimized by Ant Colony Optimization algorithm （SQP-ACO-BPNN） was proposed. Firstly， the software quality evaluation factors were selected and a software quality evaluation system was determined. Secondly， BPNN was adopted to build initial software quality prediction model and ACO algorithm was used to determine network structures， initial connection weights and thresholds of network. Then， an evaluation function was given to select the best structure， initial connection weights and thresholds of the network. Finally， the network was trained by BP algorithm， and the final software quality prediction model was obtained. Experimental results of predicting the quality of airborne embedded software show that the accuracy， precision， recall and F1 value of the optimized BPNN model are all improved with faster convergence， which indicates the validity of SQP-ACO-BPNN.

The method based on Multiple Instance Learning (MIL) can alleviate the drift problem to a certain extend. However, MIL method has relatively poor performance in running efficiency and accuracy, because the update strategy efficiency of the strong classifiers is low, and the update speed of the classifiers is not same with the appearance change speed of the targets. To solve this problem, a new update strategy for strong classifier was proposed to improve the running efficiency of MIL method. In addition, to improve the tracking accuracy of the MIL method, a new dynamic mechanisim for learning rate renewal of the classifier was given to make the updated classifier would more conform to the appearance of the target. The experimental results on comparison with MIL method and the Weighted Multiple Instance Learning (WMIL) method show that, the proposed method has the best performance in running efficiency and accuracy among the three methods, and has an advantage over tracking when there is no similar interference objects to target objects in background.

To solve the matching problem between the model and 3D object in the scenes, this paper presented a 3D object matching method combined 3D shape and 2D texture feature. Scale-Invariant Feature Transform (SIFT) feature was extracted from the range image in the scene, and then the range image matched with a series of 2.5 dimensional range images which were used for the 3D model reconstruction one by one based on SIFT algorithm, so that it could find out the most similar local range image to the object in the scene.The matching between this local range image and the object was completed through 3D shape feature. It is to initialize the model, in other words, it is to reset the model close to the object in the scene. At last, a Iterative Closest Point (ICP) algorithm combined with color was used to implement the matching between the object in the sences and the model which was reset before. In this way the pose of the object in the scene can be calculated accurately. The experimental results verify the feasibility and accuracy of the proposed method.