In order to solve the problem of low positioning accuracy of binocular vision system, a binocular vision target positioning method based on coarse-fine stereo matching was proposed. The coarse-fine matching strategy was adopted in the proposed method, firstly the random fern algorithm based on Canny-Harris feature points was used to identify the target in the left and right images at the stage of coarse matching, and the center points of target rectangular regions were extracted to achieve the center matching. Then, a binary feature descriptor based on image gradient information was established at the stage of fine matching, and the right center point obtained by center matching was used as an estimated value to set a pixel search range, in which the best matching point of left center point was found. Finally, the center matching points were substituted into the mathematical model of parallel binocular vision to achieve target positioning. The experimental results show that the proposed method has the positioning error controlled in 7 mm within 500 mm distance, and the average relative positioning error of 2.53%. Compared with other methods, the proposed method has the advantages of high positioning accuracy and short running time.

Since the lack of lesion labels and unbalanced data distribution in datasets lead to the problem that the supervised classification model can not effectively classify the lesions in the traditional Diabetic Retinopathy (DR) grading system, a Cost-Sensitive based Semi-supervised Bagging (CS-SemiBagging) algorithm for DR classification was proposed. Firstly, retinal vessels were removed from a fundus image, and then the suspicious red lesions (MicroAneurysms (MAs) and HEMorrhages (HEMs)) were detected on the image without vessels. Secondly, a 22-dimensional feature based on color, shape and texture was extracted to describe each candidate lesion region. Thirdly, a CS-SemiBagging model was constructed for the classification of MAs and HEMs. Finally, the severity of DR was graded into four levels based on the numbers of different lesions. The proposed method was evaluated on the publicly available MESSIDOR database. It achieved an average accuracy of 90.2%, which was 4.9 percentage points higher than that of classical semi-supervised learning method based on Co-training. The CS-SemiBagging algorithm can effectively classify DR without label information of the suspicious lesions, so as to avoid the time-consuming effort of labeling the lesions by specialists and the bad influence of unbalanced samples on the classification.

Since the limitation of inefficient use and unintelligent frequency selection of the HF (High Frequency) band, a method of HF cognitive frequency selection using Hidden Markov Model (HMM) was proposed. Applying cognitive radio principles to HF communications, HF legacy users were considered as primary users, and the HF radio using cognitive technologies were seen as the secondary user. Firstly, the HMM was established to predict channel states of HF legacy users based on the history data of spectrum sensing; secondly, channel parameters were estimated if the predicted state was idle; finally, the optimal frequency was selected among the channels whose predicted states were idle according to the estimated channel parameters. Simulation results show that the proposed method can be used to actually predict HF legacy users' channel states and quickly estimate channel parameters. Under the given simulation conditions, the successful transmission ratio of the proposed method is 5.54% and 10.56% higher than the methods of random channel selection using HMM prediction and energy detection, therefore the proposed method can select the optimal channel.

In order to describe different kinds of medical image more consistently and reduce the scale sensitivity, a classification model based on scale space multi-feature fusion was proposed according to the characteristics of medical image. First, scale space was built by difference of Gaussian, and then complementary features were extracted, such as gray-scale features, texture features, shape features, and features extracted in the frequency domain. In addition, maximum likelihood estimation was considered to realize decision level fusion. The scale space multi-feature fusion classification model was applied to medical image classification task following IRMA code. The experimental results show that compared with traditional methods, F1 value increased 5%-20%. Fusion classification model describes medical image more comprehensively, avoids the information loss from feature dimension reduction, improves classification accuracy, and has clinical value.

In order to improve the classification performance on the imbalanced data, a new Particle Swarm Optimization (PSO) based method was introduced. It optimized the re-sampling rate and selected the feature set simultaneously, with the imbalanced data evaluation metric as objective function through particle swarm optimization, so as to achieve the best data distribution. The proposed method was tested on a large number of UCI datasets and compared with the state-of-the-art methods. The experimental results show that the proposed method has substantial advantages over other methods; moreover, it proves that it can effectively improve the performance on the imbalanced data by optimizing the re-sampling rate and feature set simultaneously.

To solve low accuracy and efficiency issues on the large-scale data classification, an adaptive random subspace ensemble classification algorithm aided by the X-means clustering was proposed. X-means clustering was adopted to separate the original data space into multiple clusters automatically, maintaining the original data structure; moreover adaptive random subspace ensemble classifier enhanced diversity of the base components and determined the size of base classifiers automatically, so as to improve the robustness and accuracy. The experimental results show that the proposed method improves the traditional single and ensemble classifiers with respect to accuracy and robustness on the large scale datasets with high dimension. Furthermore, it improves the overall efficiency of the algorithm.