An image can be divided into foreground part and background part, while the foreground is often the visual center. Due to the large categories and complex situations of foreground part, the image colorization is difficult, thus the foreground part of an image may suffer from poor colorization and detail loss problems. To solve these problems, an image colorization algorithm based on foreground semantic information was proposed to improve the image colorization effect and achieve the purpose of natural overall image color and rich content color. First, the foreground network was used to extract the low-level features and high-level features of the foreground part. Then these features were integrated into the foreground subnetwork to eliminate the influence of background color information and emphasize the foreground color information. Finally, the network was continuously optimized by the generation loss and pixel-level color loss, so as to guide the generation of high-quality images. Experimental results show that after introducing the foreground semantic information, the proposed algorithm improves Peak Signal-to-Noise Ratio (PSNR) and Learned Perceptual Image Patch Similarity (LPIPS), effectively solving the problems of dull color, detail loss and low contrast in the colorization of the central visual regions; compared with other algorithms, the proposed algorithm achieves a more natural colorization effect on the overall image and a significant improvement on the content part.

In bamboo strip surface defect detection, the bamboo strip defects have different shapes and messy imaging environment, and the existing target detection model based on Convolutional Neural Network (CNN) does not take advantage of the neural network when facing such specific data; moreover, the sources of bamboo strips are complicated and there exist other limited conditions, so that it is impossible to collect all types of data, resulting in a small amount of bamboo strip defect data that CNN cannot fully learn. To address these problems, a special detection network aiming at bamboo strip defects was proposed. The basic framework of the proposed network is CenterNet. In order to improve the detection performance of CenterNet in less bamboo strip defect data, an auxiliary detection module based on training from scratch was designed:when the network started training, the CenterNet part that uses the pre-training model was frozen, and the auxiliary detection module was trained from scratch according to the defect characteristics of the bamboo strips; when the loss of the auxiliary detection module stabilized, the module was intergrated with the pre-trained main part by a connection method of attention mechanism. The proposed detection network was trained and tested on the same training sets with CenterNet and YOLO v3 which is currently commonly used in industrial detection. Experimental results show that on the bamboo strip defect detection dataset, the mean Average Precision (mAP) of the proposed method is 16.45 and 9.96 percentage points higher than those of YOLO v3 and CenterNet, respectively. The proposed method can effectively detect the different shaped defects of bamboo strips without increasing too much time consumption, and has a good effect in actual industrial applications.

Motion blur has an extremely negative effect on video frame interpolation. In order to handle this problem, a novel blurred video frame interpolation method was proposed. Firstly, a multi-task fusion convolutional neural network was proposed, which consists of a deblurring module and a frame interpolation module. In the deblurring module, based on the deep Convolutional Neural Network (CNN) with stack of ResBlocks, motion blur removal of two input frames was implemented by extracting and learning the deep blur features. And the frame interpolation module was used to estimate voxel flow between two consecutive frames after blur removal, then the obtained voxel flow was used to guide the trilinear interpolation of the pixels to synthesize the intermediate frame. Secondly, a large blurred video simulation dataset was made, and a “first separate and then combine” “from coarse to fine” training strategy was proposed, experimental results show that this strategy promotes the effective convergence of the multi-task fusion network. Finally, compared with the simple combination of the state-of-the-art deblurring and frame interpolation algorithms, experimental metrics show that the intermediate frame synthesized by the proposed method has the peak-to-noise ratio increased by 1.41 dB, the structural similarity improved by 0.020, and the interpolation error decreased by 1.99， at least. Visual comparison and reconstructed sequences show that the proposed model performs good frame rate up conversion effect for blurred videos, in other words, two blurred consecutive frames can be reconstructed end-to-end to three sharp and visually smooth frames by the model.

The existing optical flow estimation methods, which are frequently used in video quality enhancement and super-resolution reconstruction tasks, can only estimate the linear motion between pixels. In order to solve this problem, a new multi-frame compression artifact removal network architecture was proposed. The network consisted of motion compensation module and compression artifact removal module. With the traditional optical flow estimation algorithms replaced with the adaptive separable convolution, the motion compensation module was able to handle with the curvilinear motion between pixels, which was not able to be well solved by optical flow methods. For each video frame, a corresponding convolutional kernel was generated by the motion compensation module based on the image structure and the local displacement of pixels. After that, motion offsets were estimated and pixels were compensated in the next frame by means of local convolution. The obtained compensated frame and the original next frame were combined together as input for the compression artifact removal module. By fusing different pixel information of the two frames, the compression artifacts of the original frame were removed. Compared with the state-of-the-art Multi-Frame Quality Enhancement (MFQE) algorithm on the same training and testing datasets, the proposed network has the improvement of Peak Signal-to-Noise Ratio (Δ PSNR) increased by 0.44 dB at most and 0.32 dB on average. The experimental results demonstrate that the proposed network performs well in removing video compression artifacts.

Aiming at the deep structure and high computational complexity of current network models based on deep learning for super-resolution image reconstruction, as well as the problem that the networks can not operate effectively on resource-constrained devices caused by the high storage space requirement for the network models, a super-resolution convolutional neural network compression method based on knowledge distillation was proposed. This method utilizes a teacher network with large parameters and good reconstruction effect as well as a student network with few parameters and poor reconstruction effect. Firstly the teacher network was trained; then knowledge distillation method was used to transfer knowledge from teacher network to student network; finally the reconstruction effect of the student network was improved without changing the network structure and the parameters of the student network. The Peak Signal-to-Noise Ratio (PSNR) was used to evaluate the quality of reconstruction in the experiments. Compared to the student network without knowledge distillation method, the student network using the knowledge distillation method has the PSNR increased by 0.53 dB, 0.37 dB, 0.24 dB and 0.45 dB respectively on four public test sets when the magnification times is 3. Without changing the structure of student network, the proposed method significantly improves the super-resolution reconstruction effect of the student network.

Minimally invasive endoscopic surgery always remains a challenge due to the complexity of the anatomical location and the limitations of endoscopic vision. An Augmented Reality (AR) navigation system was designed for simulation of pelvic minimally invasive surgery. Firstly, a 3D model of pelvis which was segmented and reconstructed from the preoperative CT (Computed Tomography) was textured mapping with the real pelvic surgical video, and then a surgical video with the ground truth pose was simulated. The blank model was initially registered with the intraoperative video by a 2D/3D registration based on color consistency of visible surface points. After that, an accurate tracking of intraoperative endoscopy was performed using a stereoscopic tracking algorithm. According to the multi-DOFs (Degree Of Freedoms) transformation matrix of endoscopy, the preoperative 3D model could then be fused to the intraoperative vision to achieve an AR navigation. The experimental results show that the root mean square error of the estimated trajectory compared to the ground truth is 2.3933 mm, which reveals that the system can achieve a good AR display for visual navigation.

Concerning the problem of traditional Particle Swarm Optimization (PSO) such as slow convergence and being easy falling into local extremum, a Dynamic self-Adaptive Chaotic Quantum-behaved PSO (DACQPSO) was proposed by studying the relationship between population diversity and the evolution of PSO. The population-distribution-entropy was introduced into the evolutionary control of the particle swarm in this algorithm. Based on the Sigmoid function model, the method of calculating the contraction-expansion coefficient of the Quantum-behaved PSO (QPSO) was given. The average-distance-amongst-points was taken as the criterion of chaotic search to carry out a chaotic perturbation. The DACQPSO algorithm was applied to the coverage optimization of Wireless Sensor Network (WSN), and the simulation analysis was carried out. Experimental results show that compared with Standard PSO (SPSO), QPSO and Chaotic Quantum-behaved PSO (CQPSO), the DACQPSO algorithm improves the coverage rate by 3.3501%, 2.6502% and 1.9000% respectively. DACQPSO algorithm improves the coverage performance of WSN, and has better coverage optimization effect than other algorithms.

The Alzheimer's Disease (AD) usually leads to atrophy of hippocampus region. According to the characteristic, a Convolutional Neural Network (CNN) based method was proposed for the diagnosis of AD by using the hippocampu region in brain Magnetic Resonance Imaging (MRI). All the test data were got from the ADNI database including 188 AD and 229 Normal Control (NC). Firstly, all the brain MRI were preprocessed by skull stripping and aligned to a template space. Secondly, a linear regression model was used for age correction of brain aging atrophy. Then, after preprocessing, multiple 2.5D images were extracted from the hippocampus region in the 3D brain image for each object. Finally, the CNN was used to train and recognize the extracted 2.5D images, and the recognition results of the same object were used for the joint diagnosis of AD. The experiments were carried out by using multiple ten-fold cross validation methods. The experimental results show that the average recognition accuracy of the proposed method reaches 88.02%. The comparison results show that, compared with Stacked Auto-Encoder (SAE) method, the proposed method has improved the diagnosis effect of AD in the case of only using MRI.

For the failure of similar multi-target tracking with monocular vision caused by influence factors such as occlusion, a multi-target tracking algorithm based on improved online Hough forest tracking framework was proposed. Based on that, the tracking problem could be formulated as a detection-based trajectories association process, and the association calculation was formulated as a Maximum A Posteriori (MAP) problem with online learning Hough forest framework. Through online multi-objective samples collection and appearance and motion information extraction, a Hough forest was constructed to associate multi-target trajectories by training for track association probability. Low-rank approximation Hankel Matrix was employed to correct the trajectories, which modified associated errors and improved the efficiency of online update of the training set. Experimental results show that the trajectory miss match ratio is significantly decreased by the proposed method, and tracking accuracy and robustness of the monocular vision are effectively improved for similar or inter-occlusion targets.

When civil aviation passengers check in, various uncertainty problems exist in the baggage tag readers' number, position and angle. To solve the problems, the Dynamic Population-Double Fitness Particle Swarm Optimization (DPDF-PSO) algorithm was proposed. Firstly, the mathematical model of baggage tag detector was established, then it was transformed into an optimization problem; secondly, the optimization problem was solved by standard Particle Swarm Optimization (PSO) algorithm; finally, the standard PSO algorithm was improved in accordance with the model features. The simulation results show that compared with standard PSO algorithm, the simulation time of the DPDF-PSO algorithm reduced by 23.6%, the objective function value increased by 3.7%. DPDF-PSO algorithm overcomes the shortage of long simulation time and troublesome problem of optimal boundary solutions existed in standard PSO algorithm. Identity information can be read quickly and accurately by readers layout at a lower cost.

A hybrid control method based on backstepping and fuzzy adaptive Proportion-Integration-Differentiation (PID) was proposed, which improved the flight stability of quadrotor aircraft in different environment. The method selected the current appropriate controller according to the Unmanned Aerial Vehicles (UAV) flight environment, large attitude angle, and large attitude angular velocity. In the case of the system undisturbed, backstepping-based control algorithm could complete trajectory tracking. In case of disturbance, fuzzy adaptive PID could greatly suppress the impact of disturbance and realize the precise control of quadrotor aircraft. The Matlab simulation analysis and practical experiments illustrate that the stability augmentation hybrid controller can effectively realize the stability.

An Improved Genetic Algorithm (IGA) was proposed for the inverse kinematics problem solution of upper limb kinematic chain which had high degree of freedom and was too complex to be solved by using geometric, algebraic, and iterative methods. First, the joint-units of upper limb kinematic chain and its mathematical modeling were constructed by using Denavit-Hartenberg (D-H) method, then population diversity and initialization were completed based on simulating human being population, and the adaptive operators for crossover and mutation were designed. The simulation results show that the IGA can search the high precise solutions and avoid prematurity convergence or inefficient searching in later stage with larger probability than standard genetic algorithm.

This paper proposed a new decomposition scheme for lossless image compression by incorporating edge-directed adaptive prediction with wavelet lifting scheme. A vertical one-Dimension Discrete Wavelet Transform (1D-DWT) was first applied to images by means of lifting scheme. Second, edge-directed adaptive prediction procedure was applied to those high-frequency sub-band coefficients generated by the previous DWT. And then, a similar horizontal decomposition was performed in the low-frequency sub-band generated by vertical decomposition. A multi-resolution representation was thus acquired by an iterative repetition at the produced low-resolution approximation. Unlike the well-known coder CALIC and JPEG-LS, this scheme can provide a resolution scalable code-stream due to DWT. In addition, the experimental results indicate, due to the edge-directed prediction, this decomposition scheme has achieved noticeably better performance of lossless compression than JPEG2000 which supports resolution scalability.

There are many defects in the existing assembly modeling method such as large information redundancy or complex modeling process when descripting the disassembly process of virtual maintenance training system. A modeling method for virtual maintenances disassembly process was proposed in the paper based on researching the existing assembly modeling methods and analyzing the different between assembly process and disassembly process. In this method, the matrix was used to describe the assembly model and the matrix calculation was used to describe the partsdisassembly process, so the modeling process was simplified and the models data was reduced. It is a new idea and method for constructing the virtual maintenance training system.

Concerning the breakage warning problem of grid fence, a grid breakage detection algorithm based on improved Harris corner was proposed. As for traditional Harris corner extraction algorithm, the first derivative in vertical and horizontal direction and the corner response function value need to be calculated, and it must be done for each image pixel, which makes this method time-consuming. Therefore, the parameter of gray "similarity" was introduced to demonstrate the gray similarity between pixel and its ambient pixels, through which to filter pseudo corners, reducing the Harris corner extraction time. Then after analyzing the corners' distribution, the breakage areas can be locked. Breakage detection experiments were taken to various fence images taken by robot, and the results indicate that the Harris corner extraction time decreases largely, and the proposed algorithm is effective, meeting the practical application requirements of fence breakage detection.