In the natural scenario, it is difficult to extract a complete outline of the object because of background textures, light and occlusion. Therefore an object recognition method based on local contour feature was proposed. Local contour feature of this paper formed by chains of 2-adjacent straight and curve contour segments (2AS). First, the angle of the adjacent segments, the segment length and the bending strength were analyzed, and the semantic model of the 2AS contour feature was defined. Then on the basis of the relative position relation between object's 2AS features, the 2AS mutual relation model was defined. Second, the 2AS semantic model of the object template primarily matched with the 2AS features of the test image, then 2AS mutual relation model of object template accurately matched with the 2AS features of the test image. At last, the pairs of 2AS of detected local contour features were obtained and repeatedly grouped, then grouped objects were verified according to the 2AS mutual relation model of object template. The contrast experiment with the 2AS feature algorithm with similar straight-line chains, the proposed algorithm has higher accuracy, low false positive rate and miss rate in the recognition of grading ring, then the method can more effectively recognize the grading ring.

In order to improve the efficiency of power transmission line inspection by Unmanned Aerial Vehicle (UAV), a new method was proposed for detecting broken transmission lines and defects of foreign body based on the perception of line structure. The transmission line image acquired by UAV was easily influenced by the background texture and light, the gradient operators of horizontal and vertical direction which can be used to detect the line width were used to extract line objects in the inspection image. The study on calculation of gestalt perception of similarity, continuity and colinearity connected the intermittent wires into continuous wires. Then the parallel wire groups were further determined through the calculation of parallel relationship between wires. In order to reduce the detection error rate, spacers and stockbridge dampers of wires were recognized based on a local contour feature. Finally, the width change and gray similarity of segmented conductor wire were calculated to detect the broken part of wire and foreign object defect. The experimental results show that the proposed method can detect broken wire strand and foreign object defect efficiently under complicated backgrounds from the transmission line of UAV images.

For the image processing, computer vision and 3D rendering have the feature of massive parallel processing, the programmability and the flexible mode of parallel processing on the Polymorphic Array Architecture for Graphics (PAAG) platform were utilized adequately, the parallelism design method by combing the operation level parallelism with data level parallelism was used to implement the OpenVX Kernel functions and 3D rendering pipelines. The experimental results indicate that in the parallel implementation of image processing of OpenVX Kernel functions and graphics rendering, using Multiple Instruction Multiple Data (MIMD) of PAAG in parallel processing can obtain a linear speedup that the slope equals to 1, which achieves higher efficiency than the slope as nonlinear speedup that less than 1 of Single Instruction Multiple Data (SIMD) in traditional parallel processing of the Graphics Processing Unit (GPU).

To solve the problem of detecting highway-vehicle abnormal behavior such as retrograde motion, parking and abnormal trajectory, this paper presented a bottom-up detection algorithm based on lane model. First, the lane line and vanishing point were found out by line's continuity and collinearity, and the lane model was automatically established. Second, a region-overlap graph was established by motion prediction and KLT feature tracking to indicate region relationship of the object in the detecting and tracking process. In this graph, the corresponding relationship and reliable trajectory was made by merging or splitting the target region. The target region was ruled by posterior relationship. At last, the vehicle's location was transformed based on vanishing point. The trend of target motion was judged and its location or velocity was calculated in the lane model with sliding window to decide vehicle's behavior. The experimental results show that the proposed algorithm has more than 80% detection rate for car incident in different weather or traffic environment. This algorithm is capable to detect vehicle abnormal behavior on highway for real-time application.

To improve the prediction ability of image memorability, a method for automatically predicting the memorability of an image was proposed by using visual saliency entropy and improved Object Bank feature. The proposed method improved the traditional Object Bank feature and extracted the visual saliency entropy feature. Then a prediction model of image memorability was constructed by using Support Vector Regression (SVR). The experimental results show that the correlation coefficiency of the proposed method is three percentage higher than the state-of-the-art method. The proposed model can be used in image memorability prediction, image retrieval ranking and advertisement assessment analysis.

In order to overcome the problems of low convergence precision and easily relapsing into local extremum in basic Fruit Fly Optimization Algorithm(FOA), by introducing the chaos algorithm into the evolutionary process of basic FOA, an improved FOA called Adaptive Chaos FOA (ACFOA)is proposed. In the condition of local convergence, chaos algorithm is applied to search the global optimum in the outside space of convergent area and to jump out of local extremum and continue to optimize. Experimental results show that the new algorithm has the advantages of better global searching ability, speeder convergence and more precise convergence.

In this paper, attraction and exclusion operations of bacterial chemotaxis were introduced into original Fruit Fly Optimization Algorithm (FOA), and FOA based on Bacterial Chemotaxis (BCFOA) was proposed. Exclusion (to escape the worst individual) or attraction (to be attracted by the best individual) was decided to perform by judging the fitness variance is zero or no, so that the problem of premature convergence caused by the loss of population diversity, which resulted from the fact that individuals only were attracted by the best one in FOA, was solved. The experimental results show that the new algorithm has the advantages of better global searching ability, and faster and more precise convergence.