No abstract

No abstract

As the focal length of an optical lens in a conventional camera is limited, it is usually arduous to obtain an image in which each object is focused. This problem can be solved by multi-focus image fusion. In this paper, we propose an entirely new multi-focus image fusion method based on decision map and sparse representation (DMSR). First, we obtained a decision map by analyzing low-scale images with sparse representation, measuring the effective clarity level, and using spatial frequency methods to process uncertain areas. Subsequently, the transitional area around the focus boundary was determined by the decision map, and we implemented the transitional area fusion based on sparse representation. The experimental results show that the proposed method is superior to the other five fusion methods, both in terms of visual effect and quantitative evaluation.

Abstract. Objective: Solve the problem that was the low extract accuracy of existing target extraction algorithm. Methods: Proposes an adaptive object extraction algorithm based on energy functional. Article algorithm was defined the average item and the contour curve for the energy functional, which combining with the probability graph and differential geometry. At final, we would be getting the adaptive energy functional. Results: Article algorithm can effectively extract the target contour. And it is not affected by the background signal. Conclusion: From the contour detection experiments and algorithms comparative experiments, we can see that the algorithm can accurately extract the desired signal. Simultaneously, it can maintain a higher visual for the target.

In this paper,we introduce a new tracking algorithm to detect and track multiple Vehicles in highway or special area. For the traditional vehicle tracking algorithm that can not be real-time tracking of vehicles or appear to tracking chaotic situation when most of the scene is in motion and component parts of vehicles are occluded. To overcome this problem, we use a background image separation method to separate the moving vehicle and the background and introduce a QPSO clustering algorithm for gathering together color particles of vehicles moving target,finally we through the particle position matching template to achieve target matching tracking.In an experimental evaluation, we show that algorithm through processing the monitoring image of moving vehicles to show tracking information of moving vehicle in the real-time.