There are various saliency detection methods have been proposed recent years. These methods can often complement each other so combining them in appropriate way will be an effective solution of saliency analysis. Existing aggregation methods assigned weights to each entire saliency map, ignoring that features perform differently in certain parts of an image and their gaps between distinguishing the foreground from the backgrounds. In this work, we present a Bayesian probability based framework for multi-feature aggregation. We address saliency detection as a two-class classification problem. Saliency maps generated from each feature have been decomposed into pixels. By the statistic results of different saliency value’s reliability on foreground and background detection, we can generate an accurate, uniform and per-pixel saliency mask without any manual set parameters. This approach can significantly suppress feature’s misclassification while preserve their sensitivity on foreground or background. Experiment on public saliency benchmarks show that our method achieves equal or better results than all state-of-the-art approaches. A new dataset contains 1500 images with human labeled ground truth is also constructed.
Iris recognition is the most reliable method in personal identification. However, the current fixed-focus iris imaging system has small depth of field (DOF), which limits the wide application of the iris recognition system. This paper presents the design method and optimization of a phase mask based iris imaging system. Through wavefront coding, it can extend the DOF and enhance the convenience of iris image acquisition. Through analyzing the modulation transfer function and optical parameters of the cubic phase mask, we can get the wavefront coding iris imaging system’s optimal parameter and it’s structure. Experimental results show that the cubic phase mask based iris imaging system has larger DOF and better imaging performance.
The acquisition of the spatial data is a fundamental problem in multi-dimensional and dynamic GIS construction and infrastructure. Ground-based mobile Laser scanning System, which is mainly used in the reconstruction of 3D city and acquisition of local region geographic information, has an important function in rebuilding 3D spatial object. This integrated systems have the same sensors GPS/INS for positioning. In this paper, our researches are focused on multi-sensor integration without GPS/DGPS/INS. The application system we developed is a ground-based motive platform, upon which multi-sensors were integrated. In system, a relative positioning sensor we employed is the Rotary Encoder, which determines the relative positions of the platform from original position and the Laser Scanner’s posture. The Laser Scanner surveys the distances between the platform and the object. All data were transferred through wireless cable into the server located in the office. The wireless modem we applied provides reliable wireless data communication for either point-to-point or multipoint applications. In this paper the outline of system, principles and algorithm are presented. Moreover, some trials and experiences are presented in this paper. Finally some conclusions and further research work are introduced.