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Based on OpenGL, this paper presented a local 3D scene reconstruction from the frame buffers (including color buffer and depth buffer) after 3D scene rendered. According to projection principles and depth buffer characteristics, it discussed an inverse prooection processing and obtained theoretically a 3D vertex reconstruction method. As a result, it transformed a l valid pixels within frame buffers and generated a 3D vertex set which was a point cloudy model in the camera space in the conducted experiments. Obviously, this cloudy model was a kind of a sample of the original 3D scene in current camera system and named as virtual vision model(VVM). Secondly, it analyzed the point-wise precision of VVM detailed. The results of experiments show that VVM appears very high relative accuracy to the original 3D scene. Above all, the reconstruction method appears natural multi-resolution feature with respect to the resolution of frame buffers and supports scene simplification and streaming transmission. On the other hand, this method is capable of abstracting VVM real-time from the common 3D software easily without any details of 3D scene known previously, which provides a new approach acquiring 3D data efficiently. [ABSTRACT FROM AUTHOR]

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Based on the multi-dimensional unified geometric algebra, this paper discussed the multi-vector expression and computation of multi-dimensional vector filed. The vector differentiations are computed by template convolution. With the help of the unified expression of inner and outer product of geometric product, the unified calculation method of divergence and curl and the implementation algorithm are proposed. This method can not only reproduce the geometric association between vector field divergence and rotation parameters as well as their differential geometry characteristics, but also achieve a unified and multi-dimensional coordinate-free calculation. Simulation experimental results based on the wind fields of North American suggest that our method has much better performance of contrast and smoothness against the gradient-based algorithm. It also has strong ability to deal with vector fields of high noise and provide new insights and method foundations for vector field data analysis. [ABSTRACT FROM AUTHOR]

Background/aims: To evaluate the safety and efficacy of high intensity focused ultrasound (HIFU) therapy in patients with local advanced pancreatic cancer., Methodology: 39 patients with local advanced pancreatic cancer were treated with HIFU, including 26 male and 13 female patients. The locations of the tumours were as follows: head of pancreas in 7 patients, body and/or tail of pancreas in 32 patients. Pain relief, time to progression (TTP), median survival and complications were analysed after HIFU treatment., Results: There were no severe complications or adverse events related to HIFU therapy in any of the patients treated. Pain relief was achieved in 79.5% of patients. Median TTP was 5.0 months. The median overall survival time was 11 months. 6-month and 1-year survival rate for patients were 82.1% and 30.8% respectively., Conclusions: Although this study may have limitations, preliminary results demonstrate the safetyof clinical application of HIFU for pancreatic cancer and reveal it to be a promising mode of treatment for local advanced pancreatic cancers.

Due to the high data dimensionality of a hyperspectral image, dimensionality reduction algorithm has attracted much attention in hyperspectral image analysis. Band selection algorithm, which selects appropriate bands from the original set of spectral bands, can preserve original information from the data and is useful for image classification and recognition. In the present paper, a novel band selection algorithm based on orthogonal projection divergence (OPD) is proposed, it aims to discriminate the interesting objects from background and noise information, maximize the spectral similarity between different spectral vectors by projecting the original data to feature space. Two HYDICE Washington DC Mall images and an HYMAP Purdue campus image data were experimented, and support vector machine (SVM) classifier was used for classification. The selected band number varies from 5 to 40 in order to study the impacts of different band selection algorithms on different features. For the computation complex, the sequential floating forward search (SFFS) was used to get the appropriate bands. The experiments have proved that our proposed OPD algorithm can outperform other traditional band selection methods such as SAM, ED, SID, and LCMV-BCC for hyperspectral image analysis. It is proven that OPD band selection is effective and robust in hyperspectral remote sensing dimensionality reduction