高分辨X-RAY成像系统的搭建及其血管造影研究

本实验目的在于初步探讨血管造影技术在评定动物血管疾病模型中的应用与研究。方法是搭建X-RAY血管造影系统,测定体模,验证X射线焦斑分辨率。建立大鼠后肢缺血模型,利用搭建好X-RAY的血管造影系统检验动物模型。体模测定结果显示,该系统可以观察到10μm的线对板条纹,动物在体造影检查,可清楚地观察到血管的结构,发现缺血模型的血管阻塞部位,以及阻塞部位上游血管的侧支循环的建立。利用高分辨X-RAY,可以进行小动物的血管造影检查,该系统在小动物的血管研究中将发挥重要作用。

Snowline and Snow Cover Monitoring at High Spatial Resolution in a Mountainous River Basin Based on a Timelapse Camera at a Daily Scale

Snowline change and snow cover distribution patterns are still poorly understood in steep alpine basins of the Qilian Mountainous region because fast changes in snow cover cannot be observed by current sensing methods due to their short time scale. To address this issue of daily snowline and snow cover observations, a ground- based EOS 7D camera and four infrared digital hunting video cameras （LTL5210A） were installed around the Hulugou river basin （HRB） in the Qilian Mountains along northeastern margin of the Tibetan Plateau （38°15′54″N, 99°52′53″E） in September 2011. Pictures taken with the EOS 7D camera were georeferenced and the data from four LIL521oA cameras and snow depth sensors were used to assist snow cover estimation. The results showed that the time-lapse photography can be very useful and precise for monitoring snowline and snow cover in mountainous regions. The snowline and snow cover evolution at this basin can be precisely captured at daily scale. In HRB snow cover is mainly established after October, and the maximum snow cover appeared during February and March. The consistent rise of the snowline and decrease in snow cover appeared after middle part of March. This melt process is strongly associated with air temperature increase.

Coherence-coefficient-based Markov random field approach for building segmentation from high-resolution SAR images

Building segmentation from high-resolution synthetic aperture radar (SAR) images has always been one of the important research issues. Due to the existence of speckle noise and multipath effect, the pixel values change drastically, causing the large intensity differences in pixels of building areas. Moreover, the geometric structure of buildings can cause strong scattering spots, which brings difficulties to the segmentation and extraction of buildings. To solve of these problems, this paper presents a coherence-coefficient-based Markov random field (CCMRF) approach for building segmentation from high-resolution SAR images. The method introduces the coherence coefficient of interferometric synthetic aperture radar (InSAR) into the neighborhood energy based on traditional Markov random field (MRF), which makes interferometric and spatial contextual information more fully used in SAR image segmentation. According to the Hammersley-Clifford theorem, the problem of maximum a posteriori (MAP) for image segmentation is transformed into the solution of minimizing the sum of likelihood energy and neighborhood energy. Finally, the iterative condition model (ICM) is used to find the optimal solution. The experimental results demonstrate that the proposed method can segment SAR building effectively and obtain more accurate results than the traditional MRF method and K-means clustering.

The Use of Drones in Forestry

Recently, drones have found applicability in a variety of study fields, one of these being forestry, where an increasing interest is given to this segment of technology, especially due to the high-resolution data that can be collected flexibly in a short time and at a relatively low price. Also, drones have an important role in filling the gaps of common data collected using manned aircraft or satellite remote sensing, while having many advantages both in research and in various practical applications particularly in forestry as well as in land use in general. This paper aims to briefly describe the different approaches of applications of UAVs （Unmanned Aircraft Vehicles） in forestry, such as forest mapping, forest management planning, canopy height model creation or mapping forest gaps. These approaches have great potential in the near future applications and their quick implementation in a variety of situations is desirable for the sustainable management of forests.

A new approach for real time object detection and tracking on high resolution and multi-camera surveillance videos using GPU

High resolution cameras and multi camera systems are being used in areas of video surveillance like security of public places, traffic monitoring, and military and satellite imaging. This leads to a demand for computational algorithms for real time processing of high resolution videos. Motion detection and background separation play a vital role in capturing the object of interest in surveillance videos, but as we move towards high resolution cameras, the time-complexity of the algorithm increases and thus fails to be a part of real time systems. Parallel architecture provides a surpass platform to work efficiently with complex algorithmic solutions. In this work, a method was proposed for identifying the moving objects perfectly in the videos using adaptive background making, motion detection and object estimation. The pre-processing part includes an adaptive block background making model and a dynamically adaptive thresholding technique to estimate the moving objects. The post processing includes a competent parallel connected component labelling algorithm to estimate perfectly the objects of interest. New parallel processing strategies are developed on each stage of the algorithm to reduce the time-complexity of the system. This algorithm has achieved a average speedup of 12.26 times for lower resolution video frames(320×240, 720×480, 1024×768) and 7.30 times for higher resolution video frames(1360×768, 1920×1080, 2560×1440) on GPU, which is superior to CPU processing. Also, this algorithm was tested by changing the number of threads in a thread block and the minimum execution time has been achieved for 16×16 thread block. And this algorithm was tested on a night sequence where the amount of light in the scene is very less and still the algorithm has given a significant speedup and accuracy in determining the object.

Clinical Features and Prognosis in 21 Patients with Extrinsic Allergic Alveolitis

Objective To assess the spectrum of causes, clinical features, differences between disease phases, and prognosis of extrinsic allergic alveolitis （EAA）. Methods Patients with EAA diagnosed at Peking Union Medical College Hospital from August 1983 to May 2007 were analyzed retrospectively. Their medical records were examined to gather clinical, laboratorial, radiological, and histopathological data. Patients were divided to three phases （acute, subacute, and chronic） according to clinical presentations. Follow-up data regarding treatment response, subsequent radio- logical and pulmonary function studies, and clinical outcomes were collected. Results A total of 21 cases were enrolled. Among them, 11 were subacute, 10 were chronic. The most common exposure was pet birds （6 cases, 28.6%）. The primary abnormality of pulmonary function was restriction and/or reduction in diffusing capacity （12 cases, 63.2%）. The most common findings on high-resolution computed tomography （HRCT） were ground-glass opacities （13 cases, 68.4%） and centri- lobular nodules （8 cases, 42.1%）. Airway obstruction in pulmonary function test, emphysema, hmg cysts, and fibrosis on HRCT were more frequently seen in chronic than in subacute patients, though the differences were not statistically significant. Bronchoalveolar lavage fluid （BALF） showed lymphocytosis. The total＇cell count and the percentage of neutrophils were significantly higher in subacute than in chronic patients （P〈0.05）. Nonnecrotizing granulomas were seen in 8 （47.1%） cases. Improvement or normalization in symptoms, radiography, and pulmonary function test after treatment were seen in all 18 patients with avail- able follow-up data. Five patients recurred. Conclusions The characteristic abnormalities of pulmonary function, findings on HRCT, and pa- thology are essential for all phases of EAA, and the atypical manifestations such as obstruction and fibrosis can also be present frequently, particularly in chronic cases. Differential cell counts of BALF are related to the phase of the disease. The treatment response and prognosis of EAA are good.

Fingerspelling Recognition by Hand Shape Using Higher-Order Local Auto-Correlation Features

The fingerspelling recognition by hand shape is an important step for developing a human-computer interaction system. A method of fingerspelling recognition by hand shape using HLAC （higher-order local auto-correlation） features is proposed. Furthermore, in order to use HLAC features more effectively, the use of image processing techniques： reducing an image resolution, dividing an image, and image pre-processing techniques, is also proposed. The experimental results show that the proposed method is promising.

CHEOS Major Special Project Promotes Development of Chinese Commercial Remote Sensing Cameras

High resolution remote sensing data has been applied in many fields such as national security, economic construction and in the daily life of the general public around the world, creating a huge market. Commercial remote sensing cameras have been developed vigorously throughout the world over the last few decades, resulting in resolutions down to 0.31 m. In 2010, the Chinese government approved the implementation of the China High-resolution Earth Observation System(CHEOS) Major Special Project, giving priority to development of high resolution remote sensing satellites. More than half of CHEOS has been constructed to date and 5 satellites operate in orbit. These cameras have different characteristics. A number of innovative technologies have been adopted, which have led to camera performance increasing in leaps and bounds. The products and the production capability enables the remote sensing technical level to increase making it on a par with Europe and the US.

Chang’E-2 satellite asymmetric-descent orbit control technology

To accomplish high-resolution imaging of the preselected landing area, it was necessary for the Chang＇E-2 mission to perform orbital maneuvering on the far side of the moon to meet the conditional height requirement of the imaging area. Engine shutdown would be executed invisibly on the back side of the moon if the descent maneuver mode opposite to the target perilune or the fuel optimal maneuver mode was used. To ensure the satellite safety, the project collectivety required that the engine shutdown should be designed to be executed in the domestic segmental arcs and meet the requirement of satellite emergency treatment simultaneously. Accordingly, the asymmetric-descent orbit control technology was adopted by offsetting the ma- neuver point, which obtained the orbit control parameters of finite-thrust mode with an iteration algorithm and modified the results with target perilune drift estimation. The Chang＇E-2 satellite declined to the target of 100 km×l5 km orbit successfully on 26 October 2010, and has been flying for 32 circles in the experimental orbit to accomplish the preselected landing area imaging. This paper describes the mechanism and realization method of the asymmetric-descent orbit control technology and evaluates the maneuver effect with the actual mission data.

A fast face detection architecture for auto-focus in smart-phones and digital cameras

Auto-focus is very important for capturing sharp human face centered images in digital and smart phone cameras. With the development of image sensor technology, these cameras support more and more highresolution images to be processed. Currently it is difficult to support fast auto-focus at low power consumption on high-resolution images. This work proposes an efficient architecture for an Ada Boost-based face-priority auto-focus. The architecture supports block-based integral image computation to improve the processing speed on high-resolution images; meanwhile, it is reconfigurable so that it enables the sub-window adaptive cascade classification, which greatly improves the processing speed and reduces power consumption. Experimental results show that 96% detection rate in average and 58 fps(frame per second) detection speed are achieved for the1080p(1920×1080) images. Compared with the state-of-the-art work, the detection speed is greatly improved and power consumption is largely reduced.