The random noises caused by different devices in the process of X-ray imaging make images degraded, which results in incomplete or even incorrect medical diagnoses. The image pre-processing is the primary procedure of digital X-ray imaging device. Due to the features of X-ray medical image, the temporal recursive filter can be used, whose filtering coefficient decreases exponentially with the difference of two adjacent frames. However, considering the hardware implementation complexity in real-time dynamic processing, an improved self-adaptive filtering algorithm is proposed, of which the filtering coefficient is generated by a linear decay function. We use a Field Programmable Gate Array (FPGA) and other peripherals to design and implement the X-ray medical image pre-processing system based on the improved self-adaptive filter. The design fundamentals and methods are discussed in details, including video decoder, encoder, processing, and display. The experiment results show that the proposed algorithm and system improve the image's signal-noise-ratio effectively, after 4 to 8 frame recursions. The preprocessing system can be practically used in digital Xray imaging device.
Abstract:This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.
A novel technology of north-finding is introduced. Taking the relatively stable distribution of the skylight polarization as the information source, the angle between the carrier's long axis and the solar meridian is obtained by using the polarization-sensitive compass designed firstly. And then the angle is rectified by using the sun's current azimuth to determine the angle between the carrier's long axis and the geographical north-south finally to accomplish the task of north-finding. The experiment result shows that the angle between the carrier's long axis and the solar meridian is achieved effectively and the north can be found instantly.
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