Recognition of circular patterns on GPUs: Performance analysis and contributions

We develop a novel approach for computing the circle Hough transform entirely on graphics hardware (GPU). A primary role is assigned to vertex processors and the rasterizer, overshadowing the traditional foreground of pixel processors and enhancing parallel processing. Resources like the vertex cache or blending units are studied too, with our set of optimizations leading to extraordinary peak gain factors exceeding 358x over a typical CPU execution. Software optimizations, like the use of precomputed tables or gradient information and hardware improvements, like hyperthreading and multicores are explored on CPUs as well. Overall, the GPU exhibits better scalability and much greater parallel performance to become a solid alternative for computing the classical circle Hough transform versus those optimal methods run on emerging multicore architectures.     

复杂矢量图中矩形域填充的快速定位方法

工业生产中经常需要在已有的图纸上面添加文本信息。对于常用的矢量图纸,添加文本标注自动定位的工艺可以归结为近似封闭的矢量图形中矩形块填充的位置定位问题。针对矢量图形,首先基于多尺度进行光栅化处理,然后用Freeman链码进行象素标记,并结合光栅图形的最佳选择方向方法来解决此问题。实际应用表明,该方法快速、准确,可充分满足工业设计中实际问题的需要。     

一种图形光栅的硬件实现算法

提出了一种面向嵌入式平台的图形光栅的硬件实现算法。将三角面包围盒内的像素分成多个规则像素块,在像素块基础上进行扫描转换和像素插值以及透视校正。在对算法做了大量优化后,用FPGA(现场可编程门阵列)对算法进行了实现和验证。与传统的光栅算法相比,提出的算法提高了像素命中率,减小了计算复杂度,降低了硬件成本。验证结果表明,算法渲染的图形质量达到OpenGLES 1.1渲染效果;在一般场景下的渲染速度达到30帧/秒,满足实时渲染要求;在Xilinx FPGA Vertex2P xc2vp30-7ff89上的综合资源为5 545个Slice,硬件消耗较小。