期刊文献+

四边形分片逼近控制点的图像畸变校正算法 被引量:5

Image Distortion Correction Algorithm Based on Quadrilateral Fractal Approach Controlling Points
下载PDF
导出
摘要 针对传统的图像畸变校正算法建模复杂、实时性差且图像信息易丢失等缺点,提出了一种基于四边形分片逼近控制点的图像畸变校正算法。该方法以标准点阵图像作为量测目标,将数学形态学和滑动邻域操作相结合以确定畸变图像像素点质心,采用基于四边形分片逼近的方法来拟合高次多项式校正模型,运用两步一维线性灰度级插值向后映射算法确定输出图像中像素点的灰度。将该算法在TMS320DM6437DSP上实现,实验结果表明,校正一幅像素为768×494的图像所用的时间为0.036s,畸变校正的误差在0.31个像素以内,有效地避免了边缘信息丢失、空洞及灰度失真现象。 To overcome the shortcomings of traditional distortion correction algorithms, an image distortion correction algorithm based on quadrilateral fractal approach controlling points is proposed. The standard raster image is used as the measurement target in the algorithm, the mathematical morphology is combined with sliding impending domain operation to fix on the distorted image's pixel centroid, and the algorithm based on quadrilateral fractal approach controlling points is applied to fit high-order polynomial correction model. For image gray recovery, a two-step one-dimensional linear backward mapping method is used. The algorithm is applied on TMS320DM6437 DSP, and the experimental results show that for a 768 pixels x 494 pixels image, the correction time is 0.036 s. The correction error is within 0.31 pixel and the edge information loss and inanition are effectively avoided.
出处 《光电工程》 CAS CSCD 北大核心 2009年第5期77-82,共6页 Opto-Electronic Engineering
基金 省部级重大基金资助项目(1020020220606)
关键词 机器视觉 图像畸变 校正 四边形分片逼近 robot vision image distortion correction quadrilateral fractal approach
  • 相关文献

参考文献20

  • 1Desonza Guilherme N, Kak Avinash C. Vision for Mobile Robot Navigation: A Survey [J]. IEEE Transactions on Pattern Analysis and Machine Intelligenee(S0162-8828), 2002, 24(2): 237-267.
  • 2Pavon Juan, Gomez-Sanz Jorge, Fernandez-Caballero Antonio, et al. Development of intelligent multisensor surveillance system with agents [J]. Robotics and Autonomous Systems(S0921-8890), 2007, 55(12): 892-903.
  • 3Jagarmadan V, Prakash M C, Sarma R R, et al. Feature extraction and image registration of color images using Fourier bases [C]//IEEE International Conference on Image Processing, Genoa, Italy, Sept 11-14, 2005, 2: 657-662.
  • 4ZHAO Xiao-chuan, LUO Qing-sheng, HAN Bao-ling. Survey on robot multi-sensor information fusion technology [C]//7th World Congress on Intelligent Control and Automation, Chongqing, China, June 25-27, 2008: 5014-5018.
  • 5Tsair R. Versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV camera and lenses [J]. IEEE Journal of Roboties and Automation(S0882-4967), 1987, 3(4): 323-344.
  • 6SHIH Sheng-wen, HUNG Yi-ping, LIN Wei-song. When shouls we consider lens distortion in camera calibration [J]. Pattern Recognition(S0031-3203), 1995, 28(3): 447-461.
  • 7Bhosle Udehav, Chaudhuri Subhasis, Sumantra Dutta Roy. A fast method for image mosaicing using Geometric Hashing [J]. IETE Journal of Research(S0377-2063), 2002, 48(3/4): 317-324.
  • 8姜大志,郁倩,王冰洋,丁秋林.计算机视觉成象的非线性畸变研究与综述[J].计算机工程,2001,27(12):108-110. 被引量:33
  • 9Asari K V. Technique of distortion correction in endoscopic images using a polynomial expansion [J]. Medical and Biological Engineering and Computing(S0140-0118), 1999, 37(1): 126-132.
  • 10王珂娜,邹北骥,黄文梅.一种基于神经网络的畸变图像校正方法[J].中国图象图形学报(A辑),2005,10(5):603-607. 被引量:24

二级参考文献51

共引文献104

同被引文献34

引证文献5

二级引证文献11

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部