A novel image analysis method for rotational motion of circular feature based on perspective projection

References

• Kim M, Jeon JG, Kwak JS, Lee MH and Ahn C. Moving object segmentation in video sequences by user interaction and automatic object tracking. Image Vis. Comput., 2001, 19, 245–260.
   Web of Science ®Google Scholar
• Lee KC, Ho J, Yang MH. and Kriegman D. Visual tracking and recognition using probabilistic appearance manifolds. Comput. Vis. Image Understand., 2005, 99, 303–331.
   Web of Science ®Google Scholar
• Wu YF, Shen J and Dai M. Traffic object detections and its action analysis. Pattern Recogn. Lett., 2005, 26, 1963–1984.
   Web of Science ®Google Scholar
• Spagnolo P, Orazio TD, Leo M and Distante A. Moving object segmentation by background subtraction and temporal analysis. Image Vis. Comput., 2006, 24, 411–423.
   Web of Science ®Google Scholar
• Cosar S and Çetin M. A graphical model based solution to the facial feature point tracking problem. Image Vis. Comput., 2011, 29, 335–350.
   Web of Science ®Google Scholar
• Zappa E, Testa R, Barbesta M and Gasparetto M. Uncertainty of 3D facial features measurements and its effects on personal identification. Measurement, 2014, 49, 296–307.
   Web of Science ®Google Scholar
• Caja J, Maresca P, Gómez E, Barajas C and Berzal M. Metrological characterization of interior circular features using digital optical machines: calculation models and application scope. Prec. Eng., 2014, 38, 36–47.
   Web of Science ®Google Scholar
• Tissainayagam P and Suter D. Object tracking in image sequences using point features. Pattern Recogn., 2005, 38, 105–113.
   Web of Science ®Google Scholar
• Ma SD. Conics-based stereo, motion estimation, and pose determination. Int. J. Comput. Vis., 1993, 10, 7–25.
   Web of Science ®Google Scholar
• Safaee-Rad R, Benhabib B, Smith KC and Zhou Z. Pre-marking methods for 3D object recognition, Proc. IEEE Int. Conf. on Systems, man and cybernetics, Boston, MA, USA, November 1989, IEEE, pp. 592–595.
   Google Scholar
• Tong XH and Shi WZ. Measuring positional error of circular curve features in Geographic information systems (GIS). Comput. Geosci., 2010, 36, 861–870.
   Web of Science ®Google Scholar
• Safaee-Rad R, Tchoukanov I, Smith KC and Benhabib B. Constraints on quadratic-curved features under perspective projection. Image Vis. Comput., 1992, 10, 532–548.
   Web of Science ®Google Scholar
• Safaee-Rad R, Tchoukanov I, Smith KC and Benhabib B. Three-dimensional location estimation of circular features for machine vision. IEEE Trans. Robot. Autom., 1992, 8, 624–640.
   Google Scholar
• Chen Z and Huang JB. A vision-based method for the circle pose determination with a direct geometric interpretation. IEEE Trans. Robot. Autom., 1999, 15, 1135–1140.
   Google Scholar
• Wang GH, Wu J and Ji ZQ. Single view based pose estimation from circle or parallel lines. Pattern Recogn. Lett., 2008, 29, 977–985.
   Web of Science ®Google Scholar
• Eberli D, Scaramuzza D, Weiss S and Siegwart R. Vision based position control for MAVs using one single circular landmark. J. Intell. Robot. Syst., 2011, 61: 495–512.
   Web of Science ®Google Scholar
• Da FP, Li Q, Zhang H and Fang X. Self-calibration using two same circles. Opt. Laser Technol., 2012, 44, 1924–1933.
   Web of Science ®Google Scholar
• Wu YH, Li XJ, Wu FC and Hu ZY. Coplanar circles, quasi-affine invariance and calibration. Image Vis. Comput., 2006, 24, 319–326.
   Web of Science ®Google Scholar
• Chen XY, Hu Y, Ma Z, Yu SH and Chen YQ. The location and identification of concentric circles in automatic camera calibration. Opt. Laser Technol., 2013, 54, 185–190.
   Web of Science ®Google Scholar
• Ji Q. 3D Face pose estimation and tracking from a monocular camera. Image Vis. Comput., 2002, 20, 499–511.
   Web of Science ®Google Scholar
• Saito Y, Arai Y and Gao W. Detection of three-axis angles by an optical sensor. Sens. Actuators A: Phys., 2009, 150, 175–183.
   Web of Science ®Google Scholar
• Shang YH and Palmer PL. Lunar lander’s three-dimensional translation and yaw rotation motion estimation during a descent phase using optical navigation. Acta Astron., 2011, 68, 149–159.
   Web of Science ®Google Scholar
• Gao W, Arai Y, Shibuya A, Kiyono S and Park CH. Measurement of multi-degree-of- freedom error motions of a precision linear air-bearing stage. Prec. Eng., 2006, 30, 96–103.
   Web of Science ®Google Scholar
• Wu B, Zhang F and Xue T. Monocular- vision-based method for online measurement of pose parameters of weld stud. Measurement, 2015, 61, 263–269.
   Web of Science ®Google Scholar
• Shang Y, Liu JB, Xie TT and Yao L. S. A monocular pose measurement method of a translation-only one-dimensional object without scene information. Optik – Int. J. Light Electron Opt., 2014, 125, 4051–4056.
   Google Scholar
• Lee DH and Nakamura Y. Motion recognition and recovery from occluded monocular observations. Robot. Autonom. Syst., 2014, 62, 818–832.
   Web of Science ®Google Scholar
• Rogez G, Orrite C, Guerrero JJ and Philip HS. Torr. Exploiting projective geometry for view-invariant monocular human motion analysis in man-made environments. Comput. Vis. Image Understand., 2014, 120, 126–140.
   Web of Science ®Google Scholar