Detecting partially occluded ellipses using the Hough transform
Image and Vision Computing - 4th Alvey Vision Meeting
Invariant Descriptors for 3D Object Recognition and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part I
Ellipse fitting by accumulating five-point fits
Pattern Recognition Letters
Fitting a Second Degree Curve in the Presence of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy clustering of elliptic ring-shaped clusters
Pattern Recognition Letters
Direct Least Square Fitting of Ellipses
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision for Mobile Robot Navigation: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Statistical Bias of Conic Fitting and Renormalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Segmentation of Curves into Various Representations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Methods and Programs in Biomedicine
Robotics and Autonomous Systems
An Algorithm for the Least Square-Fitting of Ellipses
ICTAI '10 Proceedings of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Image-Based Visual Servoing for Nonholonomic Mobile Robots Using Epipolar Geometry
IEEE Transactions on Robotics
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Visual pattern recognition is a basic capability of many species in nature. The skill of visually recognizing and distinguishing different objects in the surrounding environment gives rise to the development of sensory-motor maps in the brain, with the consequent capability of object reaching and manipulation. This paper presents the implementation of a real-time tracking algorithm for following and evaluating the 3D position of a generic spatial object. The key issue of our approach is the development of a new algorithm for pattern recognition in machine vision, the Least Constrained Square-Fitting of Ellipses (LCSE), which improves the state of the art ellipse fitting procedures. It is a robust and direct method for the least-square fitting of ellipses to scattered data. In this work we applied it to the iCub humanoid robotics platform simulator and real robot. We used it as a base for a circular object localization within the 3D surrounding space. We compared its performance with the Hough Transform and the state of the art ellipse fitting algorithms, in terms of robustness (succes/failure in the object detection) and fitting precision. Our experiments involve robustness against noise, occlusion, and computational complexities analyses.