INAPSAC: A New Robust Inlier Identification Technique


  • Radha Myilsamy Reearch Scholar
  • R. Muthukrishnan





Robust statistical methods were first adopted in computer vision to improve the performance of feature extraction algorithms at the bottom level of the vision hierarchy.  These methods tolerate the presence of data points that do not obey the assumed model such points are typically called “outlier”.  Recently, various robust statistical methods have been developed and applied to computer vision tasks. Random Sample Consensus (RANSAC) estimators are one of the widely applied to tackle such problems due to its simple implementation and robustness. There have been a number of recent efforts aimed at increasing the efficiency of the basic RANSAC algorithm. N Adjacent Points Sample Consensus (NAPSAC) is one of the RANSAC method used in computer vision task. In this paper a new algorithm is proposed which is the modified version of NAPSAC with 2-sphere method. The accuracy of the proposed algorithm has been studied through a simulation study along with the existing algorithms in the context of RANSAC techniques.

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