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Modeling uncertainties in performance of object recognition

  • Author(s): Kumar, S
  • Bhanu, B
  • Thakoor, NS
  • Ghosh, S
  • et al.
Abstract

Efficient probability modeling is indispensable for uncertainty quantification of the recognition data. If the model assumptions do not reflect the intrinsic nature of data and associated random variables, then a strong performance measure will most likely fail to come up with a correct match for recognition. In this paper we propose the probability models for two kinds of data obtained with two distinct goals of recognition: identification and discovery. We consider both frequentisi and Bayesian approaches for drawing inferences from the data.

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