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MODVIS Workshop

Bayesian estimation

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Global Estimation Of Signed 3d Surface Tilt From Natural Images, Seha Kim, Johannes Burge May 2018

Global Estimation Of Signed 3d Surface Tilt From Natural Images, Seha Kim, Johannes Burge

MODVIS Workshop

The ability of human visual systems to estimate 3D surface orientation from 2D retinal images is critical. But the computation to calculate 3D orientation in real-world scenes is not fully understood. A Bayes optimal model grounded in natural statistics has explained 3D surface tilt estimation of human observers in natural scenes (Kim and Burge, 2018). However, the model is limited because it estimates only unsigned tilt (tilt modulo 180deg). We extend the model to predict signed tilt estimates and compared with human signed estimates. The model takes image pixels as input and produces optimal estimates of tilt as output, using …