  The mapping between the visual input on the retina to the cortical surface,
i.e., retinotopic mapping, is an important topic in vision science and
neuroscience. Human retinotopic mapping can be revealed by analyzing cortex
functional magnetic resonance imaging (fMRI) signals when the subject is under
specific visual stimuli. Conventional methods process, smooth, and analyze the
retinotopic mapping based on the parametrization of the (partial) cortical
surface. However, the retinotopic maps generated by this approach frequently
contradict neuropsychology results. To address this problem, we propose an
integrated approach that parameterizes the cortical surface, such that the
parametric coordinates linearly relates the visual coordinate. The proposed
method helps the smoothing of noisy retinotopic maps and obtains
neurophysiological insights in human vision systems. One key element of the
approach is the Error-Tolerant Teichmuller Map, which uniforms the angle
distortion and maximizes the alignments to self-contradicting landmarks. We
validated our overall approach with synthetic and real retinotopic mapping
datasets. The experimental results show the proposed approach is superior in
accuracy and compatibility. Although we focus on retinotopic mapping, the
proposed framework is general and can be applied to process other human sensory
maps.
