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. 2013 Dec 5;8(12):e80745.
doi: 10.1371/journal.pone.0080745. eCollection 2013.

Stereoscopic depth perception using a model based on the primary visual cortex

Fernanda da C E C Faria  1 , Jorge Batista, Helder Araújo
Affiliations

Stereoscopic depth perception using a model based on the primary visual cortex

Fernanda da C E C Faria et al. PLoS One. .

Abstract

This work describes an approach inspired by the primary visual cortex using the stimulus response of the receptive field profiles of binocular cells for disparity computation. Using the energy model based on the mechanism of log-Gabor filters for disparity encodings, we propose a suitable model to consistently represent the complex cells by computing the wide bandwidths of the cortical cells. This way, the model ensures the general neurophysiological findings in the visual cortex (V1), emphasizing the physical disparities and providing a simple selection method for the complex cell response. The results suggest that our proposed approach can achieve better results than a hybrid model with phase-shift and position-shift using position disparity alone.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Disparity tuning curves of a pair of receptive fields (Left RF solid lines; Right RF dashed lines) of binocular simple cells with position shift (left) and phase shift (right).
Position-shift disparity tuning curves have identical shapes and a horizontal translation between them. Phase-shift disparity tuning curves have different shapes and the same location in the two eyes.
Figure 2
Figure 2. A model of a complex cell.
The response of a binocular simple cell is described by the summation of the outputs of their left and right eye filters. The RF configuration is consistent with ON and OFF regions. Complex cells are modeled as nonlinear binocular interactions outputs of two quadrature pairs of simple cells. formula image denotes a squaring operation for each matrix element. The final complex cell is arranged as the energy model (sum of squares).
Figure 3
Figure 3. Square stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
Figure 4
Figure 4. Ramp stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
Figure 5
Figure 5. Gabor stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
Figure 6
Figure 6. Tsukuba stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
Figure 7
Figure 7. Venus stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
Figure 8
Figure 8. Sawtooth stereogram.
A: Left image. B: Right image. C: Ground truth disparity map. D: Disparity map using log-Gabor filters. E: Disparity map using Gabor filters.
See this image and copyright information in PMC

References

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