Bioinspired Stimulus Encoder for Cortical Visual Neuroprostheses

Sousa, Leonel; Pfister, Tomas; Pelayo, Francisco J.; Martínez, A.; Morillas, Christian A.; Romero, Samuel

doi:10.1007/1-4020-3128-9_22

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Other papers propose strategies for vision processing that include reproducing aspects of retinal behavior in encoding visual information including edge and motion detection, and contrast gain control [24]. The silicon retina [25] simulates the retina more closely, including four major ganglion cell types, with low energy requirements.…”

Section: Vision Processing In Prosthetic Visionmentioning

confidence: 99%

Mobility and low contrast trip hazard avoidance using augmented depth

et al. 2014

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Section: Vision Processing In Prosthetic Visionmentioning

confidence: 99%

Mobility and low contrast trip hazard avoidance using augmented depth

et al. 2014

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“…A retinal encoder module converts visual input to neuromorphic pulses inorder to stimulate the visual cortex of the human brain to enable the sensation of vision. This is the primary module in any visual prosthesis applications (Sousa et al, 2005;Morillas et al, 2007). In the retinal signal processing, the photo receptors are distributed in the hexagonal fashion.…”

Section: Introductionmentioning

confidence: 99%

Hardware implementation of hexagonal sampling based bio-inspired retinal encoder

Vinod

Veni

2017

IJSISE

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This paper addresses hardware implementation of a power efficient retinal encoder system used in visual prosthesis equipment for blind people. The hardware architecture is inspired from the retinal receptive fields in mammalian vision system. The captured image is passed through several filter banks in different filtering stages in primate vision system. As the photoreceptors in the primate vision system are arranged in hexagonal fashion, the hexagonal tessellation scheme was found to be the most suitable sampling scheme for retinal image processing. In this work, the hardware implementation of different retinal filtering stages and final edge detection scheme is performed in Altera Cyclone II FPGA and in Synopsys Design Vision. An area efficient Cellular Logic Array Processing (CLAP) algorithm is used as the final stage edge detector. Both rectangular and hexagonal edge detection schemes were tested and their performance was analysed using Berkeley Segmentation Dataset and ROC performance scheme.

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