VISCUBE: A Multi-Layer Vision Chip

Zarándy, Ákos; Rekeczky, Csaba; Földesy, Péter; Carmona-Galán, Ricardo; Cembrano, Gustavo Liñán; Gergely, Soós; Rodríguez-Vázquez, Á.; Roska, Tamás

doi:10.1007/978-1-4419-6475-5_8

Cited by 4 publications

(1 citation statement)

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“…There are few references in the literature that use the position of multiple foveas in the detection of visual stimuli. Soos et al [40]- [43] used neighboring foveas to create an algorithm for detecting moving land objects from images captured by unmanned aerial vehicles. They implemented a field of local potential with multiple foveas.…”

Section: Related Workmentioning

confidence: 99%

Targets Detection Using Multiple Foveas

Medeiros¹,

Gomes²,

Gonçalves³

2021

Anais Estendidos Da XXXIV Conference on Graphics, Patterns and Images (SIBRAPI Estendido 2021)

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Target detection enables running a robotic task. However, their limited resources make large amount of data processing harder. Image foveation is an approach that can reduce processing demand by reducing the amount of data to be processed. However, as an important visual stimulli can be attenuated by this reduction, some strategy should be applied in order to keep/recover awareness of it. This work compares gradient descent (potential field), maximum likelihood, multilateration, trilateration, and barycentric coordinates to solve this problem in a multiple mobile foveas context. Our results demonstrate that the proposed methodology detects the target converging with an average euclidian distance of 51 pixels from the target's center position.

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Section: Related Workmentioning

confidence: 99%