Semantic Scholar Open Access 2019 36 sitasi

Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors

M. Domínguez-Morales A. Jiménez-Fernandez G. Jiménez-Moreno C. Conde E. Cabello +1 lainnya

Abstrak

Many advances have been made in the field of computer vision. Several recent research trends have focused on mimicking human vision by using a stereo vision system. In multi-camera systems, a calibration process is usually implemented to improve the results accuracy. However, these systems generate a large amount of data to be processed; therefore, a powerful computer is required and, in many cases, this cannot be done in real time. Neuromorphic Engineering attempts to create bio-inspired systems that mimic the information processing that takes place in the human brain. This information is encoded using pulses (or spikes) and the generated systems are much simpler (in computational operations and resources), which allows them to perform similar tasks with much lower power consumption, thus these processes can be developed over specialized hardware with real-time processing. In this work, a bio-inspired stereo-vision system is presented, where a calibration mechanism for this system is implemented and evaluated using several tests. The result is a novel calibration technique for a neuromorphic stereo vision system, implemented over specialized hardware (FPGA - Field-Programmable Gate Array), which allows obtaining reduced latencies on hardware implementation for stand-alone systems, and working in real time.

Topik & Kata Kunci

Penulis (6)

M

M. Domínguez-Morales

A

A. Jiménez-Fernandez

G

G. Jiménez-Moreno

C

C. Conde

E

E. Cabello

A

A. Linares-Barranco

Format Sitasi

Domínguez-Morales, M., Jiménez-Fernandez, A., Jiménez-Moreno, G., Conde, C., Cabello, E., Linares-Barranco, A. (2019). Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors. https://doi.org/10.1109/ACCESS.2019.2943160

Akses Cepat

Lihat di Sumber doi.org/10.1109/ACCESS.2019.2943160
Informasi Jurnal
Tahun Terbit
2019
Bahasa
en
Total Sitasi
36×
Sumber Database
Semantic Scholar
DOI
10.1109/ACCESS.2019.2943160
Akses
Open Access ✓