RT Generic
T1 Dataset for: Texture Detection with Feature Extraction on Embedded FPGA
A1 Lora Rivera, Raúl
A1 Oballe-Peinado, Óscar
A1 Vidal-Verdú, Fernando
K1 Detectores
K1 Electrónica
K1 Fourier, Transformaciones de
AB A feature extraction algorithm for texture detection oriented to its implementation on embedded electronics based on a Field-Programmable Gate Array (FPGA) is proposed in this paper.Local pre-processing with smart tactile sensors can help to improve dexterity in artificial hands. Simplicity is the goal in order to achieve a hardware-friendly strategy that can be replicated and integrated with another circuitry. This is interesting, considering that tactile sensors are arrays and FPGAs are capable of parallel execution. The proposal was tested with a custom smart tactile sensor mounted on a Cartesian robot to explore different textures. A comparison with a common feature extraction approach based on the fast Fourier Transform (FFT)computation was also made. In addition, the whole procedure is implemented on a System on Chip (SoC) with the feature extraction on the embedded FPGA and a k-means classifier on an ARM core. The proposed algorithm obtains the spatial frequency components of the tactile signal but not their power. Therefore, some information is lost with respect to that provided by the FFT. Nevertheless, an 89.17% accuracy of the proposed algorithm is obtained versus 91.4% with the FFT when twelve different textures are considered, including complex and fabric textures. There is a noticeable saving in power and hardware resources. In addition, since the size of the feature vector is much smaller, data traffic and memory usage is much lower, and the classifier can be simpler.
PB IEEE
YR 2023
FD 2023
LK https://hdl.handle.net/10630/39118
UL https://hdl.handle.net/10630/39118
LA eng
NO PID2021-125091OB-I00
NO Spanish Government with a Formación de Profesorado Universitario (FPU) Grant given by the Ministerio de Ciencia, Innovación y Universidades
NO European Regional Development Fund (ERDF) Program Funds
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026