This paper presents a neuromorphic computing model that classifies material textures using a neural coding scheme based on threshold encoding. The proposed threshold encoding converts raw tactile data of each texture into an eventbased data highlighting the spatio-temporal features needed to recognize human touch. Achieved results show that the model can categorize the input tactile signals into their corresponding material textures with high accuracy and fast inference. This work paves the way toward employing the proposed encoding method in more complex tactile based applications from the theoretical and hardware implementation aspects.
Spiking Neural Network Based on Threshold Encoding For Texture Recognition
Haydar Al Haj Ali;Ali Dabbous;Ali Ibrahim;Maurizio Valle
2022-01-01
Abstract
This paper presents a neuromorphic computing model that classifies material textures using a neural coding scheme based on threshold encoding. The proposed threshold encoding converts raw tactile data of each texture into an eventbased data highlighting the spatio-temporal features needed to recognize human touch. Achieved results show that the model can categorize the input tactile signals into their corresponding material textures with high accuracy and fast inference. This work paves the way toward employing the proposed encoding method in more complex tactile based applications from the theoretical and hardware implementation aspects.File | Dimensione | Formato | |
---|---|---|---|
2022187308.pdf
accesso aperto
Tipologia:
Documento in Post-print
Dimensione
972.19 kB
Formato
Adobe PDF
|
972.19 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.