Tool-Mediated Texture Recognition Using Convolutional Neural Network
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Vibration patterns can be captured by an accelerometer sensor attached to a hand-held device when it is scratched on various type of surface textures. These acceleration signals can carry relevant information for surface texture classification. Typically, methods rely on hand crafted feature engineering but with the use of Convolutional Neural Network manual feature engineering can be eliminated. A proposed method using modern machine learning techniques such as Dropout is introduced by training a Convolutional Neural Network to distinguish between 69 and 100 various surface textures. EHapNet, which is the proposed Convolutional Neural Network model, managed to achieve state of the art results with the used datasets.
Place, publisher, year, edition, pages
2016. , 105 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-303774OAI: oai:DiVA.org:uu-303774DiVA: diva2:973971
Master Programme in Computer Science
Zachariah, Dave ZachariahDaniels, Mats