dc.contributor.author |
Perlow, J
|
|
dc.contributor.author |
Rosman, Benjamin S
|
|
dc.contributor.author |
Hayes, B
|
|
dc.contributor.author |
Ranchod, P
|
|
dc.date.accessioned |
2017-12-19T12:39:15Z |
|
dc.date.available |
2017-12-19T12:39:15Z |
|
dc.date.issued |
2017-11 |
|
dc.identifier.citation |
Perlow, J. et al. 2017. Raw material selection for object construction. PRASA-RobMech International Conference, Bloemfontein, South Africa, 29th November - 1 December 2017. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/9889
|
|
dc.description |
Paper presented at the PRASA-RobMech International Conference, Bloemfontein, South Africa, 29th November - 1 December 2017. This is the accepted version of the paper. |
en_US |
dc.description.abstract |
An important step in the construction of novel objects is the ability to recognise combinations of raw materials which are likely to be useful. We aim to exploit the intuition that the visual characteristics of candidate raw materials provide useful cues to their potential combinations. Toward this end, we present a Siamese neural network based model that is able to recognise unseen raw materials present in objects given a list of candidate material images. We demonstrate the utility and efficacy of our model within two domains. The first being a proof-of-concept within Minecraft where we predict the combinations of objects that will result in a target object. The second, more realistic domain, uses the ShapeNet 3D model dataset where we attempt to recover the materials present in a model. We empirically demonstrate that our model is able to learn from a subset of object material pairs and generalise to unseen objects, materials, texture packs. Under these conditions of high visual variation, we show that our model outperforms chance and baseline methods. |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.ispartofseries |
Worklist;19959 |
|
dc.subject |
Deep learning |
en_US |
dc.subject |
Crafting |
en_US |
dc.subject |
Material recognition |
en_US |
dc.title |
Raw material selection for object construction |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Perlow, J., Rosman, B. S., Hayes, B., & Ranchod, P. (2017). Raw material selection for object construction. http://hdl.handle.net/10204/9889 |
en_ZA |
dc.identifier.chicagocitation |
Perlow, J, Benjamin S Rosman, B Hayes, and P Ranchod. "Raw material selection for object construction." (2017): http://hdl.handle.net/10204/9889 |
en_ZA |
dc.identifier.vancouvercitation |
Perlow J, Rosman BS, Hayes B, Ranchod P, Raw material selection for object construction; 2017. http://hdl.handle.net/10204/9889 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Perlow, J
AU - Rosman, Benjamin S
AU - Hayes, B
AU - Ranchod, P
AB - An important step in the construction of novel objects is the ability to recognise combinations of raw materials which are likely to be useful. We aim to exploit the intuition that the visual characteristics of candidate raw materials provide useful cues to their potential combinations. Toward this end, we present a Siamese neural network based model that is able to recognise unseen raw materials present in objects given a list of candidate material images. We demonstrate the utility and efficacy of our model within two domains. The first being a proof-of-concept within Minecraft where we predict the combinations of objects that will result in a target object. The second, more realistic domain, uses the ShapeNet 3D model dataset where we attempt to recover the materials present in a model. We empirically demonstrate that our model is able to learn from a subset of object material pairs and generalise to unseen objects, materials, texture packs. Under these conditions of high visual variation, we show that our model outperforms chance and baseline methods.
DA - 2017-11
DB - ResearchSpace
DP - CSIR
KW - Deep learning
KW - Crafting
KW - Material recognition
LK - https://researchspace.csir.co.za
PY - 2017
T1 - Raw material selection for object construction
TI - Raw material selection for object construction
UR - http://hdl.handle.net/10204/9889
ER -
|
en_ZA |