Benjamin Williams PhD

Benjamin Williams


Publications:
2008
  Modelling motion primitives and their timing in biologically executed movements
Williams, B, Toussaint, M & Storkey, AJ 2008, Modelling motion primitives and their timing in biologically executed movements. in JC Platt, D Koller, Y Singer & ST Roweis (eds), Advances in Neural Information Processing Systems 20. Curran Associates Inc, pp. 1609-1616, Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, B.C., Canada, 3-8 December.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Williams, Ben, Toussaint, Marc & Storkey, Amos J..
Number of pages: 8
Pages: 1609-1616
Publication Date: 2008
Publication Information
Category: Conference contribution
Original Language: English
2007
  A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data
Williams, BH, Toussaint, M & Storkey, AJ 2007, A Primitive Based Generative Model to Infer Timing Information in Unpartitioned Handwriting Data. in MM Veloso (ed.), IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007. IJCAI-INT JOINT CONF ARTIF INTELL, Freiburg, pp. 1119-1124, 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, 6-12 January.
Biological movement control and planning is based upon motor primitives. In our approach, we presume that each motor primitive takes responsibility for controlling a small sub-block of motion, containing coherent muscle activation outputs. A central timing controller cues these subroutines of movement, creating complete movement strategies that are built up by overlaying primitives, thus creating synergies of muscle activation. This partitioning allows the movement to be defined by a sparse code representing the timing of primitive activations. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. The variation in the handwriting data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. The model is naturally partitioned into a low level primitive output stage, and a top-down primitive timing stage. This partitioning gives us an insight into behaviours such as scribbling, and what is learnt in order to write a new character.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Williams, Ben H., Toussaint, Marc & Storkey, Amos J..
Number of pages: 6
Pages: 1119-1124
Publication Date: 2007
Publication Information
Category: Conference contribution
Original Language: English
2006
  Extracting Motion Primitives from Natural Handwriting Data
Williams, BH, Toussaint, M & Storkey, AJ 2006, Extracting Motion Primitives from Natural Handwriting Data. in S Kollias, A Stafylopatis, W Duch & E Oja (eds), Artificial Neural Networks – ICANN 2006. Lecture Notes in Computer Science, vol. 4132, Springer-Verlag Berlin Heidelberg, pp. 634-643. DOI: 10.1007/11840930_66
For the past 10 years it has become clear that biological movement is made up of sub-routine type blocks, or motor primitives, with a central controller timing the activation of these blocks, creating synergies of muscle activation. This paper shows that it is possible to use a factorial hidden Markov model to infer primitives in handwriting data. These primitives are not predefined in terms of location of occurrence within the handwriting, and they are not limited or defined by a particular character set. Also, the variation in the data can to a large extent be explained by timing variation in the triggering of the primitives. Once an appropriate set of primitives has been inferred, the characters can be represented as a set of timings of primitive activations, along with variances, giving a very compact representation of the character. Separating the motor system into a motor primitive part, and a timing control gives us a possible insight into how we might create scribbles on paper.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Williams, Ben H., Toussaint, Marc & Storkey, Amos J..
Pages: 634-643
Publication Date: 2006
Publication Information
Category: Conference contribution
Original Language: English
DOIs: 10.1007/11840930_66

Projects:
Extracting motor primitives from handwriting data (PhD)