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Title: Positioning articulated figures
Author: Etienne, Stéphane
Awarding Body: University of Glasgow
Current Institution: University of Glasgow
Date of Award: 1998
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Many animation systems rely on key-frames or poses to produce animated sequences of figures we interpret as articulated, e.g. the skeleton of a character. The production of poses is a difficult problem which can be solved by using techniques such as forward and inverse kinematics. However, animators often find these techniques difficult to work with. The work, presented in this thesis, proposes an innovative technique which approaches this problem from a totally different direction from conventional techniques, and is based on Interactive Genetic Algorithms (IGAs). IGAs are evolutionary tools based on the theory of evolution which was first described by Darwin in 1859. They are derived from Genetic Algorithms (GAs) themselves based on the theory of evolution. IGAs have been successfully used to produce abstract pictures, sculptures and abstract animation sequences. Conventional techniques assist the animator in producing poses. On the contrary, when working with IGAs, users assist the computer in its search for a good solution. Unfortunately, this concept is too weak to allow for an efficient exploration of the space of poses as the user requires more control over the evolutionary process. So, a new concept was introduced to let the user specify directly what is of interest, that is a limb or a set of limbs. This information is efficiently used by the computer to greatly enhance the search. Users build a pose by selecting limbs which are of interest. That pose is provided to the computer as a seed to produce a new generation of poses. The degree of similarity is specified directly by the user. Typically, it is small at the beginning and increases as the process reaches convergences. The power of this new technique is demonstrated by two evaluations, one which uses a set of non expert users and another one which uses myself as the sole but expert user. The first evaluation highlighted the high cognitive requirement of the new technique whereas the second evaluation showed that given sufficient training, the new technique becomes much faster than the other two conventional techniques.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA75 Electronic computers. Computer science Pattern recognition systems Pattern perception Image processing