In this paper we describe a new method for doing text input with touch sensitive wheels. The method is called Transparent User guided Prediction (TUP). With TUP all characters are assigned to fixed positions on the wheel. A language prediction algorithm is used to make it easy to select the most likely characters. The use of the prediction algorithm is transparent for the users, which makes the use of TUP very intuitive. A prototype of TUP is evaluated against the date stamp method for doing wheel text input. Text entry speed for TUP is about 6-7 words per minute for novice users. This is approximately 30% faster than the date stamp method.

General deformable models have reduced the need for hand crafting new models for every new problem, but still most of the general models rely on manual interaction by an expert, when applied to a new problem, e.g. for selecting parameters and initialization. We propose a full and unified scheme for applying the general deformable template model proposed by (Grenander et al., 1991) to a new problem with minimal manual interaction, beside supplying a training set, which can be done by a non-expert user. The main contributions compared to previous work are a supervised learning scheme for the model parameters, a very fast general initialization algorithm and an adaptive likelihood model based on local means. The model parameters are trained by a combination of a 2D shape learning algorithm and a maximum likelihood based criteria. The fast initialization algorithm is based on a search approach using a filter interpretation of the likelihood model.