Drawing-Based Automatic Dementia Screening Using Gaussian Process Markov Chains

Screening tests play an important role for early detection of dementia. Among those widely used screening tests, drawing tests have gained much attention in clinical psychology. Traditional evaluation of drawing tests totally relies on the appearance of drawn picture, but does not consider any time-dependent behaviour. We demonstrated that the processing speed and direction can reflect the decline of cognitive function, and thus may be useful for disease screening. We proposed a model of Gaussian process Markov chains (GPMC) to study the complex associations within the drawing data. Specifically, we modeled the process of drawing in a state-space form, where a drawing state is composed of drawing direction and velocity with consideration of the processing time. For temporal modeling, our scope focused more on discrete-time Markov chains on continuous state space. Because of the short processing time of picture drawing, we applied higher-order of Markov chains to model long-term temporal correlation across drawing states. Gaussian process regression was used for universal function approximation to flexibly infer the state transition function. With Gaussian process prior to the distribution of function space, we could encode high-level function properties such as noisiness, smoothness and periodicity. We also derived an efficient training mechanism for complex Gaussian process regression on bivariate Markov chains. With GPMC, we present an optimal decision rule based on Bayesian decision theory. We applied our proposed method to a drawing test for dementia screening, i.e. interlocking pentagon-drawing test. We tested our models with 256 subjects who are aged from 65 to 95. Finally, comparing to the traditional methods, our models showed remarkable improvement in drawing test for dementia screening

Sketch recognition of digital ink diagrams : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Palmerston North, New Zealand

Figures are either re-used with permission, or abstracted with permission from the source article.Sketch recognition of digital ink diagrams is the process of automatically identifying hand-drawn elements in a diagram. This research focuses on the simultaneous grouping and recognition of shapes in digital ink diagrams. In order to recognise a shape, we need to group strokes belonging to a shape, however, strokes cannot be grouped until the shape is identified. Therefore, we treat grouping and recognition as a simultaneous task. Our grouping technique uses spatial proximity to hypothesise shape candidates. Many of the hypothesised shape candidates are invalid, therefore we need a way to reject them. We present a novel rejection technique based on novelty detection. The rejection method uses proximity measures to validate a shape candidate. In addition, we investigate on improving the accuracy of the current shape recogniser by adding extra features. We also present a novel connector recognition system that localises connector heads around recognised shapes. We perform a full comparative study on two datasets. The results show that our approach is significantly more accurate in finding shapes and faster on process diagram compared to Stahovich et al. (2014), which the results show the superiority of our approach in terms of computation time and accuracy. Furthermore, we evaluate our system on two public datasets and compare our results with other approaches reported in the literature that have used these dataset. The results show that our approach is more accurate in finding and recognising the shapes in the FC dataset (by finding and recognising 91.7% of the shapes) compared to the reported results in the literature

On-line Sketch Recognition Using Direction Feature

Abstract. Sketch recognition is widely used in pen-based interaction, especially as the increasing popularity of devices with touch screens. It can enhance human-computer interaction by allowing a natural/free form of interaction. The main challenging problem is the variability in hand drawings. This paper presents an on-line sketch recognition method based on the direction feature. We also present two feature representations to train a classifier. We support our case by experimental results obtained from the NicIcon database. A recognition rate of 97.95 % is achieved, and average runtime is 97.6ms using a Support Vector Machine classifier