DiSTA

The present study investigates autobiographical memory through the use of imagery and a short narrative. A sample of 72 young adults, residing in Italy, were asked to draw a meaningful memory (and to choose a title for it) about their infancy, childhood, pre-adolescence and adolescence. Through the use of special markers, a qualitative-quantitative analysis of the drawings was carried out. The results made it possible to highlight the different elements that gave shape to the Self in a particular period, leaving traces in memory: specific social references, emotions and moods, places, actions, achievements and personal objects.

We propose a novel method for text spotting from scene images based on augmented Multi-resolution Maximally Stable Extremal Regions and Convolutional Neural Networks. The goal of this work is augmenting text character proposals to maximize their coverage rate over text elements in scene images, to obtain satisfying text detection rates without the need of using very deep architectures nor large amount of training data. Using simple and fast geometric transformations on multi-resolution proposals our system achieves good results for several challenging datasets while also being computationally efficient to train and test on a desktop computer.