Object and Scene Classification: what does a Supervised Approach Provide us?

Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one

Change in biased thinking in a 10-session treatment for borderline personality disorder: Further evidence of the motive-oriented therapeutic relationship.

Abstract Borderline Personality Disorder (BPD) is characterized by both maladaptive thinking and problematic schemas. Kramer and colleagues (2011) showed that using the motive-oriented therapeutic relationship (MOTR), based on the individualized understanding of the patient according to Plan Analysis (Caspar, 2007), can improve treatment outcomes for BPD. The present process-outcome pilot study aimed to examine the effects of the motive-oriented therapeutic relationship on the cognitive biases of patients with BPD. Change in biased cognitions in N=10 patients who were subject to MOTR was compared to that of N=10 patients who received psychiatric-psychodynamic treatment (Gunderson & Links, 2008). Results show a greater decrease in over-generalizations in patients who received MOTR, compared to the patients who received the psychiatric-psychodynamic treatment. These changes were related to outcome in various ways. These findings underline the importance of an individualized case formulation method in bringing about therapeutic change.

Cloud code test IDE

L'objectiu principal d'aquest projecte final de carrera consisteix en desenvolupar una aplicació web que oferisca un entorn simplificat de desenvolupament integrat (IDE) en el llenguatge C/C++, on els estudiants de batxillerat (o secundària en general) puguen iniciar-se en el seu estudi. La finalitat és proveir un entorn agradable a l'alumnat perquè puga seguir correctament les pràctiques que proposa el professor independentment de les circumstàncies pròpies d'aquest (ubicació temporal o permanentde l'alumnat, sistema operatiu que utilitza, dispositiu emprat per a fer les pràctiques, etc..).

Multi-scale label interaction to improve object detection and segmentation

The project aims at advancing the state of the art in the use of context information for classification of image and video data. The use of context in the classification of images has been showed of great importance to improve the performance of actual object recognition systems. In our project we proposed the concept of Multi-scale Feature Labels as a general and compact method to exploit the local and global context. The feature extraction from the discriminative probability or classification confidence label field is of great novelty. Moreover the use of a multi-scale representation of the feature labels lead to a compact and efficient description of the context. The goal of the project has been also to provide a general-purpose method and prove its suitability in different image/video analysis problem. The two-year project generated 5 journal publications (plus 2 under submission), 10 conference publications (plus 2 under submission) and one patent (plus 1 pending). Of these publications, a relevant number make use of the main result of this project to improve the results in detection and/or segmentation of objects.