Unveiling the multimedia unconscious: implicit cognitive processes and multimedia content analysis

One of the main findings of cognitive sciences is that automatic processes of which we are unaware shape, to a significant extent, our perception of the environment. The phenomenon applies not only to the real world, but also to multimedia data we consume every day. Whenever we look at pictures, watch a video or listen to audio recordings, our conscious attention efforts focus on the observable content, but our cognition spontaneously perceives intentions, beliefs, values, attitudes and other constructs that, while being outside of our conscious awareness, still shape our reactions and behavior. So far, multimedia technologies have neglected such a phenomenon to a large extent. This paper argues that taking into account cognitive effects is possible and it can also improve multimedia approaches. As a supporting proof-of-concept, the paper shows not only that there are visual patterns correlated with the personality traits of 300 Flickr users to a statistically significant extent, but also that the personality traits (both self-assessed and attributed by others) of those users can be inferred from the images these latter post as "favourite"

What your Facebook Profile Picture Reveals about your Personality

People spend considerable effort managing the impressions they give others. Social psychologists have shown that people manage these impressions differently depending upon their personality. Facebook and other social media provide a new forum for this fundamental process; hence, understanding people's behaviour on social media could provide interesting insights on their personality. In this paper we investigate automatic personality recognition from Facebook profile pictures. We analyze the effectiveness of four families of visual features and we discuss some human interpretable patterns that explain the personality traits of the individuals. For example, extroverts and agreeable individuals tend to have warm colored pictures and to exhibit many faces in their portraits, mirroring their inclination to socialize; while neurotic ones have a prevalence of pictures of indoor places. Then, we propose a classification approach to automatically recognize personality traits from these visual features. Finally, we compare the performance of our classification approach to the one obtained by human raters and we show that computer-based classifications are significantly more accurate than averaged human-based classifications for Extraversion and Neuroticism

Painterly rendering techniques: A state-of-the-art review of current approaches

In this publication we will look at the different methods presented over the past few decades which attempt to recreate digital paintings. While previous surveys concentrate on the broader subject of non-photorealistic rendering, the focus of this paper is firmly placed on painterly rendering techniques. We compare different methods used to produce different output painting styles such as abstract, colour pencil, watercolour, oriental, oil and pastel. Whereas some methods demand a high level of interaction using a skilled artist, others require simple parameters provided by a user with little or no artistic experience. Many methods attempt to provide more automation with the use of varying forms of reference data. This reference data can range from still photographs, video, 3D polygonal meshes or even 3D point clouds. The techniques presented here endeavour to provide tools and styles that are not traditionally available to an artist. Copyright © 2012 John Wiley & Sons, Ltd

Robust Camera Calibration and Evaluation Procedure Based on Images Rectification and 3D Reconstruction

This paper presents a robust camera calibration algorithm based on contour matching of a known pattern object. The method does not require a fastidious selection of particular pattern points. We introduce two versions of our algorithm, depending on whether we dispose of a single or several calibration images. We propose an evaluation procedure which can be applied for all calibration methods for stereo systems with unlimited number of cameras. We apply this evaluation framework to 3 camera calibration techniques, our proposed robust algorithm, the modified Zhang algorithm implemented by J. Bouguet and Faugeras-Toscani method. Experiments show that our proposed robust approach presents very good results in comparison with the two other methods. The proposed evaluation procedure gives a simple and interactive tool to evaluate any camera calibration method

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

Development of a decission support system for dental treatments with digital images

La evaluación precisa de los tejidos expuestos en una pieza dental constituye una tarea crucial para la evaluación, diagnóstico y monitorización de las patologías dentales. La inspección visual es un modo subjetivo de realizar un diagnóstico sobre el estado de salud dental. Los odontólogos clínicos evalúan y registran la presencia de cada tejido usando índices estandarizados. Una evaluación y monitorización más precisa puede lograrse al registrar de manera más detallada la identificación y medición de cada tipo de tejido dentario visible clínicamente. El principal objetivo de este trabajo es diseñar un sistema computacional de clasificación de tejidos dentarios con imágenes digitalizadas, con el propósito de evaluar de una manera precisa el estado de salud dental. Con este propósito los tejidos dentarios han sido detectados, segmentados y finalmente clasificados en imágenes digitales de piezas dentales vitales. Un procedimiento computacional de tratamiento de imágenes basado en el algoritmo de desviación de la media se ha implementado para realizar la segmentación de regiones en las imágenes de la base de datos; luego se extrajo un conjunto de parámetros característicos de cada una de estas regiones. Con estos datos se ha entrenado un sistema inteligente de clasificación basado en redes neuronales. La validación del sistema clasificador se ha realizado alimentando al sistema con imágenes con las que no se hubo realizado un entrenamiento o clasificación previa. Los resultados del trabajo sugieren que las técnicas de visión por computador y de inteligencia artificial vuelven más efectivo y preciso el proceso de identificación y clasificación de tejidos dentarios, por lo que pueden ser utilizadas como una herramienta eficaz para coadyuvar al proceso de diagnóstico del estado de salud dental