Creencias sesgadas respecto al grado de "dureza" de algunas drogas en estudiantes universitarios

El objetivo de esta investigación es evaluar las creencias de los estudiantes universitarios respecto a la dureza de diez drogas: anfetaminas, café, heroína, barbitúricos, marihuana, ansiolíticos, tabaco, alcohol, cocaína y té. Ciento cincuenta y cinco estudiantes de Psicología debían indicar si creían que estas sustancias eran o no drogas duras. Los resultados indican que aunque existe consenso a la hora de clasificar como drogas duras a la heroína y la cocaína y como drogas blandas al tabaco, el café y el té, no existe acuerdo respecto a la clasificación de las otras sustancias. Asimismo se observa que aunque la OMS clasifica el alcohol como una droga altamente peligrosa, menos de la mitad de sujetos lo consideran una droga dura. En general los sujetos tienden a considerar las drogas legales como menos duras independientemente de si los efectos nocivos para la salud. Estos resultados adquieren relevancia cuando lo que se pone en juego es la fiabilidad y validez de los datos obtenidos en diferentes investigaciones que utilizan habitualmente esos conceptos

Using appearance and context for outdoor scene object classification

We propose a probabilistic object classifier for outdoor scene analysis as a first step in solving the problem of scene context generation. The method begins with a top-down control, which uses the previously learned models (appearance and absolute location) to obtain an initial pixel-level classification. This information provides us the core of objects, which is used to acquire a more accurate object model. Therefore, their growing by specific active regions allows us to obtain an accurate recognition of known regions. Next, a stage of general segmentation provides the segmentation of unknown regions by a bottom-strategy. Finally, the last stage tries to perform a region fusion of known and unknown segmented objects. The result is both a segmentation of the image and a recognition of each segment as a given object class or as an unknown segmented object. Furthermore, experimental results are shown and evaluated to prove the validity of our proposal

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