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

Scene Classification Using a Hybrid Generative/Discriminative Approach

We investigate whether dimensionality reduction using a latent generative model is beneficial for the task of weakly supervised scene classification. In detail, we are given a set of labeled images of scenes (for example, coast, forest, city, river, etc.), and our objective is to classify a new image into one of these categories. Our approach consists of first discovering latent ";topics"; using probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature here applied to a bag of visual words representation for each image, and subsequently, training a multiway classifier on the topic distribution vector for each image. We compare this approach to that of representing each image by a bag of visual words vector directly and training a multiway classifier on these vectors. To this end, we introduce a novel vocabulary using dense color SIFT descriptors and then investigate the classification performance under changes in the size of the visual vocabulary, the number of latent topics learned, and the type of discriminative classifier used (k-nearest neighbor or SVM). We achieve superior classification performance to recent publications that have used a bag of visual word representation, in all cases, using the authors' own data sets and testing protocols. We also investigate the gain in adding spatial information. We show applications to image retrieval with relevance feedback and to scene classification in videos

Use of decision trees in colour feature selection: application to object recognition in outdoor scenes

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

Monocular-based 3-D seafloor reconstruction and ortho-mosaicing by piecewise planar representation

Photo-mosaicing techniques have become popular for seafloor mapping in various marine science applications. However, the common methods cannot accurately map regions with high relief and topographical variations. Ortho-mosaicing borrowed from photogrammetry is an alternative technique that enables taking into account the 3-D shape of the terrain. A serious bottleneck is the volume of elevation information that needs to be estimated from the video data, fused, and processed for the generation of a composite ortho-photo that covers a relatively large seafloor area. We present a framework that combines the advantages of dense depth-map and 3-D feature estimation techniques based on visual motion cues. The main goal is to identify and reconstruct certain key terrain feature points that adequately represent the surface with minimal complexity in the form of piecewise planar patches. The proposed implementation utilizes local depth maps for feature selection, while tracking over several views enables 3-D reconstruction by bundle adjustment. Experimental results with synthetic and real data validate the effectiveness of the proposed approach

The role of the amygdala in facial emotional expression during a discrimination task. 'El papel de la am??gdala en una tarea de discriminaci??n de expresiones emocionales'

Resumen tomado de la publicaci??n. Resumen tambi??n en ingl??s

Capacidades de discriminaci??n fon??tica de un contraste voc??lico nativo en el prematuro

Resumen tomado de la publicaci??n

Emoci??n y memoria de reconocimiento : la discriminaci??n de la informaci??n negativa como un proceso adaptativo

Resumen tomado de la publicaci??n

El desenvolupament auditiu : la discriminació de la informació acústica de l'entorn viari. 'El desarrollo auditivo : la discriminación de la información acústica del entorno viario'.

Obtener algunas conclusiones sobre el desarrollo auditivo humano que nos permitan diseñar pautas para la elaboración de un programa de educación vial dirigido a niños de edades comprendidas entre los 6 y los 11 años. 86 niños de 6 años, 67 de 7 años, 80 de 8 años, 46 de 9 años, 72 de 10 años, 48 de 11 años y 128 adultos; la mitad aproximadamente féminas y la mitad hombres; la mitad aproximadamente procedente de zonas rurales y la mitad de zona urbana. Se tuvieron en cuenta fundamentalmente cuatro variables: la edad, el sexo, la zona de residencia y la presencia de algún tipo de hipoacusia (un oído o ambos oídos); se realizó una audiometría a todos los participantes. Se diseñó una batería de pruebas basadas en la discriminación de sonidos, la identificación de sonidos del entorno vial, la asociación de sonidos a ilustraciones, la percepción auditiva del movimiento en distancia, la percepción del riesgo a partir de la información auditiva y la atención visual selectiva. Se administró la batería de pruebas en salas adecuadamente aisladas de sonidos del exterior. Todas las pruebas se realizaron en soportes informáticos que permitían el registro directo del tiempo de reacción y del número de errores; estas dos variables dependientes son las que se utilizaron en el posterior análisis de los datos. Los niños mayores de 8 años obtuvieron umbrales auditivos similares a los de los adultos, los de 6 y 7 años mostraban umbrales significativamente superiores; en las pruebas de reconocimiento y asociación de sonidos a ilustraciones no aparecieron diferencias por edades; en la percepción auditiva de movimiento se formaron tres grupos según su nivel de competencia: 6-7 años, 8-11 años y adultos. Los niños de la zona rural obtenían mejores resultados que los de ciudad, excepto en los cambios de frecuencia, en los que los de ciudad obtenían valores más precisos; los niños no cometen más situaciones arriesgadas que los adultos en una prueba de percepción del riesgo a partir de la información auditiva, sin embargo, desaprovechan más oportunidades de cruzar la calle. El origen de las diferencias comportamentales según la edad parece provenir de la capacidad de percepción auditiva del movimiento en distancia y en la nula competencia de los niños menores de 9 años para usar los cambios tonales.