Study of an object recognition algorithm

This project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.

Written words turned into moving Images: Jeanette Winterson translates "Oranges Are Not the Only Fruit for the Screen"

Eterio Pajares, Raquel Merino y José Miguel Santamaría (eds.)

From words to images and then to one image: the poster in Spielberg' s translations of literary works

Eterio Pajares, Raquel Merino y José Miguel Santamaría (eds.)

Uso de células iPS en terapia regenerativa renal: obtención de células iPS mediante vector no integrativo y diferenciación hacia podocitos.

[ES]En las sociedades modernas existe una creciente preocupación por el aumento de la incidencia de la enfermedad renal crónica. Debido a la deficiencia de donantes de órganos y al elevado coste del tratamiento de diálisis, existe la necesidad de desarrollar nuevos tratamientos para estos pacientes. La medicina regenerativa basada en la aplicación de células iPS es una opción prometedora para el tratamiento de esta enfermedad. Sin embargo, la falta de conocimientos sobre el estado pluripotencial de las células y sobre su proceso de diferenciación, así como las limitaciones derivadas del propio procedimiento de reprogramación, impiden su aplicación clínica en un futuro inmediato. Para que se convierta en realidad, numerosas investigaciones se están llevando a cabo con el objetivo de mejorar el procedimiento y hacerlo adecuado para su aplicación clínica. En este trabajo se propone un método que permitiría obtener células iPS a partir de células mesangiales mediante la transfección con un vector no integrativo, el virus Sendai, portador de los genes Oct3/4, Sox2, Klf4 y c-Myc. Al tratarse de un vector no integrativo, se minimizaría el efecto del proceso de reprogramación sobre la estabilidad del genoma celular. Además, en este proyecto se estudiará la capacidad de las células iPS obtenidas para diferenciarse en células progenitoras de podocitos que puedan ser aplicadas específicamente en terapias regenerativas para enfermos renales crónicos.

A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images

This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach.

Contributions to the analysis and segmentation of remote sensing hyperspectral images

142 p.

BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data

A new supervised burned area mapping software named BAMS (Burned Area Mapping Software) is presented in this paper. The tool was built from standard ArcGIS (TM) libraries. It computes several of the spectral indexes most commonly used in burned area detection and implements a two-phase supervised strategy to map areas burned between two Landsat multitemporal images. The only input required from the user is the visual delimitation of a few burned areas, from which burned perimeters are extracted. After the discrimination of burned patches, the user can visually assess the results, and iteratively select additional sampling burned areas to improve the extent of the burned patches. The final result of the BAMS program is a polygon vector layer containing three categories: (a) burned perimeters, (b) unburned areas, and (c) non-observed areas. The latter refer to clouds or sensor observation errors. Outputs of the BAMS code meet the requirements of file formats and structure of standard validation protocols. This paper presents the tool's structure and technical basis. The program has been tested in six areas located in the United States, for various ecosystems and land covers, and then compared against the National Monitoring Trends in Burn Severity (MTBS) Burned Area Boundaries Dataset.

Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging

136 p.