Segmentació d'imatges amb tècniques adreçades per models

• Autores: Margarita Torre Alcoceba
• Directores de la Tesis: Fernando Martínez Saez (dir. tes.) , Petia Radeva (codir. tes.)
• Lectura: En la Universitat Autònoma de Barcelona ( España ) en 2020
• Idioma: español
• ISBN: 9788449097423
• Tribunal Calificador de la Tesis: Manuel Francisco González Penedo (presid.) , Felipe Lumbreras Ruiz (secret.) , Oliver Diaz (voc.)
• Enlaces
  • Tesis en acceso abierto en: TDX
• Resumen

  • Photography freezes in an instant the data that can later be extracted, interpreted and transformed over time to communicate information in different formats. Making maps from photographs was a revolution in cartography. Advances in Computer Vision are helping to bring about the next revolution in this discipline, which aims at more and more detailed geographic information which is required in shorter periods of time. In this way, the process that goes from image to a map has become increasingly automatic. The images already captured with high-resolution digital cameras are automatically placed in the correct position of the terrain as if they were a sheet that covers it, thanks to the digital terrain models, thus obtaining orthophotomaps. In these circumstances, the only burden that remains to be lightened is the extraction of the topographic elements, without losing the precision and quality of interpretation that until now has been provided by human operators.

    This research focuses on the development of new computerized methods that facilitate these tasks of extracting information from aerial images. We start with the development of a strategy to semi-automatically extract fields from the images. This approach uses the almost homogeneous response of the fields and how this response differs from that obtained from their neighbors. The process is carried out by means of the method in which adjacent regions compete to own a pixel. When the contrast lines of the images are also taken into account, it is possible to extend the previous methodology to extract roads. In both cases it is necessary to guide the entire process, not only by the points given by an operator, but by the model of the element to be extracted. The model helps to refine the results obtained. When Deep Learning burst onto the Computer Vision scene, all the processes of image classification were upended. So, we propose a joint venture between a deep network and an energy-minimization model-guided radiometric method that improves the benefits of each component. This approach reduces to a minimum the need for human interaction and obtains reliable results.