Observatorio de I+D+i UPM

Memorias de investigación
Tesis:
From traditional multi-stage learning to end-to-end deep learning for computer vision applications
Año:2018
Áreas de investigación
  • Tecnología electrónica y de las comunicaciones
Datos
Descripción
The renaissance of Deep Neural Networks in the era of big data, along with the use of high- performance hardware that reduces computational time, have changed the paradigm of machine learning, specially in the field of computer vision. Whereas systems based on traditional machine learning rely on multiple stages and hand-crafted features to get the insight of the problem, Convolutional Neural Networks automatically learn the features that maximize the learning accuracy directly from raw images in an end-to-end manner. The purpose of this dissertation is to show the gap between traditional multi-stage learning systems and end-to-end deep learning systems, addressing different applications for a qualitative comparison. First, an expert-knowledge recognition system has been developed to deal with dynamic hand gestures. The key aspects of this system are hand-crafted image and video descriptors, and also the pipeline of the whole system. These descriptors have been designed to face difficulties of vision- based approaches such as illumination changes, intra-class and inter-class variances, and multiple scales. The design of the multiple stages of the system solve intermediate steps that are necessary to successfully apply the previous descriptors. Since the proposed hand-gesture recognition system has been designed for a human-computer interface, it comprises detection and tracking stages to localize the object of interest, and a recognition stage to categorize the performed gesture. Second, DL approaches have been proposed for different computer vision applications. Re- search efforts have focused on building these types of end-to-end systems to face the weaknesses present in traditional learning. Unlike previous approach, they do not need multiple stages to perform the target task, nor feature engineering. Their architecture designs rely on the task to be solved, its complexity, and the available amount of data. These guidelines have been applied to common vision-based applications such vehicle detection, and hand-gesture recognition, but also to more challenging situations, such as robotics applications.
Internacional
Si
ISBN
Tipo de Tesis
Doctoral
Calificación
Sobresaliente cum laude
Fecha
28/09/2018
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Ana Isabel Maqueda Nieto (UPM)
  • Director: Narciso Garcia Santos (UPM)
  • Director: Carlos Roberto del Blanco Adan (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de Tratamiento de Imágenes (GTI)
  • Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
  • Departamento: Señales, Sistemas y Radiocomunicaciones
S2i 2021 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)