Fast and robust deep neural networks design

In the past few years, we have witnessed a rapid development of deep neural networks in computer vision, from basic image classiffcation tasks to some more advanced applications e.g. object detection and semantic segmentation. Inspire of its great success, there exists two challenges of deep neural networks real-world applications: its computational cost and vulnerability. Thus we are aimed to deal with these two problems in this thesis. To speed up deep networks, we propose a L1-Norm based low-rank approximation method to reduce oat operations based on the alternating direction method (ADM) in Chapter 2. Our experimental results on public datasets, including CIFAR-10 and ImageNet, demonstrate that this new decomposition scheme outperforms the recently developed L2-norm based nonlinear decomposition method. To defend against adversarial examples, we develop a novel pre-processing alogrithm based on image restoration to remove adversarial attack noise in Chapter 3. We detect high-sensitivity which have signiffcant contributions to the image classiffcation performance. Then we partition the image pixels into the two groups: high-sensitivity and low-sensitivity keypoints. For the low-sensitivity pixels, we use the existing total variation (TV) norm-based image smoothing. For the high-sensitivity pixels, we develop a structure-preserving low-rank image completion methods. Based on matrix analysis and optimization, we have derived an iterative solution for this optimization problem. This high-sensitivity points detection helps us to improve the defense against white-box attack BPDA. However, in our keypoints defense we only remove and recover a few part of pixels, which indicates there are still many perturbation over the whole image. In Chapter 4, we propose a novel image completion algorithm structure-preserving progressive lowrank image completion (SPLIC ) based on smoothed rank function (SRF) in which we can reconstruct a image with over 50% removed pixels. In SPLIC, we randomly remove over 50% pixels on the image and then do matrix completion by low-rank approximation to remain the global structure of the image. Differ from other lowrank methods, we replace nuclear norm by smoothed rank function (SRF) for its closer rank function approximation. We introduce total variance (TV) regularization to improve image reconstruction, and then combine total variance (TV) norm de-noising to further remove the perturbation over the whole image. Then we train the network on the SPLIC images. The experimental results show our SPLIC outperforms other pre-processing methods in image reconstruction, gray-box and black-box scenario.Includes bibliographical references (pages 102-119)

Retinal Fundus Image Analysis for Diagnosis of Glaucoma: A Comprehensive Survey

© 2016 IEEE. The rapid development of digital imaging and computer vision has increased the potential of using the image processing technologies in ophthalmology. Image processing systems are used in standard clinical practices with the development of medical diagnostic systems. The retinal images provide vital information about the health of the sensory part of the visual system. Retinal diseases, such as glaucoma, diabetic retinopathy, age-related macular degeneration, Stargardt's disease, and retinopathy of prematurity, can lead to blindness manifest as artifacts in the retinal image. An automated system can be used for offering standardized large-scale screening at a lower cost, which may reduce human errors, provide services to remote areas, as well as free from observer bias and fatigue. Treatment for retinal diseases is available; the challenge lies in finding a cost-effective approach with high sensitivity and specificity that can be applied to large populations in a timely manner to identify those who are at risk at the early stages of the disease. The progress of the glaucoma disease is very often quiet in the early stages. The number of people affected has been increasing and patients are seldom aware of the disease, which can cause delay in the treatment. A review of how computer-aided approaches may be applied in the diagnosis and staging of glaucoma is discussed here. The current status of the computer technology is reviewed, covering localization and segmentation of the optic nerve head, pixel level glaucomatic changes, diagonosis using 3-D data sets, and artificial neural networks for detecting the progression of the glaucoma disease

Influence of Early Bilingual Exposure in the Developing Human Brain.

190 p.La adquisición del lenguaje es un proceso que ese encuentra determinado tanto por mecanismos de desarrollo cognitivo, como por la experiencia lingüística durante los primeros años de vida. Aunque se trata de un proceso relativamente complejo, los bebés muestran una gran habilidad para el aprendizaje del lenguaje. Un entorno de aprendizaje lingüístico bilingüe podría considerarse aun más complejo, ya que los bebés están expuestos a las características lingüísticas de dos lenguas simultáneamente. En primer lugar, los bebés que crecen en un entorno bilingüe tienen que ser capaces de darse cuenta de que están expuestos a dos lenguas diferentes, y posteriormente deben separar y aprender las características especificas de cada una de ellas; por ejemplo, los distintos fonemas, palabras o estructuras gramaticales. Aunque la exposición lingüística total de los bebés bilingües debería ser comparable a la de los bebés monolingües, es probable que la exposición a cada una de las lenguas de su entorno sea menor, ya que tienen que dividir su tiempo de exposición entre ambas. Si bien los bebés bilingües parecen no tener problemas para enfrentarse a un contexto de aprendizaje potencialmente más complejo, ya que alcanzan las distintas etapas de adquisición del lenguaje a un ritmo similar a los bebés monolingües, sí se han observado adaptaciones a nivel conductual y a nivel de funcionamiento cerebral que podrían producirse como consecuencia de este contexto.Basque Center on cognition, brain and languag