GAZE ESTIMATION USING SCLERA AND IRIS EXTRACTION

Tracking gaze of an individual provides important information in understanding the behavior of that person. Gaze tracking has been widely used in a variety of applications from tracking consumers gaze fixation on advertisements, controlling human-computer devices, to understanding behaviors of patients with various types of visual and/or neurological disorders such as autism. Gaze pattern can be identified using different methods but most of them require the use of specialized equipments which can be prohibitively expensive for some applications. In this dissertation, we investigate the possibility of using sclera and iris regions captured in a webcam sequence to estimate gaze pattern. The sclera and iris regions in the video frame are first extracted by using an adaptive thresholding technique. The gaze pattern is then determined based on areas of different sclera and iris regions and distances between tracked points along the irises. The technique is novel as sclera regions are often ignored in eye tracking literature while we have demonstrated that they can be easily extracted from images captured by low-cost camera and are useful in determining the gaze pattern. The accuracy and computational efficiency of the proposed technique is demonstrated by experiments with human subjects

A SOM-based Chan–Vese model for unsupervised image segmentation

Active Contour Models (ACMs) constitute an efficient energy-based image segmentation framework. They usually deal with the segmentation problem as an optimization problem, formulated in terms of a suitable functional, constructed in such a way that its minimum is achieved in correspondence with a contour that is a close approximation of the actual object boundary. However, for existing ACMs, handling images that contain objects characterized by many different intensities still represents a challenge. In this paper, we propose a novel ACM that combines—in a global and unsupervised way—the advantages of the Self-Organizing Map (SOM) within the level set framework of a state-of-the-art unsupervised global ACM, the Chan–Vese (C–V) model. We term our proposed model SOM-based Chan– Vese (SOMCV) active contourmodel. It works by explicitly integrating the global information coming from the weights (prototypes) of the neurons in a trained SOM to help choosing whether to shrink or expand the current contour during the optimization process, which is performed in an iterative way. The proposed model can handle images that contain objects characterized by complex intensity distributions, and is at the same time robust to the additive noise. Experimental results show the high accuracy of the segmentation results obtained by the SOMCV model on several synthetic and real images, when compared to the Chan–Vese model and other image segmentation models

Magnitude Sensitive Competitive Neural Networks

En esta Tesis se presentan un conjunto de redes neuronales llamadas Magnitude Sensitive Competitive Neural Networks (MSCNNs). Se trata de un conjunto de algoritmos de Competitive Learning que incluyen un término de magnitud como un factor de modulación de la distancia usada en la competición. Al igual que otros métodos competitivos, MSCNNs realizan la cuantización vectorial de los datos, pero el término de magnitud guía el entrenamiento de los centroides de modo que se representan con alto detalle las zonas deseadas, definidas por la magnitud. Estas redes se han comparado con otros algoritmos de cuantización vectorial en diversos ejemplos de interpolación, reducción de color, modelado de superficies, clasificación, y varios ejemplos sencillos de demostración. Además se introduce un nuevo algoritmo de compresión de imágenes, MSIC (Magnitude Sensitive Image Compression), que hace uso de los algoritmos mencionados previamente, y que consigue una compresión de la imagen variable según una magnitud definida por el usuario. Los resultados muestran que las nuevas redes neuronales MSCNNs son más versátiles que otros algoritmos de aprendizaje competitivo, y presentan una clara mejora en cuantización vectorial sobre ellos cuando el dato está sopesado por una magnitud que indica el ¿interés¿ de cada muestra

GIS applications for poverty targeted aquaculture development in the lower Mekong Basin.

In the lower Mekong Basin, marginal socio-economic conditions prevail amongst rural small scale farming households which heavily depend on highly seasonal, rain-fed farming systems for their livelihood. Persistent rural poverty is aggravated by frequently occurring droughts and floods. A yearly flood-drought cycle, while essential to their household economy based on rice and fisheries, renders rural poor livelihoods vulnerable to recurrent periods of food insecurity. This research demonstrates how a combination of publicly accessible Remote Sensing imagery and disaggregated poverty maps, within a comprehensive rural development framework, can provide an effective method to target pro-poor aquaculture development interventions at the local level. An agro-ecosystems analysis is performed in order to capture the seasonal dynamics of water- and aquatic resource exploitation. A holistic farming systems approach emphasises the potential of ponds in integrated rural smallholder systems to reduce poverty and vulnerability under rain fed conditions. A Geographic Information System (GIS), an efficient spatial inventory tool and decision support system in resolving real world problems, is used to identify where rural poor households can potentially benefit from the integration of aquaculture into existing production systems. A time series of satellite derived vegetation index data reveals distinct agro-ecosystem seasonality over large parts of the study area, which is indicative for farming systems under rain fed conditions. The developed methodology is capable of identifying functionally different agro-ecosystems. Socio-economic indicators for Cambodian parts of the lowland areas point to widespread rural poverty and vulnerability to recurrent food insecurity, which is directly related to agro-ecosystems seasonality and annual climate variability. Dependence of farming households on low productivity rain fed rice agro-ecosystems in Cambodia’s southern provinces is in stark contrast to the highly productive farming systems directly bordering it, in the freshwater fluvial zone of the Vietnamese Mekong Delta. A rapid increase in rice productivity in this densely populated area went hand-in hand with a considerable reduction in rural poverty. In this flood-prone but fertile area, resource competition and falling market prices of rice may have prompted the development of a range of integrated farming systems. The incorporation of ponds on farm in these systems facilitates reuse of nutrients from farm by-products for low-input aquatic resource production. In Northeast Thailand, crop production and low-input aquaculture have been successfully integrated along a tradition of water- and living aquatic resources management in farmer managed systems under resource poor conditions. A spatially linked commune level rural development database for Sisaket province in Northeast Thailand provides a useful framework for planning of aquaculture development through systems that are appropriate and relevant to local socio-economic and agro-ecological conditions. It was concluded that the socio-economic and agro-ecological context of rural poverty in Southeast Cambodia offers scope for similar pathways to improve rural wellbeing and reduce vulnerability to poverty and food insecurity by integrating aquatic resources development in pond based systems as part of an interdisciplinary approach towards rural development

Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background