A TWO-STEP ESTIMATOR FOR A SPATIAL LAG MODEL OF COUNTS: THEORY, SMALL SAMPLE PERFORMANCE AND AN APPLICATION

Several spatial econometric approaches are available to model spatially correlated disturbances in count models, but there are at present no structurally consistent count models incorporating spatial lag autocorrelation. A two-step, limited information maximum likelihood estimator is proposed to fill this gap. The estimator is developed assuming a Poisson distribution, but can be extended to other count distributions. The small sample properties of the estimator are evaluated with Monte Carlo experiments. Simulation results suggest that the spatial lag count estimator achieves gains in terms of bias over the aspatial version as spatial lag autocorrelation and sample size increase. An empirical example deals with the location choice of single-unit start-up firms in the manufacturing industry in the US between 2000 and 2004. The empirical results suggest that in the dynamic process of firm formation, counties dominated by firms exhibiting (internal) increasing returns to scale are at a relative disadvantage even if localization economies are presentcount model, location choice, manufacturing, Poisson, spatial econometrics

A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized

The European Enlargement Process and Regional Convergence Revisited: Spatial Effects Still Matter.

This paper has two main goals. First, it reconsiders regional growth and convergence processes in the context of the enlargement of the European Union to new member states. We show that spatial autocorrelation and heterogeneity still matter in a sample of 237 regions over the period 1993-2002. Spatial convergence clubs are defined using exploratory spatial data analysis and a spatial autoregressive model is estimated. We find strong evidence that the growth rate of per capita GDP for a given region is positively affected by the growth rate of neighbouring regions. The second objective is to test the robustness of the results with respect to non-normality, outliers and heteroskedasticity using two other methods: The quasi maximum Likelihood and the Bayesian estimation methods.

Generalized least squares-based parametric motion estimation and segmentation

El análisis del movimiento es uno de los campos más importantes de la visión por computador. Esto es debido a que el mundo real está en continuo movimiento y es obvio que podremos obtener mucha más información de escenas en movimiento que de escenas estáticas. En esta tesis se ha trabajado principalmente en desarrollar algoritmos de estimación de movimiento para su aplicación a problemas de registrado de imágenes y a problemas de segmentación del movimiento. Uno de los principales objetivos de este trabajo es desarrollar una técnica de registrado de imágenes de gran exactitud, tolerante a outliers y que sea capaz de realizar su labor incluso en la presencia de deformaciones de gran magnitud tales como traslaciones, rotaciones, cambios de escala, cambios de iluminación globales y no espacialmente uniformes, etc. Otro de los objetivos de esta tesis es trabajar en problemas de estimación y la segmentación del movimiento en secuencias de dos imágenes de forma casi simultánea y sin conocimiento a priori del número de modelos de movimiento presentes. Los experimentos mostrados en este trabajo demuestran que los algoritmos propuestos en esta tesis obtienen resultados de gran exactitud.This thesis proposes several techniques related with the motion estimation problem. In particular, it deals with global motion estimation for image registration and motion segmentation. In the first case, we will suppose that the majority of the pixels of the image follow the same motion model, although the possibility of a large number of outliers are also considered. In the motion segmentation problem, the presence of more than one motion model will be considered. In both cases, sequences of two consecutive grey level images will be used. A new generalized least squares-based motion estimator will be proposed. The proposed formulation of the motion estimation problem provides an additional constraint that helps to match the pixels using image gradient information. That is achieved thanks to the use of a weight for each observation, providing high weight values to the observations considered as inliers, and low values to the ones considered as outliers. To avoid falling in a local minimum, the proposed motion estimator uses a Feature-based method (SIFT-based) to obtain good initial motion parameters. Therefore, it can deal with large motions like translation, rotations, scales changes, viewpoint changes, etc. The accuracy of our approach has been tested using challenging real images using both affine and projective motion models. Two Motion Estimator techniques, which use M-Estimators to deal with outliers into a iteratively reweighted least squared-based strategy, have been selected to compare the accuracy of our approach. The results obtained have showed that the proposed motion estimator can obtain as accurate results as M-Estimator-based techniques and even better in most cases. The problem of estimating accurately the motion under non-uniform illumination changes will also be considered. A modification of the proposed global motion estimator will be proposed to deal with this kind of illumination changes. In particular, a dynamic image model where the illumination factors are functions of the localization will be used replacing the brightens constancy assumption allowing for a more general and accurate image model. Experiments using challenging images will be performed showing that the combination of both techniques is feasible and provides accurate estimates of the motion parameters even in the presence of strong illumination changes between the images. The last part of the thesis deals with the motion estimation and segmentation problem. The proposed algorithm uses temporal information, by using the proposed generalized least-squares motion estimation process and spatial information by using an iterative region growing algorithm which classifies regions of pixels into the different motion models present in the sequence. In addition, it can extract the different moving regions of the scene while estimating its motion quasi-simultaneously and without a priori information of the number of moving objects in the scene. The performance of the algorithm will be tested on synthetic and real images with multiple objects undergoing different types of motion

Generalized least squares-based parametric motion estimation and segmentation

El análisis del movimiento es uno de los campos más importantes de la visión por computador. Esto es debido a que el mundo real está en continuo movimiento y es obvio que podremos obtener mucha más información de escenas en movimiento que de escenas estáticas. En esta tesis se ha trabajado principalmente en desarrollar algoritmos de estimación de movimiento para su aplicación a problemas de registrado de imágenes y a problemas de segmentación del movimiento. Uno de los principales objetivos de este trabajo es desarrollar una técnica de registrado de imágenes de gran exactitud, tolerante a outliers y que sea capaz de realizar su labor incluso en la presencia de deformaciones de gran magnitud tales como traslaciones, rotaciones, cambios de escala, cambios de iluminación globales y no espacialmente uniformes, etc. Otro de los objetivos de esta tesis es trabajar en problemas de estimación y la segmentación del movimiento en secuencias de dos imágenes de forma casi simultánea y sin conocimiento a priori del número de modelos de movimiento presentes. Los experimentos mostrados en este trabajo demuestran que los algoritmos propuestos en esta tesis obtienen resultados de gran exactitud.This thesis proposes several techniques related with the motion estimation problem. In particular, it deals with global motion estimation for image registration and motion segmentation. In the first case, we will suppose that the majority of the pixels of the image follow the same motion model, although the possibility of a large number of outliers are also considered. In the motion segmentation problem, the presence of more than one motion model will be considered. In both cases, sequences of two consecutive grey level images will be used. A new generalized least squares-based motion estimator will be proposed. The proposed formulation of the motion estimation problem provides an additional constraint that helps to match the pixels using image gradient information. That is achieved thanks to the use of a weight for each observation, providing high weight values to the observations considered as inliers, and low values to the ones considered as outliers. To avoid falling in a local minimum, the proposed motion estimator uses a Feature-based method (SIFT-based) to obtain good initial motion parameters. Therefore, it can deal with large motions like translation, rotations, scales changes, viewpoint changes, etc. The accuracy of our approach has been tested using challenging real images using both affine and projective motion models. Two Motion Estimator techniques, which use M-Estimators to deal with outliers into a iteratively reweighted least squared-based strategy, have been selected to compare the accuracy of our approach. The results obtained have showed that the proposed motion estimator can obtain as accurate results as M-Estimator-based techniques and even better in most cases. The problem of estimating accurately the motion under non-uniform illumination changes will also be considered. A modification of the proposed global motion estimator will be proposed to deal with this kind of illumination changes. In particular, a dynamic image model where the illumination factors are functions of the localization will be used replacing the brightens constancy assumption allowing for a more general and accurate image model. Experiments using challenging images will be performed showing that the combination of both techniques is feasible and provides accurate estimates of the motion parameters even in the presence of strong illumination changes between the images. The last part of the thesis deals with the motion estimation and segmentation problem. The proposed algorithm uses temporal information, by using the proposed generalized least-squares motion estimation process and spatial information by using an iterative region growing algorithm which classifies regions of pixels into the different motion models present in the sequence. In addition, it can extract the different moving regions of the scene while estimating its motion quasi-simultaneously and without a priori information of the number of moving objects in the scene. The performance of the algorithm will be tested on synthetic and real images with multiple objects undergoing different types of motion