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

Relationship between Heart Rate and the Scoreboard during a Relegation Playoff

En los deportes de equipo, el estudio de la frecuencia cardíaca (FC) resulta fundamental para la optimización del rendimiento y la prevención de lesiones, ya que nos permite conocer algunas de las demandas fisiológicas generadas por la práctica deportiva y el nivel de carga interna. El objetivo del presente estudio fue conocer la relación existente entre la FC y el marcador del partido, así como el número de acciones que se realizan en cada uno de los estratos generados, en función de la diferencia de puntos en el marcador en jugadoras de baloncesto amateur durante una fase de descenso. Para ello, se analizó la FC siguiendo los criterios propuestos por McInnes, Carlson, Jones y McKenna (1995) en una muestra de diez jugadoras (n = 10) de Copa Catalunya, durante los 10 partidos oficiales de una fase de descenso. Seguidamente, se relacionó la FC con el marcador y con la duración de las acciones de juego en cada momento del partido. Los resultados muestran valores de FC que fluctúan entre el 88.9% y el 92.2% de la FC máx. Además, se observaron diferencias significativas en cuanto a la diferencia en el marcador (p < 0.05), en cada uno de los 3 estratos analizados. En cuanto a la relación con las variables temporales de juego, se observaron diferencias significativas únicamente en las posesiones largas (17-24 s) (p < 0.01). Las conclusiones de este estudio sugieren que la diferencia de puntos en el marcador y las acciones de juego de duración comprendida entre los 17-24 s tienen una influencia directa sobre la FC, modificándose esta en función de la diferencia de puntos e influyendo en las demandas fisiológicas de las jugadoras.In team sports, the study of heart rate (HR) is fundamental for optimizing performance and preventing injuries since it allows us to learn about some of the physiological demands generated by doing sport and the level of internal load. The purpose of this study was to explore the relationship between HR and the scoreboard for the game as well as the number of actions performed in each of the strata generated depending on the difference in points on the scoreboard in amateur basketball players during a relegation playoff. To do this HR was analyzed, following the principles proposed by McInnes, Carlson, Jones and McKenna (1995), in a sample of ten players (n = 10) in the Catalan Cup during the 10 competitive matches during a relegation playoff. Next, HR was related to the scoreboard and the duration of play actions at each moment during the game. The results show HR values that range between 88.9% and 92.2% of HR max. In addition, significant differences were observed with respect to the points difference on the scoreboard (p < 0.05) in each of the 3 strata analyzed. As for the relationship with the play time variables, significant differences were observed only in long possessions (17-24 s) (p < 0.01). The conclusions of this study suggest that the difference in points on the scoreboard and play actions lasting 17-24 s have a direct impact on HR, which changes as a function of the points difference and influences the physiological demands on the players

Sistema de seguimiento y análisis de la calidad del agua para consumo humano mediante el estudio de la respuesta comportamental en peces expuestos a sustancias tóxicas

Behavioral alterations can be assessed as variables for sublethal toxicity tests, and serve as a tool for environmental risk assessment and analysis of toxicological impact. In order to investigate contaminant-induced behavioral alterations in fish, a video analysis system was designed to obtain relevant behavioral variables. Data from fish exposed to a reference toxicant, organophosphorus pesticide chlorpyrifos, are presented to exemplify alterations in fish behavior associated with exposure to this pesticide. The developed system provides valuable information on parameters associated with fish behavior and can be used to identify characteristic behavioral responses to a variety of toxicants and assist in risk assessment.La obtención de variables relacionadas con cambios comportamentales en los estudios sobre los efectos subletales de sustancias tóxicas en animales, constituyen una herramienta fundamental para la evaluación del riesgo ambiental y el análisis del impacto de sustancias tóxicas. En el presente trabajo se presenta un sistema de seguimiento y análisis mediante técnicas de visión artificial que permite cuantificar alteraciones en el comportamiento en peces expuestos a concentraciones subletales de sustancias tóxicas. El sistema permite la obtención de una serie de variables comportamentales a partir de un grupo de peces expuestos a una sustancia tóxica, en este caso el insecticida organofosforado clorpirifos, que posteriormente son analizadas para comprobar su desviación frente a un grupo control. Los resultados obtenidos muestran como las variables registradas proporcionan información muy valiosa sobre el comportamiento en peces y constatan que dicho sistema puede ser utilizado para caracterizar las respuestas comportamentales frente a la exposición de sustancias tóxicas y en la evaluación del riesgo ambiental

Estimación y segmentación de modelos de movimiento paramétricos mediante mínimos cuadrados generalizados

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

Team activity recognition in Association Football using a Bag-of-Words-based method

In this paper, a new methodology is used to perform team activity recognition and analysis in Association Football. It is based on pattern recognition and machine learning techniques. In particular, a strategy based on the Bag-of-Words (BoW) technique is used to characterize short Football video clips that are used to explain the team’s performance and to train advanced classifiers in automatic recognition of team activities. In addition to the neural network-based classifier, three more classifier families are tested: the k-Nearest Neighbor, the Support Vector Machine and the Random Forest. The results obtained show that the proposed methodology is able to explain the most common movements of a team and to perform the team activity recognition task with high accuracy when classifying three Football actions: Ball Possession, Quick Attack and Set Piece. Random Forest is the classifier obtaining the best classification results

ATM-based analysis and recognition of handball team activities

In this paper, a new methodology based on the Author Topic Model (ATM) method is presented to perform team activity recognition and analysis in handball videos. Instead of using players׳ trajectories we just rely on low level features related to local motion, the evolution of which is then modeled over time by the ATM. The proposed methodology is applied to the task of recognizing four kinds of team activities in handball videos from the CVBASE׳06 dataset and to analyze which are the most important elements of the activities. Our method is compared with two other ways of characterizing videos based on Bag-of-Words (BoW) and Latent Dirichlet Allocation (LDA) techniques. Our proposal obtains competitive results in terms of accuracy, computing time and interpretation of the results.The authors acknowledge the Fundació Caixa-Castelló Bancaixa under project P1-1A2010-11

Water quality monitoring and analysis system through study of fish behavioral response to toxic compounds

La obtención de variables relacionadas con cambios comportamentales en los estudios sobre los efectos subletales de sustancias tóxicas en animales, constituyen una herramienta fundamental para la evaluación del riesgo ambiental y el análisis del impacto de sustancias tóxicas. En el presente trabajo se presenta un sistema de seguimiento y análisis mediante técnicas de visión artificial que permite cuantificar alteraciones en el comportamiento en peces expuestos a concentraciones subletales de sustancias tóxicas. El sistema permite la obtención de una serie de variables comportamentales a partir de un grupo de peces expuestos a una sustancia tóxica, en este caso el insecticida organofosforado clorpirifos, que posteriormente son analizadas para comprobar su desviación frente a un grupo control. Los resultados obtenidos muestran como las variables registradas proporcionan información muy valiosa sobre el comportamiento en peces y constatan que dicho sistema puede ser utilizado para caracterizar las respuestas comportamentales frente a la exposición de sustancias tóxicas y en la evaluación del riesgo ambiental

Balance hídrico en jugadoras amateur de baloncesto: seguimiento en 10 partidos = Fluid balance in amateur female basketball players: Follow-up of 10 games

Introduction: The aim of the present study was to understand the development of hydration needs during a playoff stint, in a match situation, for an amateur women's basketball team. In addition, the existence of possible differences in the level of dehydration according to playing position was analyzed. Material and Methods: Pilot study with single group measurements before competition and after competition was done. Both amateur basketball players (n=10) and their personal water containers were weighted before and after every match. Weight variations were evaluated considering the ingested liquid and replenishment liquid discharged through urine. Results: Results showed that there are significant differences between body mass before and after each match (z=8.551; p < .0005). However, magnitude of dehydration was very different in each player and match, with average values between 0.63kg (0.9% body weight) and 0.95kg (1.37% body weight). In addition, there were not significant differences in the level of dehydration according to the playing position (F = 1.59; p = 0.1929). Conclusions: These evaluations confirm high intra and interpersonal variations as per body mass loss during the 10 referred playoff stint matches. Recommendation of creation of individualized hydration protocols is suggestedIntroducción: El objetivo del presente estudio fue analizar la evolución de las necesidades hídricas de un equipo de baloncesto femenino amateur, en situación de partido y durante toda una fase eliminatoria, a partir de la pérdida de peso experimentada. Además, se analizó la existencia de posibles diferencias en el nivel de deshidratación en función de la posición de juego. Material y Métodos: Estudio piloto de un solo grupo con mediciones antes de la competición y después de la competición. Jugadoras amateurs de baloncesto (n=10) y sus recipientes de rehidratación fueron pesadas antes y después de los 10 partidos analizados. Las variaciones de peso fueron evaluadas teniendo en cuenta el líquido ingerido de los bidones de reposición y el líquido evacuado a través de la orina. Resultados: Los resultados de este estudio sugieren la existencia de diferencias significativas entre el peso corporal antes y después de cada partido (z=8,551; p<,0005). La magnitud de estas diferencias parece ser muy distinta en función del partido y la jugadora analizada, con valores medios que oscilan entre los 0,63kg (0,9% Peso Corporal) y los 0,95kg (1,37% Peso Corporal) de pérdida de peso corporal. Además, no se observaron diferencias significativas en el nivel de deshidratación en función de la posición de juego (F=1,59; p=0,1929). Conclusiones: Se confirma la existencia de una alta variabilidad intra e interpersonal en cuanto a la pérdida de masa corporal durante los 10 partidos analizados, lo que sugiere la necesidad de una monitorización y rehidratación individualizad