Combining local features and region segmentation: methods and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 23-01-2020Esta tesis tiene embargado el acceso al texto completo hasta el 23-07-2021Muchas y muy diferentes son las propuestas que se han desarrollado en el área de la visión artificial para la extracción de información de las imágenes y su posterior uso. Entra las más destacadas se encuentran las conocidas como características locales, del inglés local features, que detectan puntos o áreas de la imagen con ciertas características de interés, y las describen usando información de su entorno (local). También destacan las regiones en este área, y en especial este trabajo se ha centrado en los segmentadores en regiones, cuyo objetivo es agrupar la información de la imagen atendiendo a diversos criterios. Pese al enorme potencial de estas técnicas, y su probado éxito en diversas aplicaciones, su definición lleva implícita una serie de limitaciones funcionales que les han impedido exportar sus capacidades a otras áreas de aplicación. Se pretende impulsar el uso de estas herramientas en dichas aplicaciones, y por tanto mejorar los resultados del estado del arte, mediante la propuesta de un marco de desarrollo de nuevas soluciones. En concreto, la hipótesis principal del proyecto es que las capacidades de las características locales y los segmentadores en regiones son complementarias, y que su combinación, realizada de la forma adecuada, las maximiza a la vez que minimiza sus limitaciones. El principal objetivo, y por tanto la principal contribución del proyecto, es validar dicha hipótesis mediante la propuesta de un marco de desarrollo de nuevas soluciones combinando características locales y segmentadores para técnicas con capacidades mejoradas. Al tratarse de un marco de combinación de dos técnicas, el proceso de validación se ha llevado a cabo en dos pasos. En primer lugar se ha planteado el caso del uso de segmentadores en regiones para mejorar las características locales. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, SP-SIFT, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de algoritmos de seguimiento de objetos. En segundo lugar, se ha planteado el caso de uso de características locales para mejorar los segmentadores en regiones. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, LF-SLIC, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de un algoritmo de segmentación de lesiones pigmentadas de la piel. Los resultados conceptuales han probado que las técnicas mejoran a nivel de capacidades. Los resultados aplicados han probado que estas mejoras permiten el uso de estas técnicas en aplicaciones donde antes no tenían éxito. Con ello, se ha considerado la hipótesis validada, y por tanto exitosa la definición de un marco para el desarrollo de nuevas técnicas específicas con capacidades mejoradas. En conclusión, la principal aportación de la tesis es el marco de combinación de técnicas, plasmada en sus dos propuestas específicas: características locales mejoradas con segmentadores y segmentadores mejorados con características locales, y en el éxito conseguido en sus aplicaciones.A huge number of proposals have been developed in the area of computer vision for information extraction from images, and its further use. One of the most prevalent solutions are those known as local features. They detect points or areas of the image with certain characteristics of interest, and describe them using information from their (local) environment. The regions also stand out in the area, and especially this work has focused on the region segmentation algorithms, whose objective is to group the information of the image according to di erent criteria. Despite the enormous potential of these techniques, and their proven success in a number of applications, their de nition implies a series of functional limitations that have prevented them from exporting their capabilities to other application areas. In this thesis, it is intended to promote the use of these tools in these applications, and therefore improve the results of the state of the art, by proposing a framework for developing new solutions. Speci cally, the main hypothesis of the project is that the capacities of the local features and the region segmentation algorithms are complementary, and thus their combination, carried out in the right way, maximizes them while minimizing their limitations. The main objective, and therefore the main contribution of the thesis, is to validate this hypothesis by proposing a framework for developing new solutions combining local features and region segmentation algorithms, obtaining solutions with improved capabilities. As the hypothesis is proposing to combine two techniques, the validation process has been carried out in two steps. First, the use case of region segmentation algorithms enhancing local features. In order to verify the viability and success of this combination, a speci c proposal, SP-SIFT, was been developed. This proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of object tracking algorithms. Second, the use case of enhancing region segmentation algorithm with local features. In order to verify the viability and success of this combination, a speci c proposal, LF-SLIC, was developed. The proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of a pigmented skin lesions segmentation algorithm. The conceptual results proved that the techniques improve at the capabilities level. The application results proved that these improvements allow the use of this techniques in applications where they were previously unsuccessful. Thus, the hypothesis can be considered validated, and therefore the de nition of a framework for the development of new techniques with improved capabilities can be considered successful. In conclusion, the main contribution of the thesis is the framework for the combination of techniques, embodied in the two speci c proposals: enhanced local features with region segmentation algorithms, and region segmentation algorithms enhanced with local features; and in the success achieved in their applications.The work described in this Thesis was carried out within the Video Processing and Understanding Lab at the Department of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid (from 2014 to 2019). It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo)

Scene understanding for interactive applications

Para interactuar con el entorno, es necesario entender que está ocurriendo en la escena donde se desarrolla la acción. Décadas de investigación en el campo de la visión por computador han contribuido a conseguir sistemas que permiten interpretar de manera automática el contenido en una escena a partir de información visual. Se podría decir el objetivo principal de estos sistemas es replicar la capacidad humana para extraer toda la información a partir solo de datos visuales. Por ejemplo, uno de sus objetivos es entender como percibimosel mundo en tres dimensiones o como podemos reconocer sitios y objetos a pesar de la gran variación en su apariencia. Una de las tareas básicas para entender una escena es asignar un significado semántico a cada elemento (píxel) de una imagen. Esta tarea se puede formular como un problema de etiquetado denso el cual especifica valores (etiquetas) a cada pixel o región de una imagen. Dependiendo de la aplicación, estas etiquetas puedenrepresentar conceptos muy diferentes, desde magnitudes físicas como la información de profundidad, hasta información semántica, como la categoría de un objeto. El objetivo general en esta tesis es investigar y desarrollar nuevas técnicas para incorporar automáticamente una retroalimentación por parte del usuario, o un conocimiento previo en sistemas inteligente para conseguir analizar automáticamente el contenido de una escena. en particular,esta tesis explora dos fuentes comunes de información previa proporcionado por los usuario: interacción humana y etiquetado manual de datos de ejemplo.La primera parte de esta tesis esta dedicada a aprendizaje de información de una escena a partir de información proporcionada de manera interactiva por un usuario. Las soluciones que involucran a un usuario imponen limitaciones en el rendimiento, ya que la respuesta que se le da al usuario debe obtenerse en un tiempo interactivo. Esta tesis presenta un paradigma eficiente que aproxima cualquier magnitud por píxel a partir de unos pocos trazos del usuario. Este sistema propaga los escasos datos de entrada proporcionados por el usuario a cada píxel de la imagen. El paradigma propuesto se ha validado a través detres aplicaciones interactivas para editar imágenes, las cuales requieren un conocimiento por píxel de una cierta magnitud, con el objetivo de simular distintos efectos.Otra estrategia común para aprender a partir de información de usuarios es diseñar sistemas supervisados de aprendizaje automático. En los últimos años, las redes neuronales convolucionales han superado el estado del arte de gran variedad de problemas de reconocimiento visual. Sin embargo, para nuevas tareas, los datos necesarios de entrenamiento pueden no estar disponibles y recopilar suficientes no es siempre posible. La segunda parte de esta tesis explora como mejorar los sistema que aprenden etiquetado denso semántico a partir de imágenes previamente etiquetadas por los usuarios. En particular, se presenta y validan estrategias, basadas en los dos principales enfoques para transferir modelos basados en deep learning, para segmentación semántica, con el objetivo de poder aprender nuevas clases cuando los datos de entrenamiento no son suficientes en cantidad o precisión.Estas estrategias se han validado en varios entornos realistas muy diferentes, incluyendo entornos urbanos, imágenes aereas y imágenes submarinas.In order to interact with the environment, it is necessary to understand what is happening on it, on the scene where the action is ocurring. Decades of research in the computer vision field have contributed towards automatically achieving this scene understanding from visual information. Scene understanding is a very broad area of research within the computer vision field. We could say that it tries to replicate the human capability of extracting plenty of information from visual data. For example, we would like to understand how the people perceive the world in three dimensions or can quickly recognize places or objects despite substantial appearance variation. One of the basic tasks in scene understanding from visual data is to assign a semantic meaning to every element of the image, i.e., assign a concept or object label to every pixel in the image. This problem can be formulated as a dense image labeling problem which assigns specific values (labels) to each pixel or region in the image. Depending on the application, the labels can represent very different concepts, from a physical magnitude, such as depth information, to high level semantic information, such as an object category. The general goal in this thesis is to investigate and develop new ways to automatically incorporate human feedback or prior knowledge in intelligent systems that require scene understanding capabilities. In particular, this thesis explores two common sources of prior information from users: human interactions and human labeling of sample data. The first part of this thesis is focused on learning complex scene information from interactive human knowledge. Interactive user solutions impose limitations on the performance where the feedback to the user must be at interactive rates. This thesis presents an efficient interaction paradigm that approximates any per-pixel magnitude from a few user strokes. It propagates the sparse user input to each pixel of the image. We demonstrate the suitability of the proposed paradigm through three interactive image editing applications which require per-pixel knowledge of certain magnitude: simulate the effect of depth of field, dehazing and HDR tone mapping. Other common strategy to learn from user prior knowledge is to design supervised machine-learning approaches. In the last years, Convolutional Neural Networks (CNNs) have pushed the state-of-the-art on a broad variety of visual recognition problems. However, for new tasks, enough training data is not always available and therefore, training from scratch is not always feasible. The second part of this thesis investigates how to improve systems that learn dense semantic labeling of images from user labeled examples. In particular, we present and validate strategies, based on common transfer learning approaches, for semantic segmentation. The goal of these strategies is to learn new specific classes when there is not enough labeled data to train from scratch. We evaluate these strategies across different environments, such as autonomous driving scenes, aerial images or underwater ones.<br /

Multigranularity Representations for Human Inter-Actions: Pose, Motion and Intention

Tracking people and their body pose in videos is a central problem in computer vision. Standard tracking representations reason about temporal coherence of detected people and body parts. They have difficulty tracking targets under partial occlusions or rare body poses, where detectors often fail, since the number of training examples is often too small to deal with the exponential variability of such configurations. We propose tracking representations that track and segment people and their body pose in videos by exploiting information at multiple detection and segmentation granularities when available, whole body, parts or point trajectories. Detections and motion estimates provide contradictory information in case of false alarm detections or leaking motion affinities. We consolidate contradictory information via graph steering, an algorithm for simultaneous detection and co-clustering in a two-granularity graph of motion trajectories and detections, that corrects motion leakage between correctly detected objects, while being robust to false alarms or spatially inaccurate detections. We first present a motion segmentation framework that exploits long range motion of point trajectories and large spatial support of image regions. We show resulting video segments adapt to targets under partial occlusions and deformations. Second, we augment motion-based representations with object detection for dealing with motion leakage. We demonstrate how to combine dense optical flow trajectory affinities with repulsions from confident detections to reach a global consensus of detection and tracking in crowded scenes. Third, we study human motion and pose estimation. We segment hard to detect, fast moving body limbs from their surrounding clutter and match them against pose exemplars to detect body pose under fast motion. We employ on-the-fly human body kinematics to improve tracking of body joints under wide deformations. We use motion segmentability of body parts for re-ranking a set of body joint candidate trajectories and jointly infer multi-frame body pose and video segmentation. We show empirically that such multi-granularity tracking representation is worthwhile, obtaining significantly more accurate multi-object tracking and detailed body pose estimation in popular datasets

Online Structured Learning for Real-Time Computer Vision Gaming Applications

In recent years computer vision has played an increasingly important role in the development of computer games, and it now features as one of the core technologies for many gaming platforms. The work in this thesis addresses three problems in real-time computer vision, all of which are motivated by their potential application to computer games. We rst present an approach for real-time 2D tracking of arbitrary objects. In common with recent research in this area we incorporate online learning to provide an appearance model which is able to adapt to the target object and its surrounding background during tracking. However, our approach moves beyond the standard framework of tracking using binary classication and instead integrates tracking and learning in a more principled way through the use of structured learning. As well as providing a more powerful framework for adaptive visual object tracking, our approach also outperforms state-of-the-art tracking algorithms on standard datasets. Next we consider the task of keypoint-based object tracking. We take the traditional pipeline of matching keypoints followed by geometric verication and show how this can be embedded into a structured learning framework in order to provide principled adaptivity to a given environment. We also propose an approximation method allowing us to take advantage of recently developed binary image descriptors, meaning our approach is suitable for real-time application even on low-powered portable devices. Experimentally, we clearly see the benet that online adaptation using structured learning can bring to this problem. Finally, we present an approach for approximately recovering the dense 3D structure of a scene which has been mapped by a simultaneous localisation and mapping system. Our approach is guided by the constraints of the low-powered portable hardware we are targeting, and we develop a system which coarsely models the scene using a small number of planes. To achieve this, we frame the task as a structured prediction problem and introduce online learning into our approach to provide adaptivity to a given scene. This allows us to use relatively simple multi-view information coupled with online learning of appearance to efficiently produce coarse reconstructions of a scene