Activity profiling for minimally invasive surgery

Imperial Users onl

Computational Multimedia for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of oneself. This is the idea behind the psychological theory of self-efficacy - you can learn or model to perform certain tasks because you see yourself doing it, which provides the most ideal form of behavior modeling. The effectiveness of VSM has been demonstrated for many different types of disabilities and behavioral problems ranging from stuttering, inappropriate social behaviors, autism, selective mutism to sports training. However, there is an inherent difficulty associated with the production of VSM material. Prolonged and persistent video recording is required to capture the rare, if not existed at all, snippets that can be used to string together in forming novel video sequences of the target skill. To solve this problem, in this dissertation, we use computational multimedia techniques to facilitate the creation of synthetic visual content for self-modeling that can be used by a learner and his/her therapist with a minimum amount of training data. There are three major technical contributions in my research. First, I developed an Adaptive Video Re-sampling algorithm to synthesize realistic lip-synchronized video with minimal motion jitter. Second, to denoise and complete the depth map captured by structure-light sensing systems, I introduced a layer based probabilistic model to account for various types of uncertainties in the depth measurement. Third, I developed a simple and robust bundle-adjustment based framework for calibrating a network of multiple wide baseline RGB and depth cameras

Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Doctor of Philosophy. Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with the resulting loss of speech. With recent advances in portable computing power, automatic lip-reading (ALR) may become a viable approach to voice restoration. This thesis addresses the image processing aspect of ALR, and focuses three contributions to colour-based lip segmentation. The rst contribution concerns the colour transform to enhance the contrast between the lips and skin. This thesis presents the most comprehensive study to date by measuring the overlap between lip and skin histograms for 33 di erent colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%, and results show that selecting the correct transform can increase the segmentation accuracy by up to three times. The second contribution is the development of a new lip segmentation algorithm that utilises the best colour transforms from the comparative study. The algorithm is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation error (SE) of 7:39 %. The third contribution focuses on the impact of the histogram threshold on the segmentation accuracy, and introduces a novel technique called Adaptive Threshold Optimisation (ATO) to select a better threshold value. The rst stage of ATO incorporates -SVR to train the lip shape model. ATO then uses feedback of shape information to validate and optimise the threshold. After applying ATO, the SE decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp or relative improvement of 15:1%. While this thesis concerns lip segmentation in particular, ATO is a threshold selection technique that can be used in various segmentation applications.MT201

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 /

Exploring the Internal Statistics: Single Image Super-Resolution, Completion and Captioning

Image enhancement has drawn increasingly attention in improving image quality or interpretability. It aims to modify images to achieve a better perception for human visual system or a more suitable representation for further analysis in a variety of applications such as medical imaging, remote sensing, and video surveillance. Based on different attributes of the given input images, enhancement tasks vary, e.g., noise removal, deblurring, resolution enhancement, prediction of missing pixels, etc. The latter two are usually referred to as image super-resolution and image inpainting (or completion). Image super-resolution and completion are numerically ill-posed problems. Multi-frame-based approaches make use of the presence of aliasing in multiple frames of the same scene. For cases where only one input image is available, it is extremely challenging to estimate the unknown pixel values. In this dissertation, we target at single image super-resolution and completion by exploring the internal statistics within the input image and across scales. An internal gradient similarity-based single image super-resolution algorithm is first presented. Then we demonstrate that the proposed framework could be naturally extended to accomplish super-resolution and completion simultaneously. Afterwards, a hybrid learning-based single image super-resolution approach is proposed to benefit from both external and internal statistics. This framework hinges on image-level hallucination from externally learned regression models as well as gradient level pyramid self-awareness for edges and textures refinement. The framework is then employed to break the resolution limitation of the passive microwave imagery and to boost the tracking accuracy of the sea ice movements. To extend our research to the quality enhancement of the depth maps, a novel system is presented to handle circumstances where only one pair of registered low-resolution intensity and depth images are available. High quality RGB and depth images are generated after the system. Extensive experimental results have demonstrated the effectiveness of all the proposed frameworks both quantitatively and qualitatively. Different from image super-resolution and completion which belong to low-level vision research, image captioning is a high-level vision task related to the semantic understanding of an input image. It is a natural task for human beings. However, image captioning remains challenging from a computer vision point of view especially due to the fact that the task itself is ambiguous. In principle, descriptions of an image can talk about any visual aspects in it varying from object attributes to scene features, or even refer to objects that are not depicted and the hidden interaction or connection that requires common sense knowledge to analyze. Therefore, learning-based image captioning is in general a data-driven task, which relies on the training dataset. Descriptions in the majority of the existing image-sentence datasets are generated by humans under specific instructions. Real-world sentence data is rarely directly utilized for training since it is sometimes noisy and unbalanced, which makes it ‘imperfect’ for the training of the image captioning task. In this dissertation, we present a novel image captioning framework to deal with the uncontrolled image-sentence dataset where descriptions could be strongly or weakly correlated to the image content and in arbitrary lengths. A self-guiding learning process is proposed to fully reveal the internal statistics of the training dataset and to look into the learning process in a global way and generate descriptions that are syntactically correct and semantically sound

Automatic analysis of facial actions: a survey

As one of the most comprehensive and objective ways to describe facial expressions, the Facial Action Coding System (FACS) has recently received significant attention. Over the past 30 years, extensive research has been conducted by psychologists and neuroscientists on various aspects of facial expression analysis using FACS. Automating FACS coding would make this research faster and more widely applicable, opening up new avenues to understanding how we communicate through facial expressions. Such an automated process can also potentially increase the reliability, precision and temporal resolution of coding. This paper provides a comprehensive survey of research into machine analysis of facial actions. We systematically review all components of such systems: pre-processing, feature extraction and machine coding of facial actions. In addition, the existing FACS-coded facial expression databases are summarised. Finally, challenges that have to be addressed to make automatic facial action analysis applicable in real-life situations are extensively discussed. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the future of machine recognition of facial actions: what are the challenges and opportunities that researchers in the field face