Image Guided Respiratory Motion Analysis: Time Series and Image Registration.

The efficacy of Image guided radiation therapy (IGRT) systems relies on accurately extracting, modeling and predicting tumor movement with imaging techniques. This thesis investigates two key problems associated with such systems: motion modeling and image processing. For thoracic and upper abdominal tumors, respiratory motion is the dominant factor for tumor movement. We have studied several special structured time series analysis techniques to incorporate the semi-periodicity characteristics of respiratory motion. The proposed methods are robust towards large variations among fractions and populations; the algorithms perform stably in the presence of sparse radiographic observations with noise. We have proposed a subspace projection method to quantitatively evaluate the semi-periodicity of a given observation trace; a nonparametric local regression approach for real-time prediction of respiratory motion; a state augmentation scheme to model hysteresis; and an ellipse tracking algorithm to estimate the trend of respiratory motion in real time. For image processing, we have focused on designing regularizations to account for prior information in image registration problems. We investigated a penalty function design that accommodates tissue-type-dependent elasticity information. We studied a class of discontinuity preserving regularizers that yield smooth deformation estimates in most regions, yet allow discontinuities supported by data. We have further proposed a discriminate regularizer that preserves shear discontinuity, but discourages folding or vacuum generating flows. In addition, we have initiated a preliminary principled study on the fundamental performance limit of image registration problems. We proposed a statistical generative model to account for noise effect in both source and target images, and investigated the approximate performance of the maximum-likelihood estimator corresponding to the generative model and the commonly adopted M-estimator. A simple example suggests that the approximation is reasonably accurate. Our studies in both time series analysis and image registration constitute essential building-blocks for clinical applications such as adaptive treatment. Besides their theoretical interests, it is our sincere hope that with further justifications, the proposed techniques would realize its clinical value, and improve the quality of life for patients.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60673/1/druan_1.pd

Face Centered Image Analysis Using Saliency and Deep Learning Based Techniques

Image analysis starts with the purpose of configuring vision machines that can perceive like human to intelligently infer general principles and sense the surrounding situations from imagery. This dissertation studies the face centered image analysis as the core problem in high level computer vision research and addresses the problem by tackling three challenging subjects: Are there anything interesting in the image? If there is, what is/are that/they? If there is a person presenting, who is he/she? What kind of expression he/she is performing? Can we know his/her age? Answering these problems results in the saliency-based object detection, deep learning structured objects categorization and recognition, human facial landmark detection and multitask biometrics. To implement object detection, a three-level saliency detection based on the self-similarity technique (SMAP) is firstly proposed in the work. The first level of SMAP accommodates statistical methods to generate proto-background patches, followed by the second level that implements local contrast computation based on image self-similarity characteristics. At last, the spatial color distribution constraint is considered to realize the saliency detection. The outcome of the algorithm is a full resolution image with highlighted saliency objects and well-defined edges. In object recognition, the Adaptive Deconvolution Network (ADN) is implemented to categorize the objects extracted from saliency detection. To improve the system performance, L1/2 norm regularized ADN has been proposed and tested in different applications. The results demonstrate the efficiency and significance of the new structure. To fully understand the facial biometrics related activity contained in the image, the low rank matrix decomposition is introduced to help locate the landmark points on the face images. The natural extension of this work is beneficial in human facial expression recognition and facial feature parsing research. To facilitate the understanding of the detected facial image, the automatic facial image analysis becomes essential. We present a novel deeply learnt tree-structured face representation to uniformly model the human face with different semantic meanings. We show that the proposed feature yields unified representation in multi-task facial biometrics and the multi-task learning framework is applicable to many other computer vision tasks

Modern Regularization Methods for Inverse Problems

Regularization methods are a key tool in the solution of inverse problems. They are used to introduce prior knowledge and allow a robust approximation of ill-posed (pseudo-) inverses. In the last two decades interest has shifted from linear to nonlinear regularization methods, even for linear inverse problems. The aim of this paper is to provide a reasonably comprehensive overview of this shift towards modern nonlinear regularization methods, including their analysis, applications and issues for future research. In particular we will discuss variational methods and techniques derived from them, since they have attracted much recent interest and link to other fields, such as image processing and compressed sensing. We further point to developments related to statistical inverse problems, multiscale decompositions and learning theory.Leverhulme Trust Early Career Fellowship ‘Learning from mistakes: a supervised feedback-loop for imaging applications’ Isaac Newton Trust Cantab Capital Institute for the Mathematics of Information ERC Grant EU FP 7 - ERC Consolidator Grant 615216 LifeInverse German Ministry for Science and Education (BMBF) project MED4D EPSRC grant EP/K032208/

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Feature extraction on faces : from landmark localization to depth estimation

Le sujet de cette thèse porte sur les algorithmes d'apprentissage qui extraient les caractéristiques importantes des visages. Les caractéristiques d’intérêt principal sont des points clés; La localisation en deux dimensions (2D) ou en trois dimensions (3D) de traits importants du visage telles que le centre des yeux, le bout du nez et les coins de la bouche. Les points clés sont utilisés pour résoudre des tâches complexes qui ne peuvent pas être résolues directement ou qui requièrent du guidage pour l’obtention de performances améliorées, telles que la reconnaissance de poses ou de gestes, le suivi ou la vérification du visage. L'application des modèles présentés dans cette thèse concerne les images du visage; cependant, les algorithmes proposés sont plus généraux et peuvent être appliqués aux points clés de d'autres objets, tels que les mains, le corps ou des objets fabriqués par l'homme. Cette thèse est écrite par article et explore différentes techniques pour résoudre plusieurs aspects de la localisation de points clés. Dans le premier article, nous démêlons l'identité et l'expression d'un visage donné pour apprendre une distribution à priori sur l'ensemble des points clés. Cette distribution à priori est ensuite combinée avec un classifieur discriminant qui apprend une distribution de probabilité indépendante par point clé. Le modèle combiné est capable d'expliquer les différences dans les expressions pour une même représentation d'identité. Dans le deuxième article, nous proposons une architecture qui vise à conserver les caractéristiques d’images pour effectuer des tâches qui nécessitent une haute précision au niveau des pixels, telles que la localisation de points clés ou la segmentation d’images. L’architecture proposée extrait progressivement les caractéristiques les plus grossières dans les étapes d'encodage pour obtenir des informations plus globales sur l’image. Ensuite, il étend les caractéristiques grossières pour revenir à la résolution de l'image originale en recombinant les caractéristiques du chemin d'encodage. Le modèle, appelé Réseaux de Recombinaison, a obtenu l’état de l’art sur plusieurs jeux de données, tout en accélérant le temps d’apprentissage. Dans le troisième article, nous visons à améliorer la localisation des points clés lorsque peu d'images comportent des étiquettes sur des points clés. En particulier, nous exploitons une forme plus faible d’étiquettes qui sont plus faciles à acquérir ou plus abondantes tel que l'émotion ou la pose de la tête. Pour ce faire, nous proposons une architecture permettant la rétropropagation du gradient des étiquettes les plus faibles à travers des points clés, ainsi entraînant le réseau de localisation des points clés. Nous proposons également une composante de coût non supervisée qui permet des prédictions de points clés équivariantes en fonction des transformations appliquées à l'image, sans avoir les vraies étiquettes des points clés. Ces techniques ont considérablement amélioré les performances tout en réduisant le pourcentage d'images étiquetées par points clés. Finalement, dans le dernier article, nous proposons un algorithme d'apprentissage permettant d'estimer la profondeur des points clés sans aucune supervision de la profondeur. Nous y parvenons en faisant correspondre les points clés de deux visages en les transformant l'un vers l'autre. Cette transformation nécessite une estimation de la profondeur sur un visage, ainsi que une transformation affine qui transforme le premier visage au deuxième. Nous démontrons que notre formulation ne nécessite que la profondeur et que les paramètres affines peuvent être estimés avec un solution analytique impliquant les points clés augmentés par profondeur. Même en l'absence de supervision directe de la profondeur, la technique proposée extrait des valeurs de profondeur raisonnables qui diffèrent des vraies valeurs de profondeur par un facteur d'échelle et de décalage. Nous démontrons des applications d'estimation de profondeur pour la tâche de rotation de visage, ainsi que celle d'échange de visage.This thesis focuses on learning algorithms that extract important features from faces. The features of main interest are landmarks; the two dimensional (2D) or three dimensional (3D) locations of important facial features such as eye centers, nose tip, and mouth corners. Landmarks are used to solve complex tasks that cannot be solved directly or require guidance for enhanced performance, such as pose or gesture recognition, tracking, or face verification. The application of the models presented in this thesis is on facial images; however, the algorithms proposed are more general and can be applied to the landmarks of other forms of objects, such as hands, full body or man-made objects. This thesis is written by article and explores different techniques to solve various aspects of landmark localization. In the first article, we disentangle identity and expression of a given face to learn a prior distribution over the joint set of landmarks. This prior is then merged with a discriminative classifier that learns an independent probability distribution per landmark. The merged model is capable of explaining differences in expressions for the same identity representation. In the second article, we propose an architecture that aims at uncovering image features to do tasks that require high pixel-level accuracy, such as landmark localization or image segmentation. The proposed architecture gradually extracts coarser features in its encoding steps to get more global information over the image and then it expands the coarse features back to the image resolution by recombining the features of the encoding path. The model, termed Recombinator Networks, obtained state-of-the-art on several datasets, while also speeding up training. In the third article, we aim at improving landmark localization when only a few images with labelled landmarks are available. In particular, we leverage a weaker form of data labels that are easier to acquire or more abundantly available such as emotion or head pose. To do so, we propose an architecture to backpropagate gradients of the weaker labels through landmarks, effectively training the landmark localization network. We also propose an unsupervised loss component which makes equivariant landmark predictions with respect to transformations applied to the image without having ground truth landmark labels. These techniques improved performance considerably when we have a low percentage of labelled images with landmarks. Finally, in the last article, we propose a learning algorithm to estimate the depth of the landmarks without any depth supervision. We do so by matching landmarks of two faces through transforming one to another. This transformation requires estimation of depth on one face and an affine transformation that maps the first face to the second one. Our formulation, which only requires depth estimation and affine parameters, can be estimated as a closed form solution of the 2D landmarks and the estimated depth. Even without direct depth supervision, the proposed technique extracts reasonable depth values that differ from the ground truth depth values by a scale and a shift. We demonstrate applications of the estimated depth in face rotation and face replacement tasks