Recognizing point clouds using conditional random fields

Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization.Peer ReviewedPostprint (author’s final draft

High-Order Inference, Ranking, and Regularization Path for Structured SVM

This thesis develops novel methods to enable the use of structured prediction in computer vision and medical imaging. Specifically, our contributions are four fold. First, we propose a new family of high-order potentials that encourage parsimony in the labeling, and enable its use by designing an accurate graph cuts based algorithm to minimize the corresponding energy function. Second, we show how the average precision SVM formulation can be extended to incorporate high-order information for ranking. Third, we propose a novel regularization path algorithm for structured SVM. Fourth, we show how the weakly supervised framework of latent SVM can be employed to learn the parameters for the challenging deformable registration problem.In more detail, the first part of the thesis investigates the high-order inference problem. Specifically, we present a novel family of discrete energy minimization problems, which we call parsimonious labeling. It is a natural generalization of the well known metric labeling problems for high-order potentials. In addition to this, we propose a generalization of the Pn-Potts model, which we call Hierarchical Pn-Potts model. In the end, we propose parallelizable move making algorithms with very strong multiplicative bounds for the optimization of the hierarchical Pn-Potts model and the parsimonious labeling.Second part of the thesis investigates the ranking problem while using high-order information. Specifically, we introduce two alternate frameworks to incorporate high-order information for the ranking tasks. The first framework, which we call high-order binary SVM (HOB-SVM), optimizes a convex upperbound on weighted 0-1 loss while incorporating high-order information using joint feature map. The rank list for the HOB-SVM is obtained by sorting samples using max-marginals based scores. The second framework, which we call high-order AP-SVM (HOAP-SVM), takes its inspiration from AP-SVM and HOB-SVM (our first framework). Similar to AP-SVM, it optimizes upper bound on average precision. However, unlike AP-SVM and similar to HOB-SVM, it can also encode high-order information. The main disadvantage of HOAP-SVM is that estimating its parameters requires solving a difference-of-convex program. We show how a local optimum of the HOAP-SVM learning problem can be computed efficiently by the concave-convex procedure. Using standard datasets, we empirically demonstrate that HOAP-SVM outperforms the baselines by effectively utilizing high-order information while optimizing the correct loss function.In the third part of the thesis, we propose a new algorithm SSVM-RP to obtain epsilon-optimal regularization path of structured SVM. We also propose intuitive variants of the Block-Coordinate Frank-Wolfe algorithm (BCFW) for the faster optimization of the SSVM-RP algorithm. In addition to this, we propose a principled approach to optimize the SSVM with additional box constraints using BCFW and its variants. In the end, we propose regularization path algorithm for SSVM with additional positivity/negativity constraints.In the fourth and the last part of the thesis (Appendix), we propose a novel weakly supervised discriminative algorithm for learning context specific registration metrics as a linear combination of conventional metrics. Conventional metrics can cope partially - depending on the clinical context - with tissue anatomical properties. In this work we seek to determine anatomy/tissue specific metrics as a context-specific aggregation/linear combination of known metrics. We propose a weakly supervised learning algorithm for estimating these parameters conditionally to the data semantic classes, using a weak training dataset. We show the efficacy of our approach on three highly challenging datasets in the field of medical imaging, which vary in terms of anatomical structures and image modalities.Cette thèse présente de nouvelles méthodes pour l'application de la prédiction structurée en vision numérique et en imagerie médicale.Nos nouvelles contributions suivent quatre axes majeurs.La première partie de cette thèse étudie le problème d'inférence d'ordre supérieur.Nous présentons une nouvelle famille de problèmes de minimisation d'énergie discrète, l'étiquetage parcimonieux, encourageant la parcimonie des étiquettes.C'est une extension naturelle des problèmes connus d'étiquetage de métriques aux potentiels d'ordre élevé.Nous proposons par ailleurs une généralisation du modèle Pn-Potts, le modèle Pn-Potts hiérarchique.Enfin, nous proposons un algorithme parallélisable à proposition de mouvements avec de fortes bornes multiplicatives pour l'optimisation du modèle Pn-Potts hiérarchique et l'étiquetage parcimonieux.La seconde partie de cette thèse explore le problème de classement en utilisant de l'information d'ordre élevé.Nous introduisons deux cadres différents pour l'incorporation d'information d'ordre élevé dans le problème de classement.Le premier modèle, que nous nommons SVM binaire d'ordre supérieur (HOB-SVM), optimise une borne supérieure convexe sur l'erreur 0-1 pondérée tout en incorporant de l'information d'ordre supérieur en utilisant un vecteur de charactéristiques jointes.Le classement renvoyé par HOB-SVM est obtenu en ordonnant les exemples selon la différence entre la max-marginales de l'affectation d'un exemple à la classe associée et la max-marginale de son affectation à la classe complémentaire.Le second modèle, appelé AP-SVM d'ordre supérieur (HOAP-SVM), s'inspire d'AP-SVM et de notre premier modèle, HOB-SVM.Le modèle correspond à une optimisation d'une borne supérieure sur la précision moyenne, à l'instar d'AP-SVM, qu'il généralise en permettant également l'incorporation d'information d'ordre supérieur.Nous montrons comment un optimum local du problème d'apprentissage de HOAP-SVM peut être déterminé efficacement grâce à la procédure concave-convexe.En utilisant des jeux de données standards, nous montrons empiriquement que HOAP-SVM surpasse les modèles de référence en utilisant efficacement l'information d'ordre supérieur tout en optimisant directement la fonction d'erreur appropriée.Dans la troisième partie, nous proposons un nouvel algorithme, SSVM-RP, pour obtenir un chemin de régularisation epsilon-optimal pour les SVM structurés.Nous présentons également des variantes intuitives de l'algorithme Frank-Wolfe pour l'optimisation accélérée de SSVM-RP.De surcroît, nous proposons une approche systématique d'optimisation des SSVM avec des contraintes additionnelles de boîte en utilisant BCFW et ses variantes.Enfin, nous proposons un algorithme de chemin de régularisation pour SSVM avec des contraintes additionnelles de positivité/negativité.Dans la quatrième et dernière partie de la thèse, en appendice, nous montrons comment le cadre de l'apprentissage semi-supervisé des SVM à variables latentes peut être employé pour apprendre les paramètres d'un problème complexe de recalage déformable.Nous proposons un nouvel algorithme discriminatif semi-supervisé pour apprendre des métriques de recalage spécifiques au contexte comme une combinaison linéaire des métriques conventionnelles.Selon l'application, les métriques traditionnelles sont seulement partiellement sensibles aux propriétés anatomiques des tissus.Dans ce travail, nous cherchons à déterminer des métriques spécifiques à l'anatomie et aux tissus, par agrégation linéaire de métriques connues.Nous proposons un algorithme d'apprentissage semi-supervisé pour estimer ces paramètres conditionnellement aux classes sémantiques des données, en utilisant un jeu de données faiblement annoté.Nous démontrons l'efficacité de notre approche sur trois jeux de données particulièrement difficiles dans le domaine de l'imagerie médicale, variables en terme de structures anatomiques et de modalités d'imagerie

Graph matching with a dual-step EM algorithm

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data

Recalage/Fusion d'images multimodales à l'aide de graphes d'ordres supérieurs

The main objective of this thesis is the exploration of higher order Markov Random Fields for image registration, specifically to encode the knowledge of global transformations, like rigid transformations, into the graph structure. Our main framework applies to 2D-2D or 3D-3D registration and use a hierarchical grid-based Markov Random Field model where the hidden variables are the displacements vectors of the control points of the grid.We first present the construction of a graph that allows to perform linear registration, which means here that we can perform affine registration, rigid registration, or similarity registration with the same graph while changing only one potential. Our framework is thus modular regarding the sought transformation and the metric used. Inference is performed with Dual Decomposition, which allows to handle the higher order hyperedges and which ensures the global optimum of the function is reached if we have an agreement among the slaves. A similar structure is also used to perform 2D-3D registration.Second, we fuse our former graph with another structure able to perform deformable registration. The resulting graph is more complex and another optimisation algorithm, called Alternating Direction Method of Multipliers is needed to obtain a better solution within reasonable time. It is an improvement of Dual Decomposition which speeds up the convergence. This framework is able to solve simultaneously both linear and deformable registration which allows to remove a potential bias created by the standard approach of consecutive registrations.L’objectif principal de cette thèse est l’exploration du recalage d’images à l’aide de champs aléatoires de Markov d’ordres supérieurs, et plus spécifiquement d’intégrer la connaissance de transformations globales comme une transformation rigide, dans la structure du graphe. Notre cadre principal s’applique au recalage 2D-2D ou 3D-3D et utilise une approche hiérarchique d’un modèle de champ de Markov dont le graphe est une grille régulière. Les variables cachées sont les vecteurs de déplacements des points de contrôle de la grille.Tout d’abord nous expliciterons la construction du graphe qui permet de recaler des images en cherchant entre elles une transformation affine, rigide, ou une similarité, tout en ne changeant qu’un potentiel sur l’ensemble du graphe, ce qui assure une flexibilité lors du recalage. Le choix de la métrique est également laissée à l’utilisateur et ne modifie pas le fonctionnement de notre algorithme. Nous utilisons l’algorithme d’optimisation de décomposition duale qui permet de gérer les hyper-arêtes du graphe et qui garantit l’obtention du minimum exact de la fonction pourvu que l’on ait un accord entre les esclaves. Un graphe similaire est utilisé pour réaliser du recalage 2D-3D.Ensuite, nous fusionnons le graphe précédent avec un autre graphe construit pour réaliser le recalage déformable. Le graphe résultant de cette fusion est plus complexe et, afin d’obtenir un résultat en un temps raisonnable, nous utilisons une méthode d’optimisation appelée ADMM (Alternating Direction Method of Multipliers) qui a pour but d’accélérer la convergence de la décomposition duale. Nous pouvons alors résoudre simultanément recalage affine et déformable, ce qui nous débarrasse du biais potentiel issu de l’approche classique qui consiste à recaler affinement puis de manière déformable

Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities

The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies. Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes. Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics. Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities. Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system

IST Austria Technical Report

We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks

Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis

Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models. The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency