Hierarchical and Spatial Structures for Interpreting Images of Man-made Scenes Using Graphical Models

The task of semantic scene interpretation is to label the regions of an image and their relations into meaningful classes. Such task is a key ingredient to many computer vision applications, including object recognition, 3D reconstruction and robotic perception. It is challenging partially due to the ambiguities inherent to the image data. The images of man-made scenes, e. g. the building facade images, exhibit strong contextual dependencies in the form of the spatial and hierarchical structures. Modelling these structures is central for such interpretation task. Graphical models provide a consistent framework for the statistical modelling. Bayesian networks and random fields are two popular types of the graphical models, which are frequently used for capturing such contextual information. The motivation for our work comes from the belief that we can find a generic formulation for scene interpretation that having both the benefits from random fields and Bayesian networks. It should have clear semantic interpretability. Therefore our key contribution is the development of a generic statistical graphical model for scene interpretation, which seamlessly integrates different types of the image features, and the spatial structural information and the hierarchical structural information defined over the multi-scale image segmentation. It unifies the ideas of existing approaches, e. g. conditional random field (CRF) and Bayesian network (BN), which has a clear statistical interpretation as the maximum a posteriori (MAP) estimate of a multi-class labelling problem. Given the graphical model structure, we derive the probability distribution of the model based on the factorization property implied in the model structure. The statistical model leads to an energy function that can be optimized approximately by either loopy belief propagation or graph cut based move making algorithm. The particular type of the features, the spatial structure, and the hierarchical structure however is not prescribed. In the experiments, we concentrate on terrestrial man-made scenes as a specifically difficult problem. We demonstrate the application of the proposed graphical model on the task of multi-class classification of building facade image regions. The framework for scene interpretation allows for significantly better classification results than the standard classical local classification approach on man-made scenes by incorporating the spatial and hierarchical structures. We investigate the performance of the algorithms on a public dataset to show the relative importance of the information from the spatial structure and the hierarchical structure. As a baseline for the region classification, we use an efficient randomized decision forest classifier. Two specific models are derived from the proposed graphical model, namely the hierarchical CRF and the hierarchical mixed graphical model. We show that these two models produce better classification results than both the baseline region classifier and the flat CRF.Hierarchische und räumliche Strukturen zur Interpretation von Bildern anthropogener Szenen unter Nutzung graphischer Modelle Ziel der semantischen Bildinterpretation ist es, Bildregionen und ihre gegenseitigen Beziehungen zu kennzeichnen und in sinnvolle Klassen einzuteilen. Dies ist eine der Hauptaufgabe in vielen Bereichen des maschinellen Sehens, wie zum Beispiel der Objekterkennung, 3D Rekonstruktion oder der Wahrnehmung von Robotern. Insbesondere Bilder anthropogener Szenen, wie z.B. Fassadenaufnahmen, sind durch starke räumliche und hierarchische Strukturen gekennzeichnet. Diese Strukturen zu modellieren ist zentrale Teil der Interpretation, für deren statistische Modellierung graphische Modelle ein geeignetes konsistentes Werkzeug darstellen. Bayes Netze und Zufallsfelder sind zwei bekannte und häufig genutzte Beispiele für graphische Modelle zur Erfassung kontextabhängiger Informationen. Die Motivation dieser Arbeit liegt in der überzeugung, dass wir eine generische Formulierung der Bildinterpretation mit klarer semantischer Bedeutung finden können, die die Vorteile von Bayes Netzen und Zufallsfeldern verbindet. Der Hauptbeitrag der vorliegenden Arbeit liegt daher in der Entwicklung eines generischen statistischen graphischen Modells zur Bildinterpretation, welches unterschiedlichste Typen von Bildmerkmalen und die räumlichen sowie hierarchischen Strukturinformationen über eine multiskalen Bildsegmentierung integriert. Das Modell vereinheitlicht die existierender Arbeiten zugrunde liegenden Ideen, wie bedingter Zufallsfelder (conditional random field (CRF)) und Bayesnetze (Bayesian network (BN)). Dieses Modell hat eine klare statistische Interpretation als Maximum a posteriori (MAP) Schätzer eines mehrklassen Zuordnungsproblems. Gegeben die Struktur des graphischen Modells und den dadurch definierten Faktorisierungseigenschaften leiten wir die Wahrscheinlichkeitsverteilung des Modells ab. Dies führt zu einer Energiefunktion, die näherungsweise optimiert werden kann. Der jeweilige Typ der Bildmerkmale, die räumliche sowie hierarchische Struktur ist von dieser Formulierung unabhängig. Wir zeigen die Anwendung des vorgeschlagenen graphischen Modells anhand der mehrklassen Zuordnung von Bildregionen in Fassadenaufnahmen. Wir demonstrieren, dass das vorgeschlagene Verfahren zur Bildinterpretation, durch die Berücksichtigung räumlicher sowie hierarchischer Strukturen, signifikant bessere Klassifikationsergebnisse zeigt, als klassische lokale Klassifikationsverfahren. Die Leistungsfähigkeit des vorgeschlagenen Verfahrens wird anhand eines öffentlich verfügbarer Datensatzes evaluiert. Zur Klassifikation der Bildregionen nutzen wir ein Verfahren basierend auf einem effizienten Random Forest Klassifikator. Aus dem vorgeschlagenen allgemeinen graphischen Modell werden konkret zwei spezielle Modelle abgeleitet, ein hierarchisches bedingtes Zufallsfeld (hierarchical CRF) sowie ein hierarchisches gemischtes graphisches Modell. Wir zeigen, dass beide Modelle bessere Klassifikationsergebnisse erzeugen als die zugrunde liegenden lokalen Klassifikatoren oder die einfachen bedingten Zufallsfelder

The feasibility of using feature-flow and label transfer system to segment medical images with deformed anatomy in orthopedic surgery

In computer-aided surgical systems, to obtain high fidelity three-dimensional models, we require accurate segmentation of medical images. State-of-art medical image segmentation methods have been used successfully in particular applications, but they have not been demonstrated to work well over a wide range of deformities. For this purpose, I studied and evaluated medical image segmentation using the feature-flow based Label Transfer System described by Liu and colleagues. This system has produced promising results in parsing images of natural scenes. Its ability to deal with variations in shapes of objects is desirable. In this paper, we altered this system and assessed its feasibility of automatic segmentation. Experiments showed that this system achieved better recognition rates than those in natural-scene parsing applications, but the high recognition rates were not consistent across different images. Although this system is not considered clinically practical, we may improve it and incorporate it with other medical segmentation tools

Sublinear Computation Paradigm

This open access book gives an overview of cutting-edge work on a new paradigm called the “sublinear computation paradigm,” which was proposed in the large multiyear academic research project “Foundations of Innovative Algorithms for Big Data.” That project ran from October 2014 to March 2020, in Japan. To handle the unprecedented explosion of big data sets in research, industry, and other areas of society, there is an urgent need to develop novel methods and approaches for big data analysis. To meet this need, innovative changes in algorithm theory for big data are being pursued. For example, polynomial-time algorithms have thus far been regarded as “fast,” but if a quadratic-time algorithm is applied to a petabyte-scale or larger big data set, problems are encountered in terms of computational resources or running time. To deal with this critical computational and algorithmic bottleneck, linear, sublinear, and constant time algorithms are required. The sublinear computation paradigm is proposed here in order to support innovation in the big data era. A foundation of innovative algorithms has been created by developing computational procedures, data structures, and modelling techniques for big data. The project is organized into three teams that focus on sublinear algorithms, sublinear data structures, and sublinear modelling. The work has provided high-level academic research results of strong computational and algorithmic interest, which are presented in this book. The book consists of five parts: Part I, which consists of a single chapter on the concept of the sublinear computation paradigm; Parts II, III, and IV review results on sublinear algorithms, sublinear data structures, and sublinear modelling, respectively; Part V presents application results. The information presented here will inspire the researchers who work in the field of modern algorithms

Stereo matching using higher-order graph cuts

Stereo matching is one of the fundamental tasks in early vision. Unlike human brain recognizes objects and estimates the depth easily, it is difficult to design algorithms that perform well on a computer due to variations of illumination, occlusion or textureless. Like most of the early vision problems, stereo matching can be formulated as an energy minimization problem in which the optimal depth is the one with the lowest energy. And graph cuts is one of the efficient and effective minimization tools that avoids the problems of local minima. Conventional energy functions are defined on Markov Random Fields (MRFs) with a 4-connected grid structure derived from the image, however it is incapable of expressing complex relationship between group of pixels. This thesis focuses on exploring some aspects of stereo matching problems through higher-order structure and higher-order graph cuts. The first problem I address relates to the evaluation of five state-of-the-art segmentation approaches. Their different contributions to segment-based stereo matching have been quantitatively measured and analyzed. This works aim at helping researchers to choose the segmentation approach that most suitable for their stereo matching application. The second part of the thesis proposes a novel approach to dense stereo matching. This method features sub-segmentation and adopts a higher-order potential to enforce the label consistency inside segments as a soft constraint. Moreover, several successful techniques have been combined. Experiments show that this approach obtains state-of-the-art results while still keeping efficiency. In the last part of the thesis, a novel two-layer MRFs framework is presented in which stereo matching and surface boundary estimation are combined. Both properties are inferred simultaneously and globally so that they can benefit each other. This work has direct application in phosphene vision based human indoor navigation. Experiments prove that the proposed framework achieves significantly better performance than other popular methods in all resolutions