In this thesis we study the computational complexity of five NP-hard graph problems. It is widely accepted that, in general, NP-hard problems cannot be solved efficiently, that is, in polynomial time, due to many unsuccessful attempts to prove the contrary. Hence, we aim to identify properties of the inputs other than their length, that make the problem tractable or intractable. We measure these properties via parameters, mappings that assign to each input a nonnegative integer. For a given parameter k, we then attempt to design fixed-parameter algorithms, algorithms that on input q have running time upper bounded by f(k(q)) * |q|^c , where f is a preferably slowly growing function, |q| is the length of q, and c is a constant, preferably small. In each of the graph problems treated in this thesis, our input represents the setting in which we shall find a solution graph. In addition, the solution graphs shall have a certain property specific to our five graph problems. This property comes in three flavors. First, we look for a graph that shall be sparse! That is, it shall contain few edges. Second, we look for a graph that shall be dense! That is, it shall contain many edges. Third, we look for a graph that shall be robust! That is, it shall remain a good solution, even when it suffers several small modifications. Be sparse! In this part of the thesis, we analyze two similar problems. The input for both of them is a hypergraph H , which consists of a vertex set V and a family E of subsets of V , called hyperedges. The task is to find a support for H , a graph G such that for each hyperedge W in E we have that G[W ] is connected. Motivated by applications in network design, we study SUBSET INTERCONNECTION DESIGN, where we additionally get an integer f , and the support shall contain at most |V| - f + 1 edges. We show that SUBSET INTERCONNECTION DESIGN admits a fixed-parameter algorithm with respect to the number of hyperedges in the input hypergraph, and a fixed-parameter algorithm with respect to f + d , where d is the size of a largest hyperedge. Motivated by an application in hypergraph visualization, we study r-OUTERPLANAR SUPPORT where the support for H shall be r -outerplanar, that is, admit a edge-crossing free embedding in the plane with at most r layers. We show that r-OUTER-PLANAR SUPPORT admits a fixed-parameter algorithm with respect to m + r , where m is the number of hyperedges in the input hypergraph H. Be dense! In this part of the thesis, we study two problems motivated by community detection in social networks. Herein, the input is a graph G and an integer k. We look for a subgraph G' of G containing (exactly) k vertices which adheres to one of two mathematically precise definitions of being dense. In mu-CLIQUE, 0 < mu <= 1, the sought k-vertex subgraph G' should contain at least mu time k choose 2 edges. We study the complexity of mu-CLIQUE with respect to three parameters of the input graph G: the maximum vertex degree delta, h-index h, and degeneracy d. We have delta >= h >= d in every graph and h as well as d assume small values in graphs derived from social networks. For delta and for h, respectively, we obtain fixed-parameter algorithms for mu-CLIQUE and we show that for d + k a fixed-parameter algorithm is unlikely to exist. We prove the positive algorithmic results via developing a general framework for optimizing objective functions over k-vertex subgraphs. In HIGHLY CONNECTED SUBGRAPH we look for a k-vertex subgraph G' in which each vertex shall have degree at least floor(k/2)+1. We analyze a part of the so-called parameter ecology for HIGHLY CONNECTED SUBGRAPH, that is, we navigate the space of possible parameters in a quest to find a reasonable trade-off between small parameter values in practice and efficient running time guarantees. The highlights are that no 2^o(n) * n^c -time algorithms are possible for n-vertex input graphs unless the Exponential Time Hypothesis fails; that there is a O(4^g * n^2)-time algorithm for the number g of edges outgoing from the solution G; and we derive a 2^(O(sqrt(a)log(a)) + a^2nm-time algorithm for the number a of edges not in the solution. Be robust! In this part of the thesis, we study the VECTOR CONNECTIVITY problem, where we are given a graph G, a vertex labeling ell from V(G) to {1, . . . , d }, and an integer k. We are to find a vertex subset S of V(G) of size at most k such that each vertex v in V (G)\S has ell(v) vertex-disjoint paths from v to S in G. Such a set S is useful when placing servers in a network to satisfy robustness-of-service demands. We prove that VECTOR CONNECTIVITY admits a randomized fixed-parameter algorithm with respect to k, that it does not allow a polynomial kernelization with respect to k + d but that, if d is treated as a constant, then it allows a vertex-linear kernelization with respect to k. In dieser Dissertation untersuchen wir die Berechnungskomplexität von fünf NP-schweren Graphproblemen. Es wird weithin angenommen, dass NP-schwere Probleme im Allgemeinen nicht effizient gelöst werden können, das heißt, dass sie keine Polynomialzeitalgorithmen erlauben. Diese Annahme basiert auf vielen bisher nicht erfolgreichen Versuchen das Gegenteil zu beweisen. Aus diesem Grund versuchen wir Eigenschaften der Eingabe herauszuarbeiten, die das betrachtete Problem handhabbar oder unhandhabbar machen. Solche Eigenschaften messen wir mittels Parametern, das heißt, Abbildungen, die jeder möglichen Eingabe eine natürliche Zahl zuordnen. Für einen gegebenen Parameter k versuchen wir dann Fixed-Parameter Algorithmen zu entwerfen, also Algorithmen, die auf Eingabe q eine obere Laufzeitschranke von f(k(q)) * |q|^c erlauben, wobei f eine, vorzugsweise schwach wachsende, Funktion ist, |q| die Länge der Eingabe, und c eine Konstante, vorzugsweise klein. In den Graphproblemen, die wir in dieser Dissertation studieren, repräsentiert unsere Eingabe eine Situation in der wir einen Lösungsgraph finden sollen. Zusätzlich sollen die Lösungsgraphen bestimmte problemspezifische Eigenschaften haben. Wir betrachten drei Varianten dieser Eigenschaften: Zunächst suchen wir einen Graphen, der sparse sein soll. Das heißt, dass er wenige Kanten enthalten soll. Dann suchen wir einen Graphen, der dense sein soll. Das heißt, dass er viele Kanten enthalten soll. Zuletzt suchen wir einen Graphen, der robust sein soll. Das heißt, dass er eine gute Lösung bleiben soll, selbst wenn er einige kleine Modifikationen durchmacht. Be sparse! In diesem Teil der Arbeit analysieren wir zwei ähnliche Probleme. In beiden ist die Eingabe ein Hypergraph H, bestehend aus einer Knotenmenge V und einer Familie E von Teilmengen von V, genannt Hyperkanten. Die Aufgabe ist einen Support für H zu finden, einen Graphen G, sodass für jede Hyperkante W in E der induzierte Teilgraph G[W] verbunden ist. Motiviert durch Anwendungen im Netzwerkdesign betrachten wir SUBSET INTERCONNECTION DESIGN, worin wir eine natürliche Zahl f als zusätzliche Eingabe bekommen, und der Support höchstens |V| - f + 1 Kanten enthalten soll. Wir zeigen, dass SUBSET INTERCONNECTION DESIGN einen Fixed-Parameter Algorithmus in Hinsicht auf die Zahl der Hyperkanten im Eingabegraph erlaubt, und einen Fixed-Parameter Algorithmus in Hinsicht auf f + d, wobei d die Größe einer größten Hyperkante ist. Motiviert durch eine Anwendung in der Hypergraphvisualisierung studieren wir r-OUTERPLANAR SUPPORT, worin der Support für H r-outerplanar sein soll, das heißt, er soll eine kantenkreuzungsfreie Einbettung in die Ebene erlauben mit höchstens r Schichten. Wir zeigen, dass r-OUTERPLANAR SUPPORT einen Fixed-Parameter Algorithmus in Hinsicht auf m + r zulässt, wobei m die Anzahl der Hyperkanten im Eingabehypergraphen H ist. Be dense! In diesem Teil der Arbeit studieren wir zwei Probleme, die durch Community Detection in sozialen Netzwerken motiviert sind. Dabei ist die Eingabe ein Graph G und eine natürliche Zahl k. Wir suchen einen Teilgraphen G' von G, der (genau) k Knoten enthält und dabei eine von zwei mathematisch präzisen Definitionen davon, dense zu sein, aufweist. In mu-CLIQUE, 0 < mu <= 1, soll der gesuchte Teilgraph G' mindestens mu mal k über 2 Kanten enthalten. Wir studieren die Berechnungskomplexität von mu-CLIQUE in Hinsicht auf drei Parameter des Eingabegraphen G: dem maximalen Knotengrad delta, dem h-Index h, und der Degeneracy d. Es gilt delta >= h >= d für jeden Graphen und h als auch d nehmen kleine Werte in Graphen an, die aus sozialen Netzwerken abgeleitet sind. Für delta und h erhalten wir Fixed-Parameter Algorithmen für mu-CLIQUE und wir zeigen, dass für d + k wahrscheinlich kein Fixed-Parameter Algorithmus existiert. Unsere positiven algorithmischen Resultate erhalten wir durch Entwickeln eines allgemeinen Frameworks zum Optimieren von Zielfunktionen über k-Knoten-Teilgraphen. In HIGHLY CONNECTED SUBGRAPH soll in dem gesuchten k-Knoten-Teilgraphen G' jeder Knoten Knotengrad mindestens floor(k/2) + 1 haben. Wir analysieren einen Teil der sogenannten Parameter Ecology für HIGHLY CONNECTED SUBGRAPH. Das heißt, wir navigieren im Raum der möglichen Parameter auf der Suche nach einem vernünftigen Trade-off zwischen kleinen Parameterwerten in der Praxis und effizienten oberen Laufzeitschranken. Die Highlights hier sind, dass es keine Algorithmen mit 2^o(n) * poly(n)-Laufzeit für HIGHLY CONNECTED SUBGRAPH gibt, es sei denn die Exponential Time Hypothesis stimmt nicht; die Entwicklung eines Algorithmus mit O(4^y * n^2 )-Laufzeit, wobei y die Anzahl der Kanten ist, die aus dem Lösungsgraphen G' herausgehen; und die Entwicklung eines Algorithmus mit 2^O(sqrt(a) log(a)) + O(a^2nm)-Laufzeit, wobei a die Anzahl der Kanten ist, die nicht in G' enthalten sind.

Premiering in 1990 in Antibes, France, the European Conference on Computer Vision, ECCV, has been held biennially at venues all around Europe. These conferences have been very successful, making ECCV a major event to the computer vision community. ECCV 2002 was the seventh in the series. The privilege of organizing it was shared by three universities: The IT University of Copenhagen, the University of Copenhagen, and Lund University, with the conference venue in Copenhagen. These universities lie ̈ geographically close in the vivid Oresund region, which lies partly in Denmark and partly in Sweden, with the newly built bridge (opened summer 2000) crossing the sound that formerly divided the countries. We are very happy to report that this year’s conference attracted more papers than ever before, with around 600 submissions. Still, together with the conference board, we decided to keep the tradition of holding ECCV as a single track conference. Each paper was anonymously refereed by three different reviewers. For the nal selection, for the rst time for ECCV, a system with area chairs was used. These met with the program chairsinLundfortwodaysinFebruary2002toselectwhatbecame45oralpresentations and 181 posters.Also at this meeting the selection was made without knowledge of the authors’identity.

This book constitutes the refereed proceedings of the 2021 International Conference on Business Intelligence and Information Technology (BIIT 2021) held in Harbin, China, during December 18–20, 2021. BIIT 2021 is organized by the School of Computer and Information Engineering, Harbin University of Commerce, and supported by Scientific Research Group in Egypt (SRGE), Egypt. The papers cover current research in electronic commerce technology and application, business intelligence and decision making, digital economy, accounting informatization, intelligent information processing, image processing and multimedia technology, signal detection and processing, communication engineering and technology, information security, automatic control technique, data mining, software development, and design, blockchain technology, big data technology, artificial intelligence technology.

Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications. Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of different decompositions, algorithms, implementations, and benchmarking techniques. Divided into five parts, the book begins with an overall introduction to robust principal component analysis (PCA) via decomposition into low-rank and sparse matrices. The second part addresses robust matrix factorization/completion problems while the third part focuses on robust online subspace estimation, learning, and tracking. Covering applications in image and video processing, the fourth part discusses image analysis, image denoising, motion saliency detection, video coding, key frame extraction, and hyperspectral video processing. The final part presents resources and applications in background/foreground separation for video surveillance. With contributions from leading teams around the world, this handbook provides a complete overview of the concepts, theories, algorithms, and applications related to robust low-rank and sparse matrix decompositions. It is designed for researchers, developers, and graduate students in computer vision, image and video processing, real-time architecture, machine learning, and data mining.

The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors.

Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods and deep learning. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics and deep learning architectures. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods. To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized. The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCV and other imaging and deep learning tools.

This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement the robust perception tasks, which introduces sequential feature association and depth hint augmentation, and introduces seven adaptive methods. Proposes efficient and robust semantic segmentation of traffic scenes through real-time deep dual-resolution networks and representation separation of vision transformers. Focuses on trajectory prediction and proposes phased and progressive trajectory prediction methods that is more consistent with human psychological characteristics, which is able to take both social interactions and personal intentions into account. Puts forward methods based on conditional random field and multi-task segmentation learning to solve the robust multi-object tracking problem for environment perception in autonomous vehicle scenarios. Presents the novel reliability control strategies of intelligent vehicles to optimize the dynamic tracking performance and investigates the completely unknown autonomous vehicle tracking issues with actuator faults.

This book presents new and original research in Statistical Information Theory, based on minimum divergence estimators and test statistics, from a theoretical and applied point of view, for different statistical problems with special emphasis on efficiency and robustness. Divergence statistics, based on maximum likelihood estimators, as well as Wald’s statistics, likelihood ratio statistics and Rao’s score statistics, share several optimum asymptotic properties, but are highly non-robust in cases of model misspecification under the presence of outlying observations. It is well-known that a small deviation from the underlying assumptions on the model can have drastic effect on the performance of these classical tests. Specifically, this book presents a robust version of the classical Wald statistical test, for testing simple and composite null hypotheses for general parametric models, based on minimum divergence estimators.

The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy, The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.