Parallelism in problems of low- and medium-level image processing and pattern recognition is the subject of this book. It covers the investigation of parallelism in algorithms and in fundamental methods of image processing and pattern recognition. Based on this, new concepts for parallel architectures are derived and their performance is evaluated. Different hardware structures such as SIMD, MIMD, data flow machines, transputer systems, neural networks and interconnection networks are described, including high-speed VLSI-implementations. Additional topics covered include software aspects and image processing systems.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Proceedings of a workshop held at Bonas, France, 22-27 August, 1988. The focus of the 35 contributions is on the interpretation and conversion of iconic data (pixels) into features. However, as this problem cannot be isolated from the recognition problem as a whole, several other related problems are also treated, such as the coding and compression.

This volume constitutes the refereed proceedings of the 5th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2011, held in Las Palmas de Gran Canaria, Spain, in June 2011. The 34 revised full papers and 58 revised poster papers presented were carefully reviewed and selected from 158 submissions. The papers are organized in topical sections on computer vision; image processing and analysis; medical applications; and pattern recognition.

Volume II of the Six Volume Remote Sensing Handbook, Second Edition, is focused on digital image processing including image classification methods in land cover and land use. It discusses object-based segmentation and pixel-based image processing algorithms, change detection techniques, and image classification for a wide array of applications including land use/land cover, croplands, urban studies, processing hyperspectral remote sensing data, thermal imagery, light detection and ranging (LiDAR), geoprocessing workflows, frontiers of GIScience, and future pathways. This thoroughly revised and updated volume draws on the expertise of a diverse array of leading international authorities in remote sensing and provides an essential resource for researchers at all levels interested in using remote sensing. It integrates discussions of remote sensing principles, data, methods, development, applications, and scientific and social context. Features Provides the most up-to-date comprehensive coverage of digital image processing. Highlights object-based image analysis (OBIA) and pixel-based classification methods and techniques of digital image processing. Demonstrates practical examples of image processing for a myriad of applications such as land use/land cover, croplands, and urban. Establishes image processing using different types of remote sensing data that includes multispectral, radar, LiDAR, thermal, and hyperspectral. Highlights change detection, geoprocessing, and GIScience. This volume is an excellent resource for the entire remote sensing and GIS community. Academics, researchers, undergraduate and graduate students, as well as practitioners, decision makers, and policymakers, will benefit from the expertise of the professionals featured in this book, and their extensive knowledge of new and emerging trends.

This thesis addresses two different topics, both vital for implementing modern high-energy physics experiments: detector development and data analysis. Providing a concise introduction to both the standard model of particle physics and the basic principles of semiconductor tracking detectors, it presents the first measurement of the top quark pole mass from the differential cross-section of tt+J events in the dileptonic tt decay channel. The first part focuses on the development and characterization of silicon pixel detectors. To account for the expected increase in luminosity of the Large Hadron Collider (LHC), the pixel detector of the compact muon solenoid (CMS) experiment is replaced by an upgraded detector with new front-end electronics. It presents comprehensive test beam studies conducted to verify the design and quantify the performance of the new front-end in terms of tracking efficiency and spatial resolution. Furthermore, it proposes a new cluster interpolation method, which utilizes the third central moment of the cluster charge distribution to improve the position resolution. The second part of the thesis introduces an alternative measurement of the top quark mass from the normalized differential production cross-sections of dileptonic top quark pair events with an additional jet. The energy measurement is 8TeV. Using theoretical predictions at next-to-leading order in perturbative Quantum Chromodynamics (QCD), the top quark pole mass is determined using a template fit method.