This volume presents new essays on the theory and practice of recognition. In order to retain its overall plausibility as a critical social theory, contemporary recognition theory needs to be able to successfully combine theory with real-life perspectives, in both contemporary and historical contexts. Contemporary recognition theory has developed into an established and active multidisciplinary research programme. The chapters in this volume have two main purposes. First, they engage in theoretical development of the contemporary theories of recognition. They explore the conceptual histories and the environments of recognition, as well as the connection between recognition and authenticity, emancipation, and social ontology. Second, they connect the theoretical insights of contemporary recognition with analyses of contemporary and historical social practices. These contributions explore themes such as populism and polarization, models of harmful invisibilization and social ignorance, the problem of evil and suffering, and social justice phenomena such as the #MeToo movement. The Theory and Practice of Recognition will be of interest to scholars and advanced students working in social and political philosophy, social ontology, political theory, and sociology.

Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos. Logo Recognition: Theory and Practice is the first book to focus on logo recognition, especially under noisy conditions. Beginning with an introduction to fundamental concepts and methods in pattern and shape recognition, it surveys advances in logo recognition. The authors also propose a new logo recognition system that can be used under adverse conditions such as broken lines, added noise, and occlusion. The proposed system introduces a novel polygonal approximation, a robust indexing scheme, and a new line segment Hausdorff distance (LHD) matching method that can handle more distortion and transformation types than previous techniques. In the first stage, raw logos are transformed into normalized line segment maps. In the second stage, effective line pattern features are used to index the database to generate a moderate number of likely models. In the third stage, an improved LHD measure screens and generates the best matches. A comprehensive overview of logo recognition, the book also presents successful applications of the technology and suggests directions for future research.

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face de

In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-function form. This common form enables the use of the growth transformation (or extended Baum–Welch) optimization framework in discriminative learning of model parameters. In addition to all the necessary introduction of the background and tutorial material on the subject, we also included technical details on the derivation of the parameter optimization formulas for exponential-family distributions, discrete hidden Markov models (HMMs), and continuous-density HMMs in discriminative learning. Selected experimental results obtained by the authors in firsthand are presented to show that discriminative learning can lead to superior speech recognition performance over conventional parameter learning. Details on major algorithmic implementation issues with practical significance are provided to enable the practitioners to directly reproduce the theory in the earlier part of the book into engineering practice. Table of Contents: Introduction and Background / Statistical Speech Recognition: A Tutorial / Discriminative Learning: A Unified Objective Function / Discriminative Learning Algorithm for Exponential-Family Distributions / Discriminative Learning Algorithm for Hidden Markov Model / Practical Implementation of Discriminative Learning / Selected Experimental Results / Epilogue / Major Symbols Used in the Book and Their Descriptions / Mathematical Notation / Bibliography

In recent years the political landscape has changed: established ideas about class, economy, nation and equality have been challenged by a new politics of identity, culture, ethnicity and difference. The political theory of recognition is a response to these challenges. In this, the first introductory book on the subject, Simon Thompson analyses the argument that a just society is one that shows all its members due recognition. Focusing on the work on Charles Taylor, Axel Honneth and Nancy Fraser, he discusses how political theorists have conceptualised recognition, the different accounts they have given and the criticisms made of the very idea of a politics of recognition. Through the political theory of recognition, Thompson argues, we gain a better understanding of identity and difference. Practically, the concept of recognition can serve as a basis for determining which individual rights should be protected, whether cultures ought to be valued, and whether a case can be made for group representation. This clear and accessible book provides an excellent guide through the ongoing and increasingly significant debate between multiculturalism and its critics.

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. Plan, Activity, and Intent Recognition explains the crucial role of these techniques in a wide variety of applications including: personal agent assistants computer and network security opponent modeling in games and simulation systems coordination in robots and software agents.