This book introduces a theory of music analysis--a language and conceptual framework--that analysts can use to delve into aspects of segmentation and associative organization in a wide range of repertoire from the Baroque to the present. Rather than a methodology, the theory provides analysts with a precise language and broad, flexible conceptual framework that they can when formulating and investigating questions of interest and develop their own interpretations of individual pieces and passages. The theory begins with a basic distinction among three domains of musical experience and discourse about it: the sonic (psychoacoustic); the contextual (or associative, sparked by varying degrees of repetition); and the structural (guided by a specific theory of musical structure or syntax invoked by the analyst). A comprehensive presentation of the theory (with copious musical illustrations) is balanced with close analyses of works by Beethoven, Debussy, Nancarrow, Riley, Feldman, and Morris. Dora A. Hanninen is associate professor of music theory at the University of Maryland. She was recipient of the 2010 Outstanding Publication Award from the Society for Music Theory.
Modern marketing techniques in industrialized countries cannot be implemented without segmentation of the potential market. Goods are no longer produced and sold without a significant consideration of customer needs combined with a recognition that these needs are heterogeneous. Since first emerging in the late 1950s, the concept of segmentation has been one of the most researched topics in the marketing literature. Segmentation has become a central topic to both the theory and practice of marketing, particularly in the recent development of finite mixture models to better identify market segments.
The primary focus of this book is on techniques for segmentation of visual data. By "visual data," we mean data derived from a single image or from a sequence of images. By "segmentation" we mean breaking the visual data into meaningful parts or segments. However, in general, we do not mean "any old data": but data fundamental to the operation of robotic devices such as the range to and motion of objects in a scene. Having said that, much of what is covered in this book is far more general: The above merely describes our driving interests. The central emphasis of this book is that segmentation involves model fitting. We believe this to be true either implicitly (as a conscious or sub conscious guiding principle of those who develop various approaches) or explicitly. What makes model-fitting in computer vision especially hard? There are a number of factors involved in answering this question. The amount of data involved is very large. The number of segments and types (models) are not known in advance (and can sometimes rapidly change over time). The sensors we have involve the introduction of noise. Usually, we require fast ("real-time" or near real-time) computation of solutions independent of any human intervention/supervision. Chapter 1 summarizes many of the attempts of computer vision researchers to solve the problem of segmenta tion in these difficult circumstances."