For courses in Image Processing and Computer Vision. Completely self-contained--and heavily illustrated--this introduction to basic concepts and methodologies for digital image processing is written at a level that truly is suitable for seniors and first-year graduate students in almost any technical discipline. The leading textbook in its field for more than twenty years, it continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing--e.g., image fundamentals, image enhancement in the spatial and frequency domains, restoration, color image processing, wavelets, image compression, morphology, segmentation, image description, and the fundamentals of object recognition. It focuses on material that is fundamental and has a broad scope of application.

This is a book about how we see: the environment around us (its surfaces, their layout, and their colors and textures); where we are in the environment; whether or not we are moving and, if we are, where we are going; what things are good for; how to do things (to thread a needle or drive an automobile); or why things look as they do. The basic assumption is that vision depends on the eye which is connected to the brain. The author suggests that natural vision depends on the eyes in the head on a body supported by the ground, the brain being only the central organ of a complete visual system. When no constraints are put on the visual system, people look around, walk up to something interesting and move around it so as to see it from all sides, and go from one vista to another. That is natural vision -- and what this book is about.

Humans perceive the three-dimensional structure of the world with apparent ease. However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art? "Computer Vision: Algorithms and Applications" explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of 'recipes,' this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; and, Supplies supplementary course material for students at the associated website. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use training data to learn the relationships between the observed image data and the aspects of the world that we wish to estimate, such as the 3D structure or the object class, and how to exploit these relationships to make new inferences about the world from new image data. With minimal prerequisites, the book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. Primarily meant for advanced undergraduate and graduate students, the detailed methodological presentation will also be useful for practitioners of computer vision. * Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry * A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking * More than 70 algorithms are described in sufficient detail to implement * More than 350 full-color illustrations amplify the text * The treatment is self-contained, including all of the background mathematics * Additional resources at www.computervisionmodels.com

Computer Vision: A Modern Approach, 2e, is appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This textbook provides the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods

David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis--in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain.

This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.

This book covers Gaussian scale-space theory from its applications to its mathematical foundation. The reader not so familiar with scale-space will find it instructive to first consider some potential applications described in Part I. The next two parts both address fundamental aspects of scale-space. Whereas scale is treated as an essentially arbitrary constant in Part II, Part III emphasises the deep structure, i.e. the structure that is revealed by varying scale. Finally Part IV is devoted to non-linear extensions, notably non-linear diffusion techniques and morphological scale-spaces, and their relation to the linear case. Audience: This volume is addressed to researchers in the field of image analysis seeking mathematical foundation of algorithms.

We perceive objects in the world as having structures at both coarse and fine scales. A tree, for instance, may appear as having a roughly round or cylindrical shape when seen from a distance, even though it is built up from a large number of branches. At a closer look, individual leaves become visible, and we can observe that they in turn have texture at an even finer scale. The fact that objects in the world appear in different ways, depending upon the scale of observation, has important implications when analyzing measured data, such as images, with automatic methods. Scale-Space Theory in Computer Vision describes a formal framework, called scale-space representation, for handling the notion of scale in image data. It gives an introduction to the general foundations of the theory and shows how it applies to essential problems in computer vision such as computation of image features and cues to surface shape. The subjects range from mathematical underpinning to practical computational techniques. The power of the methodology is illustrated by a rich set of examples.

Shows us the science behind the hit series "Lie to Me: the Truth is Written on our Faces" This is the only book helps you "read faces," and interpret their emotions, in an easy-to-read visual format. There are hundreds of illustrations which show how to tell what someone is experiencing. Great for viewers of "Lie to Me," people interested in understanding their friends and coworkers, and students in College and High School. Dr. Paul Ekman, who is the basis for the character Cal Lightman in "Lie to Me," is the researcher who developed the new science of face recognition. There is a lot of media and popular interest in this work, as well as its use in the classroom. "I've been familiar with Ekman's work for several years now; I have found nothing else that even comes close to Unmasking the Face] providing the reader with the knowledge they need to master the science of reading the emotions of others by decoding their facial expressions. Ekman is the king " Vincent Harris --

Why does the world look to us as it does? Generally speaking, this question has received two types of answers in the cognitive sciences in the past fifty or so years. According to the first, the world looks to us the way it does because we construct it to look as it does. According to the second, the world looks as it does primarily because of how the world is. In The Innocent Eye, Nico Orlandi defends a position that aligns with this second, world-centered tradition, but that also respects some of the insights of constructivism. Orlandi develops an embedded understanding of visual processing according to which, while visual percepts are representational states, the states and structures that precede the production of percepts are not representations.
If we study the environmental contingencies in which vision occurs, and we properly distinguish functional states and features of the visual apparatus from representational states and features, we obtain an empirically more plausible, world-centered account. Orlandi shows that this account accords well with models of vision in perceptual psychology -- such as Natural Scene Statistics and Bayesian approaches to perception -- and outlines some of the ways in which it differs from recent 'enactive' approaches to vision. The main difference is that, although the embedded account recognizes the importance of movement for perception, it does not appeal to action to uncover the richness of visual stimulation.
The upshot is that constructive models of vision ascribe mental representations too liberally, ultimately misunderstanding the notion. Orlandi offers a proposal for what mental representations are that, following insights from Brentano, James and a number of contemporary cognitive scientists, appeals to the notions of de-coupleability and absence to distinguish representations from mere tracking states.