Stefano Soatto's Books

An Invitation to 3-D Vision

From Images to Geometric Models

By: Yi Ma,Stefano Soatto,Jana Kosecká,S. Shankar Sastry

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An Invitation to 3-D Vision

From Images to Geometric Models

By: Yi Ma,Stefano Soatto,Jana Kosecká,S. Shankar Sastry

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.

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Page Count

542

Publisher

Springer Science & Business Media

Published Date

ISBN 10

0387217797

ISBN 13

9780387217796

3-D Shape Estimation and Image Restoration

Exploiting Defocus and Motion-Blur

By: Paolo Favaro,Stefano Soatto

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3-D Shape Estimation and Image Restoration

Exploiting Defocus and Motion-Blur

By: Paolo Favaro,Stefano Soatto

In the areas of image processing and computer vision, there is a particular need for software that can, given an unfocused or motion-blurred image, infer the three-dimensional shape of a scene. This book describes the analytical processes that go into designing such software, delineates the options open to programmers, and presents original algorithms. Written for readers with interests in image processing and computer vision and with backgrounds in engineering, science or mathematics, this highly practical text/reference is accessible to advanced students or those with a degree that includes basic linear algebra and calculus courses.

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Page Count

262

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1846286883

ISBN 13

9781846286889

Deep Learning to See

Towards New Foundations of Computer Vision

By: Alessandro Betti,Marco Gori,Stefano Melacci

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Deep Learning to See

Towards New Foundations of Computer Vision

By: Alessandro Betti,Marco Gori,Stefano Melacci

The remarkable progress in computer vision over the last few years is, by and large, attributed to deep learning, fueled by the availability of huge sets of labeled data, and paired with the explosive growth of the GPU paradigm. While subscribing to this view, this work criticizes the supposed scientific progress in the field, and proposes the investigation of vision within the framework of information-based laws of nature. This work poses fundamental questions about vision that remain far from understood, leading the reader on a journey populated by novel challenges resonating with the foundations of machine learning. The central thesis proposed is that for a deeper understanding of visual computational processes, it is necessary to look beyond the applications of general purpose machine learning algorithms, and focus instead on appropriate learning theories that take into account the spatiotemporal nature of the visual signal. Serving to inspire and stimulate critical reflection and discussion, yet requiring no prior advanced technical knowledge, the text can naturally be paired with classic textbooks on computer vision to better frame the current state of the art, open problems, and novel potential solutions. As such, it will be of great benefit to graduate and advanced undergraduate students in computer science, computational neuroscience, physics, and other related disciplines.

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