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The Geometry of Multiple Images: The Laws That Govern the Formation of Multiple Images of a Scene and Some of Their Applications

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TLDR
The state of knowledge in one subarea of vision is described, the geometric laws that relate different views of a scene from the perspective of various types of geometries, which is a unified framework for thinking about many geometric problems relevant to vision.
Abstract
From the Publisher: with contributions from Theo Papadopoulo Over the last forty years, researchers have made great strides in elucidating the laws of image formation, processing, and understanding by animals, humans, and machines. This book describes the state of knowledge in one subarea of vision, the geometric laws that relate different views of a scene. Geometry, one of the oldest branches of mathematics, is the natural language for describing three-dimensional shapes and spatial relations. Projective geometry, the geometry that best models image formation, provides a unified framework for thinking about many geometric problems relevant to vision. The book formalizes and analyzes the relations between multiple views of a scene from the perspective of various types of geometries. A key feature is that it considers Euclidean and affine geometries as special cases of projective geometry. Images play a prominent role in computer communications. Producers and users of images, in particular three-dimensional images, require a framework for stating and solving problems. The book offers a number of conceptual tools and theoretical results useful for the design of machine vision algorithms. It also illustrates these tools and results with many examples of real applications.

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Book ChapterDOI

Image Based Visual Servoing: Estimated Image Jacobian by Using Fundamental Matrix VS Analytic Jacobian

TL;DR: Tests in static and dynamic cases showed that the performance of estimated Jacobian by using the properties of the epipolar geometry is such as good and robust against noise as the analytic Jacobian.
Proceedings ArticleDOI

Camera egomotion estimation in the ADAS context

TL;DR: Egomotion estimation from a monocular camera under the ADAS context is reviewed and compared with simulated and real ADAS-like sequences, showing which of the considered nonlinear and linear algorithms have the best performance in this domain.
Journal ArticleDOI

Sensor Guided Robot Path Generation for Surface Repair Tasks on a Large-Scale Buoyancy Module

TL;DR: Experimental results have shown that the proposed solution is able to accurately and efficiently remove defective regions on the surface of DRBMs.
Journal ArticleDOI

Monocular obstacle detection using reciprocal-polar rectification

TL;DR: Recovered FOE, vanishing line and sinusoid amplitude fully define the ground plane motion (homography) across a pair of images and thus obstacles and ground plane can be segmented without any explicit knowledge of either camera parameters or camera motion.
Book ChapterDOI

Euclidean structure recovery from motion in perspective image sequences via Hankel rank minimization

TL;DR: A new constraint is introduced that implicitly exploits the temporal ordering of the frames, leading to a provably correct algorithm to find Euclidean structure (up to a single scaling factor) without the need to alternate between projective depth and motion estimation, estimate the Fundamental matrices or assume a camera motion model.
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