scispace - formally typeset
Open AccessBook

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

Reads0
Chats0
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.

read more

Citations
More filters
Journal ArticleDOI

Revisiting Hartley's normalized eight-point algorithm

TL;DR: A novel explanation is given for the improvement in performance of the eight-point algorithm that results from using normalized data, and it is shown that this cost function is statistically better founded than the cost function associated with the nonnormalized algorithm.
Journal ArticleDOI

Automatic scene structure and camera motion using a catadioptric system

TL;DR: Fully automatic methods are presented for the estimation of scene structure and camera motion from an image sequence acquired by a catadioptric system, and many experiments dealing with robustness, accuracy, uncertainty, comparisons between both central and non-central models, and piecewise planar 3D modeling are provided.
Journal ArticleDOI

Scalable Extrinsic Calibration of Omni-Directional Image Networks

TL;DR: A linear-time algorithm that recovers absolute camera orientations and positions, along with uncertainty estimates, for networks of terrestrial image nodes spanning hundreds of meters in outdoor urban scenes and achieves accurate registration even in the face of significant lighting variations, low-level feature noise, and error in initial pose estimates is described.
Proceedings ArticleDOI

Visual sensing of continuum robot shape using self-organizing maps

TL;DR: This work presents a robust and efficient stereo-vision-based, shape-sensing algorithm for continuum robots that does not rely on fiducials or assume orthogonal camera placement, and employs self-organizing maps to triangulate three-dimensional backbone curves.
Journal ArticleDOI

Performance characterization in computer vision: A guide to best practices

TL;DR: What is seen as current best practices in algorithmic novelty and the increasing importance of validation on particular data sets and problems are reviewed and refinements that may benefit the field of computer vision are suggested.
Related Papers (5)