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Author

Emanuele Trucco

Other affiliations: University of Udine, University of Genoa, Heriot-Watt University  ...read more
Bio: Emanuele Trucco is an academic researcher from University of Dundee. The author has contributed to research in topics: Medicine & Fundus (eye). The author has an hindex of 43, co-authored 260 publications receiving 10942 citations. Previous affiliations of Emanuele Trucco include University of Udine & University of Genoa.


Papers
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Book
01 Jan 1998
TL;DR: A guide to well-tested theory and algorithms including solutions of problems encountered in modern computer vision, showing how fundamental problems are solved using both intensity and range images, the most popular types of images used today.
Abstract: From the Publisher: FEATURES: Provides a guide to well-tested theory and algorithms including solutions of problems encountered in modern computer vision. Contains many practical hints highlighted in the book. Develops two parallel tracks in the presentation, showing how fundamental problems are solved using both intensity and range images, the most popular types of images used today. Each chapter contains notes on the literature, review questions, numerical exercises, and projects. Provides an Internet list for accessing links to test images, demos, archives and additional learning material.

2,176 citations

Journal ArticleDOI
TL;DR: Computer and Robot Vision Vol.
Abstract: Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

1,426 citations

Proceedings ArticleDOI
01 Jul 2000
TL;DR: A linear rectification algorithm for general, unconstrained stereo rigs that takes the two perspective projection matrices of the original cameras, and computes a pair of rectifying projectionMatrices, compact and easily reproducible.
Abstract: We present a linear rectification algorithm for general, unconstrained stereo rigs. The algorithm takes the two perspective projection matrices of the original cameras, and computes a pair of rectifying projection matrices. It is compact (22-line MATLAB code) and easily reproducible. We report tests proving the correct behavior of our method, as well as the negligible decrease of the accuracy of 3D reconstruction performed from the rectified images directly.

747 citations

Proceedings ArticleDOI
17 Jun 1997
TL;DR: A new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions by an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty.
Abstract: We present a new, efficient stereo algorithm addressing robust disparity estimation in the presence of occlusions. The algorithm is an adaptive, multi-window scheme using left-right consistency to compute disparity and its associated uncertainty. We demonstrate and discuss performances with both synthetic and real stereo pairs, and show how our results improve on those of closely related techniques for both robustness and efficiency.

326 citations

Journal ArticleDOI
TL;DR: Geometric Invariance in Computer Vision, edited by Joseph L. Mundy and Andrew Zisserman, the MIT Press, 1992, $70.95 in Europe.
Abstract: Geometric Invariance in Computer Vision, edited by Joseph L. Mundy and Andrew Zisserman, the MIT Press, 1992, $70.95 in Europe.

309 citations


Cited by
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01 Jan 2001
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Abstract: Downloading the book in this website lists can give you more advantages. It will show you the best book collections and completed collections. So many books can be found in this website. So, this is not only this multiple view geometry in computer vision. However, this book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts. This is simple, read the soft file of the book and you get it.

14,282 citations

Book ChapterDOI
07 May 2006
TL;DR: A novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features), which approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster.
Abstract: In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Robust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be computed and compared much faster. This is achieved by relying on integral images for image convolutions; by building on the strengths of the leading existing detectors and descriptors (in casu, using a Hessian matrix-based measure for the detector, and a distribution-based descriptor); and by simplifying these methods to the essential. This leads to a combination of novel detection, description, and matching steps. The paper presents experimental results on a standard evaluation set, as well as on imagery obtained in the context of a real-life object recognition application. Both show SURF's strong performance.

13,011 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.

8,730 citations

Book
30 Sep 2010
TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Abstract: 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; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. 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.

4,146 citations