COMP08176 2020 Computer Vision

Computer Vision

Computer Vision

COMP08176

N/A %

COMP - 0613 Computer Science

COEL - Computing & Electronic Eng

08 - Level 8

05 - 05 Credits

Semester

€

2020 - Full Academic Year 2020-21

9999 - The End of Time

Eva Murphy, Shane Gilroy, Sean Mullery

SG_EELEC_H08 202000 Bachelor of Engineering (Honours) in Electronics and Self Driving Technologies SG_EROBO_H08 202000 Bachelor of Engineering (Honours) in Robotics and Automation SG_EMECH_H08 202000 Bachelor of Engineering (Honours) in Mechanical Engineering SG_EROBO_H08 202400 Bachelor of Engineering (Honours) in Robotics and Automation SG_EROBO_H08 202500 Bachelor of Engineering (Honours) in Robotics and Automation

This module presents the fundamental processes that allow computers to view and make sense of the world. Computer Vision includes both the physical hardware for acquiring the image, the environment in which the image is acquired and the classical algorithms for understanding the acquired image.  This may be taken as a standalone module or in conjunction with an Image Processing and Deep Learning module to complete the low, mid and high-level computer vision domains.

40 %

60 %

Required Reading
2010-10-19 Computer Vision Springer
ISBN 1848829345 ISBN-13 9781848829343

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.

Required Reading
2017-10 Computer Vision Academic Press
ISBN 012809284X ISBN-13 9780128092842

Computer Vision: Principles, Algorithms, Applications, Learning (previously entitled Computer and Machine Vision) clearly and systematically presents the basic methodology of computer vision, covering the essential elements of the theory while emphasizing algorithmic and practical design constraints. This fully revised fifth edition has brought in more of the concepts and applications of computer vision, making it a very comprehensive and up-to-date text suitable for undergraduate and graduate students, researchers and R&D engineers working in this vibrant subject. See an interview with the author explaining his approach to teaching and learning computer vision - http://scitechconnect.elsevier.com/computer-vision/ Three new chapters on Machine Learning emphasise the way the subject has been developing; Two chapters cover Basic Classification Concepts and Probabilistic Models; and the The third covers the principles of Deep Learning Networks and shows their impact on computer vision, reflected in a new chapter Face Detection and Recognition. A new chapter on Object Segmentation and Shape Models reflects the methodology of machine learning and gives practical demonstrations of its application. In-depth discussions have been included on geometric transformations, the EM algorithm, boosting, semantic segmentation, face frontalisation, RNNs and other key topics. Examples and applications-including the location of biscuits, foreign bodies, faces, eyes, road lanes, surveillance, vehicles and pedestrians-give the 'ins and outs' of developing real-world vision systems, showing the realities of practical implementation. Necessary mathematics and essential theory are made approachable by careful explanations and well-illustrated examples. The 'recent developments' sections included in each chapter aim to bring students and practitioners up to date with this fast-moving subject. Tailored programming examples-code, methods, illustrations, tasks, hints and solutions (mainly involving MATLAB and C++)