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Computer Vision

Code 11499
Year 1
Semester S2
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements Notions of structured programming. Block, iterative and conditional blocks. Notions of artificial intelligence. Elementary notions of: linear algebra, probability and statistics, geometry.
Mode of delivery Face-to-face.
Work placements (Not applicable)
Learning outcomes Study of mechanisms of biological vision and its relationship to computational vision systems. Understanding of the main concepts about vision systems. Dynamics of the acquired information and noise handling. Understanding of the main techniques for detection, segmentation and classification of objects in images.
By the end of this course, students should be able to identify the requisites for computer vision systems. Identify techniques for image processing and vision, most suitable for specific problems.
Planning and implement solutions for computer vision systems.
Syllabus Introduction. Computer Vision (CV). What is it? Applications of CV. Biologic Perspective Vision. Cameras and Images. Optics. Digital Images. Sampling. Calibration. Filtering. Convolution, Correlation. Spatial and Frequency Domains. Fourier Transform. Noise Sources. Image Representation: features. Data Descriptors: Color, intensity, texture and shape. Detection, Segmentation and Recognition
Main Bibliography Main

David A. Forsyth and Jean Ponce; Computer Vision: A Modern Approach, Prentice-Hall, 2002.
Dana Ballard and Chris Brown; Computer Vision, Online.
J. R. Parker; Algorithms for Image Processing and Computer Vision, Wiley, 1995.

Complementary

Torras, C.; Computer Vision, Theory and Industrial Applications, New York, Springer, 1992.
Davies, E.R.; Machine Vision: Theory, Algorithms, Practicalities, Third Edition, Morgan Kaufmann, 2005.
On-line resources: http://homepages.inf.ed.ac.uk/rbf/CVonline/books.htm#online
Teaching Methodologies and Assessment Criteria Introduction. Computer Vision (CV). What is it? Applications of CV. Biologic Perspective Vision. Cameras and Images. Optics. Digital Images. Sampling. Calibration. Filtering. Convolution, Correlation. Spatial and Frequency Domains. Fourier Transform. Noise Sources. Image Representation: features. Data Descriptors: Color, intensity, texture and shape. Detection, Segmentation and Recognition
Language Portuguese. Tutorial support is available in English.
Last updated on: 2019-07-09

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