| Code |
12889
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
PL(30H)/T(30H)
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| Scientific area |
Informatics
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Entry requirements |
Notions of structured programming. Block, iterative and conditional blocks. Notions of artificial intelligence. Elementary notions of: linear algebra, probability and statistics, geometry.
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Mode of delivery |
Face-to-face.
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Work placements |
(Not applicable)
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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.
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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
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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
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Teaching Methodologies and Assessment Criteria |
- Assiduity (A) To get approved at this course, students should attend to - at least - 80% of the theoretical and practical classes
- Practical Project (P) The practical projects of this course weights 50% (10/20) of the final mark
- To get approved at the course, a minimal mark of 5/20 should be obtained in the practical project part;
- The pratical project mark is conditioned to an individual presentation and discussion by each student;
- Written Test (F) Wednesday, June 8th, 2022, 14:00. Room 6.18
- Mark (M) M = (A >= 0.8) * (P * 10/20 + F * 10/20)
- Admission to Exams Students with M >= 6 are admitted to final exams
- The practical projects mark is considered in all exam epochs;
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Language |
Portuguese. Tutorial support is available in English.
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