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Artificial Vision Applications

Code:

MEC121013

Sigla:

AVA

Áreas Científicas
Classificação	Área Científica
OFICIAL	Etectrónica e Telecomunicações

Ocorrência: 2023/2024 - 2S

Ativa?	Yes
Unidade Responsável:	Departamento de Engenharia Eletrotécnica
Curso/CE Responsável:

Ciclos de Estudo/Cursos

Sigla	Nº de Estudantes	Plano de Estudos	Anos Curriculares	Créditos UCN	Créditos ECTS	Horas de Contacto	Horas Totais
MEEC	6	Plano de Estudos_2020	1	-	7,5	60	202,5

Língua de trabalho

Portuguese

Objetivos

Provide students with general knowledge of artificial vision, namely image acquisition technology, vision system calibration and lighting techniques. Introduce basic image processing and pattern recognition techniques that enable the development of systems based on these techniques. Use tools suitable for digital im

Resultados de aprendizagem e competências

The main objectives of this curricular unit are associated with the development of skills that allow the use of artificial vision and digital image processing in the development of specific applications. The methodology of using the expository method and carrying out laboratory work, dealing with the material taught in theoretical classes, is considered to be an adequate process of transmitting to students the essential knowledge to achieve the proposed objectives. The use of the Moodle e-learning platform makes it possible to promote greater contact between the teaching staff and their trainees, either through forum activities or through the availability of the classes taught.

Modo de trabalho

Presencial

Programa

Artificial Vision
- The Human Visual System
- Concept and digital representation of an image, color, noise.
- Sampling, quantization and resolution.
- Image acquisition technologies and lighting techniques
- Calibration of artificial vision systems
Digital Image Processing
- Point-to-point manipulation
- Spatial filters
- Local or global segmentation
Pattern Recognition
- Extraction of features based on contour or region;
- Classification based on neural networks, K-Nearest Neighbors, Decision Trees.
Artificial Vision in the context of Internet of Things (IoT) and Industry 4.0.

Bibliografia Obrigatória

Gonzalez, R. e Woods, R; Digital Image Processing. Second Edition, Prentice Hal, 2002. ISBN: 0-201-18075-8
Davies, E. R; Machine Vision –Theory, Algorithms, Practicalities, 3rd Edition, Morgan Kaufmann, 2005. ISBN: 9780122060939
Gérard Blanchet and Maurice Charbit; Digital Signal and Image Processing Using Matlab, Volume 1 Fundamentals, 2nd Edition, John Wiley&Sons, Inc., 2014. ISBN: 9781848216402
Bernd Jahne, Horst HauBecker and Peter Geibler; Handbook of Computer Vision and Applications, Vol. 1, Sensors and Imaging, Academic Press, 1999. ISBN: 0–12–379771-3

Bibliografia Complementar

Chen C. H., Wang P. S. P ; Handbook of Pattern Recognition and Computer Vision, 3rd Edition, 2005
Gonzalez, R. e Woods, R.; Digital Image Processing, Global Edition. 4th Edition., Pearson, 2017. ISBN: 9781292223049
Ballard, Dana H. and Cristopher, M. Brown; Computer Vision, 1982

Métodos de ensino e atividades de aprendizagem

The presentation at the end of the semester of a topic on artificial vision that will be elaborated throughout the semester, outside class hours.
Laboratory work will be carried out during classes and during the first weeks of the semester. The evaluation of the laboratory work is carried out during the period of its realization.
The Laboratory Project will be developed in the last weeks of the semester (during the hours of synchronous classes and outside of them) and will have to be delivered in a timely defined date. The Laboratory Project will be presented and evaluated on the dates defined for the UC exam.

Software

LabView
Matlab
Processing

Tipo de avaliação

Distributed evaluation without final exam

Componentes de Avaliação

Designation	Peso (%)
Trabalho laboratorial	35,00
Apresentação/discussão de um trabalho científico	20,00
Trabalho de campo	5,00
Trabalho escrito	40,00
Total:	100,00

Componentes de Ocupação

Designation	Tempo (Horas)
Elaboração de projeto	30,00
Trabalho laboratorial	30,00
Total:	60,00

Obtenção de frequência

NF>= 10

Fórmula de cálculo da classificação final

NF=0.2xTA+0.05*TC+0.4*TE+0.35xTL