- Course
- 2022-2023
- Semester
- 2
- ECTS
- 6
- Type
- Elective
- University
- UVigo
Subject objectives
In this subject students will learn to:
1.- Accurately model an image acquisition system from a geometric point of view;
2.- Model the relative orientation between images and the acquisition and processing methodologies to obtain
a local system 3D model
3.- Describe and obtain a three-dimensional model in a global reference system based on orientation tools
4.-Integrate heterogeneus sensors and multimodal vision-laser information aimed at mapping and navigating
the environment.
Contents
-Calibration of cameras. Geometrical transformations.
Geometrical properties of optical systems:
Collinearity Condition. Geometrical resolution of a camera.
Transformations in the plane: Similarity. Affinity. Projectivity. Polynomial transformations
Calibration of a camera. Parameters. Errors. Iterative correction. Precision.
Correction of perspective, rectification and metrology:
Spatial image resection. Planar image rectification. Single view Measurement.
-Relative and Absolute Orientation
Coplanarity condition.
Epipolar geometry and triangulation.
Model Coordinate system.
Quality Parameters and precision.
Stereoscopic pairs
Absolute orientation.
Geographical and Projected Global Reference Systems. Datum.
Spatial Transformations. Parameter Transformation.
-Bundle Adjustment
Adjustment Models and self-calibration.
Generation of orthoimages.
-3D Point Clouds
Calculation and Collection.
3D Processing
-Robot Vision Applications
Motion estimation.
Spatial image resection and Visual Odometry. Mapping.
Basic and complementary bibliography
Thomas Luhmann, Close Range Photogrammetry, 1-870325-50-8, Whittles Publishing, 2006
Richard Hartley, Multiple view geometry in Computer Vision, 0521-54051-8, 2, Cambridge : Cambridge University Press, 2003
Karl Kraus, Photogrammetry : geometry from images and laser scans, 978-3-11-019007-6, 2, Berlin ; New York : Walter De Gruyter, cop., 2007
Complementary Bibliography
Wolfgang FörstnerBernhard P. Wrobel, Photogrammetric Computer Vision, 978-3-319-11549-8, Springer, 2016
Competencies
CB6 Possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context.
CB9. That the students know how to communicate their conclusions –and the ultimate knowledge and reasons that support them– to specialized and non-specialized audiences in a clear and unambiguous way.
CB10: Students are expected to acquire the learning skills that allow them to continue studying in a way that will be largely self-driven or autonomous.
CT2- Capacity for teamwork, organization and planning.
CE1. Know and apply the concepts, methodologies and technologies of image processing.
CE3. Know and apply the concepts, methodologies and technologies of image and video analysis.
CE5: Students are expected to know how to analyze and apply state of the art methods in computer vision.
CE6- To be knowledgeable and to apply fundamentals of image acquisition and computer vision.
CE9: Students are expected to know and apply the concepts, methodologies and technologies for the recognition of visual patterns in real scenes.
Teaching methodology
Lecturing:
It will consisst of the collaborative discussion of contents of the course of way.
This includes discussion and solving problems and practical case studies in the classroom.
Practices through ICT:
Methodology oriented to solving cases of study related with the thematic of the course using software of reference.
Practices and exercises focused on the implementation of the algorithms explained in the participatory classes.
This subject requires face-to-face assistance fof all students at the University of Vigo to carry out part of their laboratory practices.
Mentored work:
taking into account proposed practical case studies, this method is oriented to solving and documenting a complete photogrammetric project, including the definition of: image acquisition methodologies in the field, supporting data collection for model georeferencing and the main
photogrammetric products obtained through the photogrammetric process .
Seminars:
The description of a concrete practical case related with the professional practice of photogrammetry.
For all the modalities of teaching, tutorial session meetings could be held by telematic means (email, videoconference, forums in FAITIC, …) Under the modality of previous agreement.
Evaluation system
Mentored work (30):
The students will have to complete a case of study by means of the design of a methodology that include the steps seen in the course:
1.- Objectives, Requirements and Products analysis
2.- Definition of the image acquisition networks in the case study
3.- Image processing and analysis
4.- Obtaining key photogrammetric products.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Objective questions exam (30):
The students will have to answer individually a test with questions about the contents of the course.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Problem and/or exercise solving (40):
The students will have to resolve of individual form and in small groups a group of cases and concrete practical exercises.
Evaluated Competences:
CB6
CB9
CB10
CT2
CE1
CE3
CE5
CE6
CE9
Studying time and personal work
Recommended study time for students is about 2 hours per week. Additionally, we estimate that they should spend about 6,5 hours / week working in a number of assignments. All of these activities add up to around 120h/semester.
Subject study recommendations
Subjects that are recommended to be taken simultaneously
Instrumentation and processing for machine vision/V05M185V01104
Real time machine visión/V05M185V01207
Subjects that it is recommended to have taken before
Image description and modeling/V05M185V01102
Fundamentals of image analysis and processing/V05M185V01101