LEARNING OUTCOMES
Following the two previous courses (Photogrammetry I and II), the purpose here is first to convey necessary knowledge regarding certain concepts and tools of image processing. This will help understand the logic, the mathematical models and the methods of automatic photogrammetric techniques in today’s digital environment. A further task is to effectively insert modern photogrammetric approaches (orientations and reconstruction) into the wider context of a rapidly evolving geospatial technology as well as into a more general inter-disciplinary framework involving automatic extraction of geometric and semantic information from images, which includes fields such as computer vision and pattern recognition. The combination of theoretical lectures and implementation (in the Lab) of algorithms of digital photogrammetry by the students themselves aims at equipping them with the capability to address new problems and adapt to specific requirements Successful completion of this third compulsory course in photogrammetry means that students:
- Have understood the theoretical background of image processing techniques, of image transformation methods and of today’s automatic photogrammetric tools, and thus are in position to plan and carry out digital photogrammetric projects.
- Have understood basic mathematical models from computer vision (e.g. fundamental and essential matrices) and their relation to standard photogrammetric formulations.
- Are capable of controlling the performance of current photogrammetric software (methods and strategies of multi-view matching, robust estimation techniques etc.) and are in principle competent to assess results and tackle basic problems encountered.
- Have also the capability of programming computer modules for basic tasks of photogrammetry and computer vision (radiometric/geometric image transformations, image matching) according to the problem involved.
- Have good understanding of the potential of photogrammetry and computer vision as well as their relation to relevant fields to which they provide important input data (digital terrain models, orthomosaics, textured 3D models etc.), such as cartography and GIS.
- In view of the above, the students may successfully design, execute and evaluate today’s photogrammetric projects, and at the same time follow and integrate into their activity current scientific and technological developments.
General Competences
- Working independently
- Teamwork
- Criticism and self-criticism
- Adapting to new situations
SYLLABUS
Introduction to digital photogrammetry and computer vision. Radiometric and spatial resolution. The meaning of scale in a digital image. Radiometric image transformations (histogram transformations, thresholding, image convolution, linear and non-linear filters, smoothing, enhancements, automatic edge detection). Geometric image transformations. Colour interpolation and image resampling. Image pyramid. Panoramic images. Digital rectification, orthorectification, digital surface development and cartographic projections. Photogrammetric automation and real-time solutions. Epipolar geometry and epipolar rectification of the stereopair. Computer vision and photogrammetry (essential matrix, fundamental matrix), robust estimators, RANSAC. Point operators and descriptors. Point extraction and point matching. Automatic relative orientation. Automatic phototriangulation. Digital image matching / correlation and generation of depth maps. Methods for automatic stereo and multi-view matching. Introduction of geometric constraints. Automatic DSM generation and 3D reconstruction.
STUDENT PERFORMANCE EVALUATION
Language of evaluation: Greek
Methods of Evaluation:
• Written examination in the end of the semester (70%), which combines open-ended questions and numeric calculations.
• Evaluation of performance in the Lab exercises (30%)
SUGGESTED BIBLIOGRAPHY
1. ASPRS, 2013. Manual of Photogrammetry. 6th edition, J. Chris McGlone (editor).
2. Luhmann T., Robson S., Kyle S., Harley I., 2006. Close Range Photogrammetry: Principles, Techniques and Applications. Whittles Publishing, Scotland.
3. Szeliski R., 2010. Computer Vision: Algorithms and Applications (draft). Springer (http://szeliski.org/Book/).
4. Hartley R., Zisserman A., 2000. Multiple View Geometry in Computer Vision. Cambridge University Press.
5. Fӧrstner W., Wrobel B. P., 2016. Photogrammetric Computer Vision. Springer.
6. Schenk T., 1999. Digital Photogrammetry. TerraScience, Laurelville, Ohio, USA.
In Greek:
1. Kraus K., 2003. Photogrammetry. Vol 1. TEE Editions, Athens.
2. Petsa E., 2019. Couse Slides for “Digital Photogrammetry and Computer Vision”. UniWA.
3. Grammatikopoulos L., Kalisperakis I., Karras G., Petsa E., Tsironis V., 2018. Elements of Projective Geometry in Computer Vision. UniWA