GEOMETRIC AND 3D COMPUTER VISION

This course provides an in-depth study of the theory and techniques of modern computer vision, focusing on the non-trivial process of recovering geometric information of a scene from its 2-dimensional image representation.
The course develops in a bottom-up fashion, starting from the fundamental concepts of "early vision" and progressing with the classical methods to detect geometrical primitives, like curves and point-based features. Finally, the mathematical framework of projective geometry is discussed in the context of recovering the 3D structure of a scene.

After the course, the students will be able to:

- Develop and understand some fundamental image processing algorithms
- Perform the most common operations of Mid-level vision, including the detection and
tracking of linear and point features
- Understand the general concepts of 2D and 3D projective geometry
- Develop algorithms to perform stereo-based 3D reconstruction

Una conoscenza di base di algebra lineare è consigliata per poter meglio comprendere i contenuti del corso

Early vision
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- Introduction to vision
- The image formation process
- Intensity transformations
- Color vision
- Spatial filtering
- Filtering in frequency domain
- Morphological image processing
- Edge detection


Mid-level vision
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- Fitting of curves and Hough transform.
- Detection and matching of point features
- Tracking


Projective geometry
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- Elements of Analytical Euclidean Geometry
- Geometric primitives
- 2D and 3D projective transformations


Camera models
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- Affine and projective cameras
- Intrinsic calibration
- Pose estimation


Two-view geometry
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- Epipolar geometry
- Stereopsis
- 3D Reconstruction and triangulation


Laboratory activities: Development of algorithms in Python, Numpy and the OpenCV library

[1] R. C. Gonzalez, R.E. Woods. Digital Image Processing (3rd edition). Pretience Hall
[2] R. Szeliski. Computer Vision Algorithms and Applications. Springer
[3] D. Forsyth, J. Ponce. Computer Vision: A Modern Approach (2nd edition). Pearson
[4] R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision (2nd edition). Cambridge University Press, New York, NY, USA.

The verification of learning involves the development of a software project that uses tools and methods developed during the course. The project will then be discussed by oral examination.

The course is composed of frontal lessons, typically comprising practical examples to better understand all the studied concepts. Together with the referral texts, additional material will be provided by means of PowerPoint slides and source code.

English

oral