DAMSL-272 Computer Vision

Type

Elective

Course Code

DAMSL-272

Teaching Semester

B semester

ECTS Credits

Homework and/or Lab Assignments, Final Exam and/or Project

Linear Algebra

Overview of Image Acquisition and Processing Topics(Sampling, Quantization, Color Perception, Smoothing Filters, Derivatives)
Overview of Image Analysis Topics(Edge Detection, Segmentation)
Representation, Analysis, and Synthesis of Textures
Interest Point Detection(Harris Corner Detector)
Blob Detection
Descriptors of Interest Points(SIFT)
Hough Transform
Methods for Estimating Parametric Models (Least Squares Method)
Robust Parameter Estimation Methods (LMedS, RANSAC)
Alignment of Model-Image Based on Features
Camera and Lens Models, Projective Geometry
Camera Calibration
Epipolar Geometry
Stereo Vision: The Correspondence Problem and 3D Reconstruction
Volumetric 3D Reconstruction from Multiple Cameras
Estimation of 2D Motion(Vertical Optical Flow, Optical Flow)
Modeling 3D Motion(Motion Field, Eigenmotion)
Tracking of Linear Dynamic Models
Tracking with Particle Filters
Object Detection(Human Body, Face)
Object Detection
Object classification

Having attended and succeeded in the course, the student is able to describe how specific, selected problems in computer vision are addressed in the relevant literature.
Having attended and succeeded in the course, the student has achieved an in-depth understanding of the mechanisms for solving specific computer vision problems and can explain the reasons that make these mechanisms sufficient for solving other problems.
Having attended and succeeded in the course, the student can reuse existing methodologies and tools to generate other solutions for solving specific instances of computer vision problems or developing applications.
Having attended and succeeded in the course, the student can critically evaluate specific problems and perceive them as compositions of a series of individual sub-problems.
Having attended and succeeded in the course, the student can combine individual tools and methodologies to achieve the solution of complex computer vision problems.
Having attended and succeeded in the course, the student can measure/quantitatively assess the quality of solutions to computer vision problems and compare these solutions to other existing ones