MOOC - Computer Vision Expert
Computer Vision Fundamentals
The second unit of this job role provides insights on the underlying principles of computer vision as well as its main characteristics.
Object Detection and Tracking, Motion Estimation, Facial Recognition, Scene Understanding and 3D Reconstruction:
This learning element provides insights on the fundamental aspects of object detection and tracking, motion estimation, facial recognition, scene understanding and 3D reconstruction.
CVE.U2.E10.PC1
The student understands the machine learning and deep learning approaches for object detection and is able to select the best approach according to a specific problem or situation.
CVE.U2.E10.PC2
The student understands the process of object tracking and knows the classification of traditional methods for tracking objects.
CVE.U2.E10.PC3
The student is able to define target representation and localization algorithms and list some examples.
CVE.U2.E10.PC4
The student is able to define filtering and data association algorithms and list some examples
CVE.U2.E10.PC5
The student understands the differences between target representation and localization algorithms and filtering and data association algorithms.
CVE.U2.E10.PC6
The student is able to define and understand what facial recognition systems are as well as identify the applications of these systems.
CVE.U2.E10.PC7
The student knows the different techniques for face acquisition.
CVE.U2.E10.PC8
The student understands the motion estimation problem and knows the different methods and algorithms for finding motion vectors.
CVE.U2.E10.PC9
The student is able to define the need and purposes of scene understanding in typical computer vision problems.
CVE.U2.E10.PC10
The student understands the need, definition, and applications of 3D reconstruction.
CVE.U2.E10.PC11
The student knows the active and passive methods for 3D reconstruction and understand their differences.