The goal of computer vision is recognize and understand the world through visual information such as images or videos.
After completing the course, the student:
- understands the motivation and goal of computer vision, including the applications and general challenges.
- understands the mechanisms of image formation in terms of both geometry and radiometry.
- understands and is able to construct 3D voxel data from images or videos based on silhouettes.
- understands and is able to cluster data points based on various distance measures.
- understands the concepts of image features and their importance in computer vision.
- understands the concepts and challenges of optical flow.
- understands the challenges of image image classification and object detection.
- understands and able to express the performance of classification and detection algorithms.
- understands the components and the learning mechanisms of convolutional neural networks (CNN).
- understands the training of CNNs, and is able to develop and evaluate CNNs for image and video classification tasks.
Assessment
Final exam (40% of the final mark), assignments (60%)
A repair test requires at least a 4 for the original test.
Prerequisites
(Basic) knowledge and experience of programming in Python.
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This course is about the algorithms and mechanisms to extract and classify information from images and video.
The course combines theory and practice, with two themes: multi-view reconstruction and CNN image/video classification.
Course form
Lectures, tutorials, practicals.
Study material
Python, OpenCV, TensorFlow, Keras
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