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 and is able to identify multiple people in 3D voxel space based on color.
- 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 mechanisms of convolutional neural networks (CNN)
- understands the training of CNNs, and is able to develop CNNs for image and video classification tasks.
|
|
|
The goal of computer vision is recognize and understand the world through visual information such as images or videos. 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.
|
|