Classical and Deep Learning-based Computer Vision
Instructor: Richard Xu, University of Technology Sydney
Level: Intermediate
Duration: 4 hours total; two 2-hour sessions with a 15-minute break and 30-minute post-class discussion. This class takes place over two days.
Course Time:
Day 1 of 2:
New York: Monday 11 January, 10:00 – 12:15
Paris: Monday 11 January, 16:00 – 18:15
Tokyo: Tuesday 12 January, 00:00 – 02:15
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Day 2 of 2:
New York: Tuesday 12 January, 10:00 – 12:15
Paris: Tuesday 12 January, 16:00 – 18:15
Tokyo: Wednesday 13 January, 00:00 – 02:15 |
Benefits
This course enables the attendee to:
- Understand several important aspects and tasks in computer vision.
- Explain why computer vision is challenging.
- Explain the role deep learning plays in computer vision.
- Be able to apply OpenCV and TensorFlow 2.0 to build CV models, including object classification, object detection, face recognition, generating synthetic images, and image-to-text.
Computer Vision (CV) is an important application of Artificial Intelligence and Machine Learning models. CV concerns techniques to help computers to "see" and "understand" the content of still images (photos) and sequences of images (videos) and often in the modern context, to combine visual info with other media: e.g., text-to-image and/or image-to-text. In this course, our aim is to teach participants both the classical and modern treatments in computer vision: from camera anatomy (e.g., camera calibration) to manually designed filters (for example, scale-invariant-feature-transform) to powerful modern-day deep learning approach to computer vision.
Intended Audience
Professionals and academics with some background in computer science or programming who are interested in entering the field of computer vision.
Richard Xu is an associate professor in machine learning and a leading researcher in the fields of machine learning, deep learning, data analytics, and computer vision. He is the founder and director of the UTS DataLounge, which provides customised short courses for organisations seeking expertise in the field of machine learning. Xu is also a core member of the Innovation in IT Services and Applications research centre and the Global Big Data Technologies research centre, both at UTS.
COST
by December 31:
member $135
non-member $150
student $70
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after December 31:
member $160
non-member $175
student $95 |
Discounts given for multiple classes. See Registration page for details and to register.
For office use only: