Course logistics and introduction to deep learning and image representations.
1. What is Computer Vision?
2. CV then and now, a historical perspective.
3. Classification, then and now - Non-neural vs. Neural in imagenet.
4. Some results of CNNs. What is really new? What is really wrong? What all can they do?
1. Course Logistics and course plan.
2. Basics of Image Acquisition (sampling and quantization).
3. Fourier representations.
4. Histogram representations.
4. Edge map representations.