Standard syllabus
Computer vision · Graduate · CS / Programming
Topics
Imaging foundations
- Pinhole camera model and calibration (intro)
- Filtering, edge detection, and convolution
- Color spaces and histogram methods
- Feature descriptors: SIFT/HOG (survey)
- Stereo and depth from motion (intro)
Recognition pipelines
- Image classification with CNNs
- Object detection: R-CNN family survey
- Segmentation: semantic and instance (intro)
- Transfer learning and fine-tuning
- Data augmentation and dataset bias
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.