Instance Segmentation

Human Vision

Color Spaces

Transforms

Image Coordinates

Resizing

Filters

Convolution

Edges and Features

Harris Corner Detector

Feature Matching

RANSAC (Random Sample Consensus)

HOG (Histogram of Oriented Gradients)

SIFT (Scale-invariant Feature Transform)

Optical Flow

Depth Perception

Stereo

Neural network

Convolutional neural network (CNN)

Network Architectures

Semantic Segmentation

Object Detection

Vision and Language

Generative adversarial network (GAN)

CSE 455 Computer Vision University of Washington Winter 2022 A general introduction to computer vision, this course covers traditional image processing techniques and newer, machine-learning based approaches. It discusses topics like filtering, edge detection, stereo, flow, and neural network architectures. This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. (old-school vision), as well as newer, machine-learning based computer vision.   

CSE 455 Computer Vision

Computer Vision

Instance Segmentation

1 courses cover this concept

CSE 455 Computer Vision