Real-World Computer Vision Applications

Common CV Challenges

Evaluation Metrics: IoU and mAP

Transfer Learning with Pretrained Vision Models

Image Segmentation: Semantic vs Instance

Object Detection and Bounding Boxes

Image Classification Task

Edge Detection and Classical Features

CNN Architecture for Vision Tasks

Pooling Layers and Downsampling

Feature Maps and Learned Visual Features

Convolution and Kernels Explained

Image Augmentation Techniques

Image Preprocessing Essentials

Color Spaces: RGB, Grayscale, HSV

How Digital Images Are Represented

What Computer Vision Is