Introduction to Neural Networks for Computer Vision - Part I

Explore the fundamentals of neural networks in this introductory lecture from the University of Central Florida's Computer Vision course. Delve into the core concepts of object classification, pixel-based representation, and feature extraction. Examine the ImageNet dataset and its role in image classification, while understanding the various learning phases involved. Investigate the spectrum of supervision in machine learning and gain insights into the biological inspiration behind artificial neural networks. Discover how the brain's remarkable computing power is translated into computational models, including the implementation of neural activation functions. Gain a solid foundation in neural network architecture and its applications in computer vision tasks such as filtering, classification, detection, and segmentation.

Intro
Our goal in object classification
Pixel-based representation
What we want
Some feature representations
Image classification - ImageNet
Dataset split
Learning phases Images
Features
Recognition task and supervision
Spectrum of supervision
The machine learning framework
Neurons in the Brain
Brain is a remarkable Computer
Computational Implementation of the Neural Activation Function
Neural Networks