Talk: Semantic Segmentation

We recently gave a talk on Semantic Segmentation to Danbury AI.

One of the first fundamental problems of computer vision was the classification of images — where we are given a matrix of pixel data and we must each pixel a categorical class label. When we can classify images, in our computer vision applications we can reason about the class labels instead of comparing pixel values. Semantic segmentation takes image classification to the next level of complexity: not only do we need to classify an image, but also define the specific regions of an image which relate to one or more trained classes representing objects of interest. When we can obtain successful semantic segmentations, we can achieve amazing feats of machine reasoning, among which are self-driving cars.


About the Author: Andrew Ribeiro

I am a computer scientist working in the field of Artificial Intelligence as the co-founder of Knowledge-Exploration Systems -- a Danbury based AI and systems development contractor. I studied traditional computer science at WCSU and extended my knowledge to include AI by taking online courses, reading the classic books, and reading research papers in the field while applying the ideas to commercial R&D applications. Due to the intensive intellectual demands of mastering such a diverse field, I also co-founded Danbury AI in 2016 to foster a local community of AI experts and enthusiasts that help each other grow and learn.

Leave A Reply

Your email address will not be published. Required fields are marked *