Understanding Computer Vision: how AI sees our world

One reason artificial intelligence and neural nets became so popular is probably professor Fei Fei Li’s CS231n visual recognition open course at Stanford. Along with many other influencers such as Andrew Ngai, they attracted lots of attention to deep learning. Whether it is detecting a cancer tissue in medical images, adding cute animal ears on top of selfies, or driving a car onto the street, Computer Vision (CV) has painted us a bright future where machines can make our lives more convenient in many ways.

Robot Looking at Hands, Image from Quantica

Computer Vision, a Brief History

“If we want machines to think, we need to teach them to see.”

— Fei Fei Li

Diverted from artificial intelligence, research in the field of CV began around the 1960s. One of the earliest relevant papers may be Receptive Fields of Single Neurons in the Cat’s Striate Cortex by Hubel and Wiesel in 1959, where they inserted electrode into a cat’s brain and observed response after changing what the cat sees.

Hubel & Wiesel’s Experiment, Image from CS231n Slides

Portrait of Lenna Forsén, Image from Wikipedia

Sneak Peak of Remondon’s Resume

Computer Vision: The Basics, and Before Neural Networks

Since the time of television, monitor screens display an image by adjusting the brightness of Cathode Ray Tubes (CRT) with 3 different colors — Red (R), Green (G) and Blue (B). Despite our screen nowadays probably uses more advanced hardware such as Liquid Crystal Display (LCD) or Organic Light-Emitting Diodes (OLED), the images are usually stored and transmitted digitally by some format of an RGB table. The Bitmaps, for example, store the images in arrays of hexadecimal values ranging from 0x000000 to 0xFFFFFF, which can represent 3 numbers ranging from 0 to 255.

Illustration of Bitmaps, Image from an ITNEXT Article on Medium

A MacAdam diagram in the CIE 1931 color space, Image from Wikipedia

Edge Detection and Feature Extraction

“A visual image is built by our brain’s ability to package groups of pixels together in the form of edges.”

— Jill Bolte Taylor

It would be difficult to analyze an image upright, as the pixel arrays can be complex and noisy. This is why researchers usually extract features such as edge and lines. These features can be represented by much simpler numerical values or relationships. There are many ways to detect edges such as taking a derivative or Canny’s method. In this article, we will briefly go over Sobel’s method.

Comparison Between Different Edge Detection Algorithms, Image from PyImageSearch

Sobel‘s Method Demonstrated

Hough Transform and Autonomous Driving

“In Soviet Russia, car drives you!”

— Russian Reversal

Imagine we are engineers working for an automobile company that wishes to invent its self-driving cars. At some point, we will have to teach the car to drive inside the lane on the road. Otherwise, our car will drive like that Asian women driver in Family Guy.

Asian Women Driver from Family Guy

Processed Image of Dotted Lines, Image from a Medium Article

Hough Transform Demonstrated

Advancing to Deep Learning: Convolutional Neural Networks & Residual Neural Networks

“No one tells a child how to see, especially in the early years. They learn this through real-world experiences and examples.”

— Fei Fei Li

The phrase “Machine Learning” first came up from Arthur Samuel in 1952. However, research in neural networks was heavily discouraged until the 2000s. The earliest version of an artificial neuron was invented by neurophysiologist Warren McCulloch and mathematician Walter Pitts in 1943. It was meant to describe how neurons work inside our brains.

Deep Convolutional Neural Networks

Whenever convolutional neural networks were mentioned, the first who came to mind of a data scientist is probably Yann LeCun, who published his paper of LeNet-5 in 1998.

The Architecture of LeNet-5, Image from Original Paper

Convolutional Layer (3×3)

Pooling Layer (2×2 — Max Pooling)

ReLU Layer

Fully Connected Layer is Just an Artificial Neural Network

Deep Residual Neural Networks

In 2015, Kaiming He and his team at Microsoft Research published their paper Deep Residual Learning for Image Recognition. The paper applied residual learning to convolutional neural networks and achieved better performance than all other State-of-the-Art (SOTA) models at that time.

Example of a Residual Connection, Image from Original Paper

Comparison Between ResNet and other Models, Image from Original Paper

Computer Vision Today: Semantic Segmentation, Object Detection, and Image Classification

“Big brother is watching you!”

— 1984, George Orwell

Along with the advancements in computer vision, artificial intelligence nowadays can do many crazy things. Out of concerns for privacy invasion, the European Union is even considering banning facial recognition. While other countries such as China, already embedded facial recognition into their social credit system.

Image Classification Workflow

Semantic Segmentation: CASENet and Mask R-CNN

Semantic Segmentation is highly related to the detection of boundaries and edges. CASENet, for example, outlines the boundaries of the objects.

Semantic Segmentation with CASENet, Demo from Youtube

Semantic Segmentation with Mask R-CNN, Demo from Youtube

Object Detection: YOLOv1, YOLOv2, and YOLOv3

“You only look once!”

— Joseph Redmon

Published by Joseph Redmon in 2016, YOLOv1 already demonstrated much faster speed compared to other models at that time in object detection. The author incrementally improved the model into YOLOv2 and YOLOv3 with even better performance.

Comparison Between YOLOv2, YOLOv3, Mask R-CNN, and Deeplab Xception, Demo from Youtube

Image Classification: ImageNet State-of-the-Art and Epic Failures of CNN

ImageNet invites researchers around the world to compete on their data set, the state-of-the-art models are still rapidly iterating. Maybe you will define the next SOTA?

Image Classification Accuracy Visualization from Paper with Code, 2020

Neural Networks Makes Judgements Based on Textures, Image from Original Paper

VGG-16 Thinks a Squirrel with Ripples is a Gold Fish, Image from Original Paper

Words in the end…

“AI is everywhere. It’s not that big, scary thing in the future. AI is here with us.”

— Fei Fei Li

I have finally finished this article as planned. There are way too many to write about in the field and you can never fit them all inside a 10-minute article. I hope this article has raised your interest in some of them. I might write about gesture recognition and social behavior prediction in my future articles.

Original post: https://towardsdatascience.com/understanding-cv-how-ai-sees-our-world-a977b90bf612

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