With advances in camera quality, image fidelity, and neural network research focused on solving image- and video-based challenges, computer vision continues to capture the attention and imaginations of machine learning researchers and practitioners.
But computer vision is an incredibly broad umbrella term that encompasses an array of specific tasks and challenges, and the field continues to expand.
With that in mind, we’ve selected some of our favorite Heartbeat posts that cover a range of topics within computer vision and separated them into two categories:
- Computer Vision Foundations
- Use Cases
Computer Vision Foundations
The Ancient Secrets of Computer Vision (condensed, by Joseph Redmon)
Condensed summaries and discussion of Joseph Redmon’s sprawling examination of all things computer vision.
— by Angus Addlesee
The 5 Computer Vision Techniques That Will Change How You See The World
In-depth overviews of common computer vision techniques: Image classification, object detection, object tracking, semantic segmentation, and instance segmentation.
— by James Le
The 5 Trends Dominating Computer Vision
Explore this detailed look at the trends in computer vision that have defined the last couple of years and will continue to make waves moving forward.
— by James Le
Artificial Art: How GANs are making machines creative
Exploring how advances in neural networks have created a new wave of creative machines capable of producing art, literature, and music — not exclusively CV, but an interesting look at generative computer vision within.
— by Machine’s Creativity
Convolutional Neural Networks: An Intro Tutorial
An overview of convolutional neural networks—the foundational building block of deep learning-based computer vision—including how and why they work and an implementation with Keras and TensorFlow.
— by Derrick Mwiti
Use Cases: Image Classification
Basics of Image Classification with PyTorch
Learn how to build a complete image classification pipeline with PyTorch — from scratch.
— by John Olafenwa
Scannable Chess Scoresheets with a Convolutional Neural Network and OpenCV
In this tutorial, you’ll learn how to build a ready-to-ship machine learning model that can automatically scan chess scoresheets.
— by Rithwik Sudharsan
Training an Image Classification Convolutional Neural Net to Detect Plant Disease Using fast.ai
Learn how to combine the powers of image classification and fast.ai’s training library to detect plant disease with a neural network.
— by Steve Mutuvi
Building a Vision-Controlled Car Using Raspberry Pi — From Scratch
Build a car robot from scratch and control it using Raspberry Pi based on images captured and by a USB camera and then classified.
— by Ahmed Gad
Using TensorFlow.js to Train a “Rock-Paper-Scissors” Model
Train an image classification machine learning model in your browser in ~10 minutes.
— by Gant Laborde
Classification with TensorFlow and Dense Neural Networks
Learn how to perform classification using TensorFlow and its dense neural network classifier module.
Binary Classification using Keras in R
RStudio comes equipped with an interface for using Keras to build machine learning models in R. Learn how to use this interface to create a binary classification model.
— by Derrick Mwiti
Use Cases: Object Detection
Detecting objects in videos and camera feeds using Keras, OpenCV, and ImageAI
Learn how to detect objects in single video frames from camera feeds with Keras, OpenCV, and ImageAI.
— by Moses Olafenwa
Real-Time Object Detection on Raspberry Pi Using OpenCV DNN
Learn how to use OpenCV’s Deep Neural Network module (DNN) to detect objects in real time on a Raspberry Pi.
Gentle guide on how YOLO Object Localization works with Keras
Learn how to build and implement a YOLO object localization model with Keras.
— by Chengwei Zhang
Real-Time Person Tracking on the Edge with a Raspberry Pi
A look at model architectures and optimizations for real-time detection and tracking of people on the edge.
— by Priya Dwivedi
Object detection in just 3 lines of R code using Tiny YOLO
Using R and a Tiny YOLO model, learn how you can detect objects in just 3 lines of code.
— by AbdulMajedRaja RS
Use Cases: Generative Adversarial Networks (GANs)
Introduction to Generative Adversarial Networks (GANs): Types, and Applications, and Implementation
This tutorial will introduce Generative Adversarial Networks (GANs), explore the different variations, their applications, and help you learn to build your own simple GAN using Keras.
— by Derrick Mwiti
Animated StyleGAN image transitions with RunwayML
Create an endless loop of synthetically-generated StyleGAN landscapes with smooth transitions using RunwayML and P5.js.
— by Mike Heavers
My MangaGAN: Building My First Generative Adversarial Network
Build a generative adversarial network to make your very own anime characters with Keras.
— by Nikita Sharma
Use Cases: Style Transfer
Neural Style Transfer with PyTorch
Neural style transfer is an exciting technology that generates images in the style of another image. Learn how this works, along with a simple implementation in PyTorch.
— by Derrick Mwiti
Art & Soul— A Style Transfer Website based on Tkinter and Django
Parts one and two of a complete walkthrough for building your own style transfer website using VGG16 and Django.
— by Nikita Sharma
Part 1: Building a style transfer model:
Part 2: Implementing the model in a website built with Tkinter and Django:
Use Cases: Pose Estimation
“Just Point It”: Machine Learning on iOS with Pose Estimation + OCR Using Core ML and ML Kit
A look a “Just Point It”, an iOS app that leverages machine learning to allow users to point at text on a paper document and look up the word that’s being pointed at.
— by Doyoung Gwak
One Last Cool Computer Vision Thing
Real-Time 2D/3D Feature Point Extraction from a Mobile Camera
Explore the intersection of machine learning and augmented reality, and learn how to extract 2D and 3D feature points from a smartphone camera.
— by Hart Woolery
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