Getting Started with Computer Vision: The Basics and Beginner Projects

Computer vision is a rapidly growing field of artificial intelligence that focuses on enabling computers to interpret and understand visual information from the world around us. Just as humans use our eyes and brains to make sense of what we see, computer vision aims to give machines that same capability.

From self-driving cars that can navigate roads to facial recognition systems that unlock our phones, computer vision powers many of the cutting-edge AI applications emerging today. And its potential use cases are vast—computer vision can be applied to everything from healthcare to agriculture to retail and beyond.

As such an impactful technology, computer vision is an exciting area for aspiring AI practitioners and enthusiasts to learn. In this guide, we‘ll break down the basics of computer vision and share some beginner-friendly projects to help you start building your own computer vision applications. By the end, you‘ll have a solid foundation to keep exploring this fascinating field.

How Computer Vision Works

At a high level, computer vision systems work by acquiring, processing, analyzing, and understanding digital images or video frames to produce numerical or symbolic information that can be acted upon.

The typical computer vision pipeline involves the following key steps:

1. Image acquisition – Obtaining an image from a camera, scanner, or other digital source
2. Preprocessing – Enhancing the image and handling issues like noise, distortion, or color
3. Feature extraction – Identifying and extracting relevant features and patterns in the image
4. Detection/segmentation – Localizing and labeling specific regions or objects of interest
5. Higher-level processing – Analyzing the extracted information to recognize objects, interpret scenes, make predictions, etc.

Stages 1-3 deal largely with traditional image processing techniques, while stages 4-5 rely more heavily on machine learning algorithms to detect patterns and make intelligent decisions about the visual input.

The goal is to transform raw pixel values into a higher-level understanding of the image‘s contents, mimicking the way human vision works. While this is a highly complex task, advances in deep learning and neural networks have accelerated the field of computer vision in recent years.

Key Concepts and Techniques

To make sense of how computer vision works in practice, it‘s helpful to know some of the core concepts and techniques used:

• Image processing fundamentals – Things like color spaces, filters, transformations, histograms, and morphological operators for modifying and enhancing images
• Feature detection and description – Finding and extracting distinctive features like edges, corners, blobs, or patterns to help analyze and match images
• Image segmentation – Dividing an image into multiple segments or regions to locate objects or boundaries
• Object detection and recognition – Identifying and localizing specific objects as well as categorizing them into predefined classes
• Facial detection and recognition – Finding human faces and matching them to identify individuals
• Optical character recognition – Detecting and extracting text from images to convert it into machine-readable formats
• Motion and tracking – Following the movements of objects or people between video frames
• 3D vision and depth estimation – Reconstructing the 3D structure of a scene from 2D views or estimating the distance of objects

Behind these techniques there are many different algorithms and approaches used. Classic methods include things like edge detection filters, color-based segmentation, feature descriptors like SIFT or HOG, and cascading classifiers for object detection.

In the deep learning era, convolutional neural networks (CNNs) have become the dominant approach for many computer vision tasks. CNNs automatically learn hierarchical features from images, obviating the need for handcrafted feature extractors. Models like R-CNN, YOLO, and SSD are commonly used for object detection, while architectures like ResNet, Inception, and MobileNet provide strong backbones for image classification.

Tools of the Trade

To implement computer vision techniques, most practitioners rely on open source libraries that provide optimized algorithms and helpful abstractions. The most popular computer vision libraries include:

• OpenCV – Extensive library of computer vision and machine learning algorithms with bindings for multiple languages
• SimpleCV – Simplified interface for common computer vision tasks built on top of OpenCV
• Scikit-image – Collection of algorithms for image processing in Python
• MATLAB Computer Vision Toolbox – Proprietary computer vision library for MATLAB
• Keras/TensorFlow – Deep learning libraries that can be used to train and deploy computer vision models
• PyTorch – Deep learning library with strong computer vision capabilities and eager execution
• ImageAI – Python library that simplifies building computer vision applications using pre-trained deep learning models

These libraries provide a wealth of functionality to help you load, process, transform, and extract insights from image and video data. Most also come with detailed documentation and example code to help you get up and running quickly.

In addition to the above libraries, there are also more specialized tools for tasks like labeling image datasets (e.g. LabelImg, VGG Image Annotator), developing real-time computer vision applications (e.g. OpenMV, DepthAI), and working with 3D data (e.g. Open3D, PyTorch3D).

Applications of Computer Vision

The applications of computer vision span nearly every industry. Some key areas where computer vision is driving innovation include:

• Automotive – Autonomous driving, driver monitoring, traffic sign recognition
• Healthcare – Disease diagnosis, surgical robotics, early detection of health issues
• Agriculture – Crop health monitoring, livestock tracking, yield optimization
• Retail – Cashierless checkout, inventory management, foot traffic analysis
• Manufacturing – Defect inspection, process control, predictive maintenance
• Security – Surveillance, intruder detection, access control
• Robotics – Object grasping and manipulation, navigation, human-robot interaction

As cameras become ubiquitous and computing power continues to grow, opportunities abound for computer vision to transform the way we live and work. As a practitioner, finding ways to apply computer vision to real-world problems that matter to you is a great way to grow your skills and make an impact.

Beginner Projects to Try

Alright, now that you know the basics, it‘s time to get your hands dirty with some code! Here are a few beginner-friendly computer vision projects to help cement your knowledge:

1. OpenCV tutorial – Work through the official OpenCV Python tutorial to learn the basics of image processing, thresholding, edge detection, and more.

2. Selfie segmentation – Build a Python app using OpenCV and MediaPipe to segment a person from their background in real-time video. Follow this Google Codelab for a step-by-step walkthrough.

3. Emoji reader – Use Apple‘s Turicreate library to build an emoji reader that can classify hand-drawn emojis using transfer learning. Check out this fun tutorial from Fritz AI to get started.

4. Facial keypoint detection – Combine OpenCV with a deep learning approach to detect and track key facial landmarks like eyes, nose, and mouth. Experiment with Dlib, OpenFace, or MediaPipe for pre-trained facial landmark models.

5. License plate reader – Create an automated license plate reader using the EasyOCR library in Python. This tutorial will get you up to speed on the character recognition task.

6. Social distancing detector – Build a computer vision system to detect whether people are maintaining a safe physical distance from each other. Learn how in this informative guide using OpenCV, YOLO, and CUDA.

7. Image colorization – Use OpenCV and deep learning to automatically colorize black-and-white photos. Follow the steps in this tutorial to bring historical images to life.

8. Teaching sign language to a neural network – Collect your own dataset and train a sign language recognition model using Keras. This creative project will give you hands-on experience with the entire computer vision pipeline.

When learning computer vision, seeing your code in action is highly motivating. Don‘t be afraid to experiment, iterate, and learn from your mistakes! As you tackle these projects, take time to understand what‘s happening under the hood and how the different pieces fit together.

Leveling Up Your Skills

Like any technical field, computer vision mastery comes through continuous practice and learning. Once you have the basics down, here are some tips to keep growing your skills:

• Learn by doing – The best way to learn computer vision is through hands-on projects. As you gain experience, take on increasingly complex projects that push you out of your comfort zone.
• Read research papers – Stay on top of the latest techniques coming out of academia. Websites like arXiv and Papers With Code are great resources for discovering state-of-the-art computer vision research.
• Compete in online challenges – Put your skills to the test by competing in computer vision challenges on platforms like Kaggle, AIcrowd, or CodaLab. You‘ll get to apply your knowledge to real-world datasets and see how your approach stacks up.
• Contribute to open source – Give back to the vibrant ecosystem of open source computer vision projects. Look for ways to contribute code, documentation, bug reports, or tutorials to libraries you use and admire.
• Learn adjacent skills – Broaden your horizons by learning adjacent skills like deep learning, data engineering, cloud computing, IoT, or UI/UX design. These complementary skills will make you a more well-rounded and effective computer vision practitioner.
• Join a community – Tap into the collective knowledge of the computer vision community. Participate in online forums, attend meetups and conferences, and collaborate with other practitioners to accelerate your learning and expand your network.

Computer vision is a powerful technology that will only become more pervasive in the years ahead. With the right skills and mindset, you can be at the forefront of this exciting field—building intelligent systems that can quite literally change how we see the world. So what are you waiting for? Grab your camera and start coding!