Basic Guide to Computer Vision for Beginners in AI

Artificial Intelligence (AI) is one of the most exciting and rapidly evolving areas in modern technology, transforming virtually every aspect of our lives. One of its most fascinating subfields is Computer Vision, a field that allows machines to “see” and interpret the world around them. This article is an introductory guide designed for beginners who wish to understand the basics of Computer Vision and its practical applications.

What is computer vision?

The history of Computer Vision dates back to the 1960s, but it has been in the last few decades that we have seen significant advancements, driven by the availability of large amounts of data and increased computational power.

Computer vision is a process of modeling and replicating human vision through software and hardware, whose main goal is to accurately recognize and describe images. These images can be static or moving and can contain objects, people, animals, scenes, texts, gestures, expressions, etc.

To perform this task, computer vision uses complex algorithms, traditional or based on deep learning, which allow computers to process and understand pixels, the tiny dots that form digital images. Thus, computers can identify patterns, shapes, colors, contours, textures, and other visual features, and associate them with concepts and categories.

Computer vision is a multidisciplinary field, involving knowledge from mathematics, statistics, computer science, engineering, physics, biology, psychology, and other areas. It also relates to other areas of AI, such as natural language processing, machine learning, robotics, and augmented reality.

What are the main tasks and techniques of computer vision?

Computer vision can perform various tasks and techniques, depending on the goal and the type of image or video to be analyzed. Some of the most common are:

Object detection: consists of locating and identifying objects in an image or video, usually through bounding boxes that indicate the position and size of the object. For example, an object detection system can recognize cars, pedestrians, traffic lights, signs, etc., in a traffic scene.
Facial recognition: involves identifying and verifying a person’s identity from an image or video of their face, comparing it with a pre-existing database. For example, a facial recognition system can unlock a cell phone, authorize a payment, or grant entry, using the user’s face as a password.
Image segmentation: involves dividing an image into regions or pixels that share some property, such as color, intensity, texture, or semantics. For example, an image segmentation system can separate the foreground from the background or identify parts of the human body in an image.
Motion tracking: involves estimating the trajectory of an object or a point of interest in a sequence of images or videos, usually through markers or characteristic points. For example, a motion tracking system can follow the position and orientation of a car, a ball, or a finger in a video.
3D reconstruction: involves generating a three-dimensional representation of an object or scene from one or more two-dimensional images or videos, using techniques of geometry, projection, and stereo. For example, a 3D reconstruction system can create a 3D model of a face, building, or landscape from photos taken at different angles.

What are some tools and libraries for computer vision?

For developing computer vision projects, there are several tools and libraries that can facilitate the work and offer ready-made or customizable resources. Some of the most popular include:

Ope n CV: is an open-source software library that provides over 2,500 computer vision and machine learning algorithms for various applications, such as object and face detection and recognition, object segmentation and tracking, 3D reconstruction and calibration, motion analysis, and image stabilization, among others. It is written in C++, but has interfaces for other languages, like Python, Java, and MATLAB.
TensorFlow: is an open-source platform that allows creating and training machine learning and deep learning models, using artificial neural networks. It is widely used for computer vision, as it offers specific tools and libraries, such as the TensorFlow Object Detection API, TensorFlow Lite, and TensorFlow.js, which facilitate the development and deployment of image and video detection, recognition, and classification systems.
PyTorch: is an open-source library that also allows creating and training machine learning and deep learning models, using artificial neural networks. It is based on the Python language, but can also be used with C++ and CUDA. It is widely used for computer vision, as it offers specific tools and libraries, such as PyTorch Vision, PyTorch Mobile, and PyTorch Hub, which facilitate the development and deployment of image and video segmentation, reconstruction, and generation systems.
Matlab: is a proprietary software platform that allows performing numerical calculations, data analysis, visualizations, and programming, using a matrix-based language. It is widely used for computer vision, as it offers specific tools and libraries, such as the Image Processing Toolbox, Computer Vision Toolbox, and Deep Learning Toolbox, which facilitate the development and deployment of image and video processing, analysis, and synthesis systems.

These tools are essential for implementing Computer Vision techniques and have been used in a wide range of practical applications, from facial recognition to medical image analysis.

Real-World Applications of Computer Vision

Computer Vision has applications in various sectors. In healthcare, it is used for medical image analysis, assisting in the detection and diagnosis of diseases. In retail, it is applied in automated checkout systems and inventory monitoring. In the automotive industry, it is essential for the development of autonomous cars. Additionally, it plays a significant role in security and surveillance systems, offering automated monitoring and behavior analysis.

Challenges and Limitations of Computer Vision

Despite significant advancements, Computer Vision still faces challenges. Some of them include:

Difficulties with Variations in Lighting and Perspective: Changes in light and viewing angle can affect the accuracy of object detection and recognition.
Need for Large Data Sets: To train accurate models, access to large amounts of annotated data is required, which can be a challenge.
Privacy and Ethical Concerns: The use of technologies like facial recognition raises significant concerns about privacy and consent.

Learning Computer Vision

For beginners interested in learning Computer Vision, there are several resources available:

Online Courses: Platforms like Coursera, edX and Udemy offer specific courses on Computer Vision and AI.
Tutorials and Documentation: Websites like GitHub and Stack Overflow are excellent for finding practical projects and solving specific queries.
Communities and Forums: Participating in communities like Reddit and groups on LinkedIn can be a great way to stay updated and network.

The Future of Computer Vision

The future of Computer Vision is promising, with emerging applications in areas such as augmented reality, autonomous vehicles, and robotics. As technology continues to evolve, Computer Vision capabilities are expected to become even more advanced and integrated into our daily lives.

Conclusion

Computer vision is a fascinating and challenging field that seeks to give computers the ability to see and understand the visual world, just like humans. It can be applied in various domains and problems, such as security, health, education, entertainment, and more. To develop computer vision projects, there are several tools and libraries that can help you, like OpenCV, TensorFlow, PyTorch, and Matlab. We hope this article has helped you better understand what computer vision is and how it works.