Computer Vision: Exploring the Depths of Computer Vision

By Fouad Sabry

What is Computer Vision

Computer vision tasks include methods for acquiring, processing, analyzing, and comprehending digital images, as well as the extraction of high-dimensional data from the actual world in order to provide numerical or symbolic information, such as, for example, in the form of judgments. In the context of this discussion, understanding refers to the process of transforming visual pictures into descriptions of the environment that are comprehensible to thinking processes and have the ability to evoke appropriate action. It is possible to interpret this picture understanding as the process of extracting symbolic information from image data by making use of models that have been created with the assistance of learning theory, geometry, physics, and computer science.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Computer vision

Chapter 2: Machine vision

Chapter 3: Image analysis

Chapter 4: Image segmentation

Chapter 5: Optical flow

Chapter 6: Motion detection

Chapter 7: Gesture recognition

Chapter 8: Pose (computer vision)

Chapter 9: Rita Cucchiara

Chapter 10: Stereo cameras

(II) Answering the public top questions about computer vision.

(III) Real world examples for the usage of computer vision in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Computer Vision.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Apr 27, 2024

Book preview

Chapter 1: Computer vision

The study of how computers can derive high-level knowledge from digital pictures or videos is the focus of the multidisciplinary scientific area of computer vision. From a technological point of view, it investigates and attempts to automate activities that are within the capabilities of the human visual system.

Tasks associated with computer vision include techniques for obtaining, processing, analyzing, and comprehending digital pictures, as well as the extraction of high-dimensional data from the physical environment in order to create numeric or symbolic information, such as judgments.

Computer vision is a subfield of computer science that investigates the theoretical underpinnings of artificial systems designed to derive information from pictures. The visual data may be presented in a variety of formats, including video sequences, images obtained from several cameras, multi-dimensional data obtained from a 3D scanner or medical scanning equipment, and so on. The goal of the technical field known as computer vision is to implement the ideas and models it has developed in the process of building computer vision systems.

The fields of scene reconstruction, object detection, event detection, video tracking, object recognition, 3D pose estimation, learning, indexing, motion estimation, visual servoing, 3D scene modeling, and image restoration are all sub-domains of computer vision. Other sub-domains of computer vision include 3D scene modeling.

Computer vision is a multidisciplinary study that examines how computers can be programmed to extract high-level knowledge from digital pictures or movies. This area focuses on how computers can be taught to comprehend what is being shown to them. From the point of view of engineering, the goal is to find ways to automate operations that can already be done by the human visual system. Computer vision is a field of study in the field of information technology that focuses on applying existing theories and models to the process of building computer vision systems.

In the late 1960s, colleges that were on the cutting edge of artificial intelligence were the first to experiment with computer vision. Its purpose was to function in a manner similar to that of the human visual system, with the ultimate goal of imbuing robots with intelligent behavior. In the 1990s, several of the study areas that had been studied before became more active than the others. The study of projective three-dimensional reconstructions led to a deeper understanding of how to calibrate a camera. It became clear, with the introduction of optimization techniques for camera calibration, that a significant number of the concepts had previously been investigated by the discipline of photogrammetry's bundle adjustment theory. This came to light as a result of this development. This resulted in the development of techniques for doing sparse three-dimensional reconstructions of scenes using several photographs. Both the dense stereo correspondence issue and the development of further multi-view stereo approaches saw some degree of forward movement. Concurrently, many variants of graph cut were used in order to address the picture segmentation problem. This decade was especially significant since it was the first time statistical learning methods were used in practice to the task of recognizing faces in photographs (see Eigenface). The areas of computer graphics and computer vision have become more intertwined in recent years, which has led to a large rise in the amount of collaboration that has taken place between the two. This featured early forms of light-field rendering, panoramic picture stitching, image morphing, view interpolation, and image-based rendering. The area of computer vision has been given a new lease of life thanks to the development of algorithms based on deep learning. The accuracy of deep learning algorithms on numerous benchmark computer vision data sets for tasks ranging from classification to optical flow has exceeded that of earlier approaches. These tasks include segmentation of images and optical flow.

Solid-state Computer vision is strongly connected to a number of other disciplines, including physics. The vast majority of computer vision systems are based on image sensors, which are devices that are able to detect electromagnetic radiation. This radiation is commonly manifested as either visible or infrared light. Quantum physics was used in the development of the sensors. The scientific discipline of physics provides an explanation for the method through which light interacts with surfaces. The behavior of optics, which is a fundamental component of the majority of imaging systems, may be explained by physics. In order to offer a comprehensive knowledge of the process by which a picture is formed, sophisticated image sensors need the use of quantum mechanics. Computer vision may also be used to solve a variety of measurement issues that arise in physics, such as those involving the motion of fluids.

The scientific discipline of neurobiology, more especially the investigation of the biological visual system. The eyes, the neurons, and the brain regions that are dedicated to the processing of visual inputs in both humans and diverse animals have been the subject of a substantial amount of research over the course of the last century. As a consequence of this, a simple but intricate description of the way actual vision systems function in order to complete various vision-related tasks has emerged. As a consequence of these findings, a subfield of computer vision has emerged in which artificial systems are meant to imitate the processing and behavior of biological systems, with varying degrees of biological resemblance. A lot of the learning-based approaches that have been created within computer vision have their roots in biology. Some examples of these learning-based methods include neural net and deep learning based image and feature analysis and categorization.

Some branches of computer vision research are very similar to the study of biological vision. Many branches of AI research are also very similar to the study of human consciousness and the application of previously acquired knowledge to interpret, integrate, and utilise visual input. The study and modeling of the physiological mechanisms that underlie visual perception in humans and other animals is the purview of the academic discipline of biological vision. On the other side, computer vision is the study of and description of the processes that are implemented in software and hardware that are underlying artificial vision