Machine Vision: Insights into the World of Computer Vision

By Fouad Sabry

What is Machine Vision

The technology and methods that are used to provide imaging-based automatic inspection and analysis for applications such as automatic inspection, process control, and robot guiding, typically in industry, are referred to as machine vision. The term "machine vision" encompasses a wide range of technologies, including software and hardware items, integrated systems, activities, procedures, and skilled professionals. Unlike computer vision, which is a subfield of computer science, machine vision is a field of systems engineering that might be considered to be different from computer vision. It seeks to combine existing technologies in novel ways and apply them to the solution of problems that are encountered in the real world. This word is the one that is most commonly used for these functions in situations that involve industrial automation; nevertheless, it is also used for these functions in other environments, such as vehicle guiding.

How you will benefit

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

Chapter 1: Machine vision

Chapter 2: Computer vision

Chapter 3: Thermography

Chapter 4: Smart camera

Chapter 5: 3D scanning

Chapter 6: Mobile mapping

Chapter 7: Visual servoing

Chapter 8: Visual odometry

Chapter 9: Vision-guided robot systems

Chapter 10: Optical sorting

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

(III) Real world examples for the usage of machine 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 Machine Vision.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: May 5, 2024

Book preview

Chapter 1: Machine vision

Machine vision (MV) refers to both the technology and the methodologies that are used to deliver imaging-based automated inspection and analysis for applications such as automatic inspection, process control, and robot guiding, which are often utilized in industrial settings. Machine vision is an umbrella term that encompasses a wide variety of technologies, software and hardware products, integrated systems, activities, approaches, and expertise. Computer vision, which is a subfield of computer science, and machine vision, which is a systems engineering subject, may be differentiated from one another. It makes an effort to combine already existing technology in novel ways and use them in the process of finding solutions to issues that occur in the real world. This is the name that is most often used for these activities in situations involving industrial automation; nevertheless, it is also used for these functions in other environments, including those involving vehicle guidance.

The process of machine vision as a whole begins with designing the specifics of the needs and the project, and it is followed by the development of a solution. Imaging is the first step in the process, which is followed by the automatic examination of the picture and the collection of any necessary information during run time.

The word machine vision may have a number of different meanings, but they all refer to the technology and procedures that are used to automatically extract information from a picture. This is in contrast to image processing, which results in the creation of an entirely new image. The information that is retrieved might be as simple as a good-part/bad-part signal, or it can be a more sophisticated collection of data such as the identification, location, and orientation of each individual item included inside a picture. The data may be put to use in a variety of applications, including autonomous inspection, robot and process guiding in industry, security monitoring, and vehicle guidance. See the machine vision glossary for further information.

Imaging-based automated inspection and sorting as well as robot navigation are the key applications for machine vision; This section provides a description of the technological procedure that takes place when the solution is in operation.

The capture of a picture is the first stage in the chain of operations that make up automated inspection. This phase often involves the use of cameras, lenses, and illumination that have been engineered to offer the difference that is needed for future processing.

Lighting is one of the components that is often included in an automated inspection system, a photographic or other imaging device, a processor, software, and output devices.: 11–13

It is possible for the imaging device (such as a camera) to be independent from the primary image processing unit or to be coupled with it. If the latter option is chosen, the resulting device is often referred to as a smart camera or smart sensor.

The processing of a picture comes after it has been acquired. In most cases, the final product is the culmination of many phases of processing that were performed in the correct order. In a typical process, the first step is the modification of the image using tools such as filters, then comes the removal of objects, then comes the removal (using methods such as measurements or the reading of codes) of data from those objects, and finally comes the communication of that data or the comparison of that data to target values, which results in the creation and communication of pass/fail results. Among the many image processing technologies available is machine vision; Combining neighboring 2D or 3D pictures via a process known as stitching or registration.

Filtering (e.g. morphological filtering)

The first stage in thresholding is to establish or determine a gray value that will be helpful for the subsequent phases in the process. Thresholding After that, the value is used to partition the picture into sections, and occasionally it is also employed to convert each section of the image to simple black and white depending on whether or not it is below or above that grayscale value.

Pixel counting is the process of tallying the number of pixels that are either luminous or dark.

The process of simplifying and/or transforming the representation of an image into something that is more understandable and simpler to analyze is referred to as segmentation. Segmentation is the partitioning of a digital picture into several segments.

Finding an object's edges is what edge detection refers to.

Color Analysis: Identifying components, goods, and things based on their colors; judging the quality of products based on their colors; isolating characteristics using colors.

Examining a picture in search of discrete blobs of linked pixels (such a black hole in a gray object, for example) as image landmarks is referred to as blob detection and extraction.

Processing based on neural networks, deep learning, and machine learning: weighted and self-training multi-variable decision making

The identification of patterns, especially the matching of templates. Locating, matching, and/or counting certain patterns is required. This may include the position of an item that is capable of being rotated, partly covered by another object, or fluctuating in size.

Reading of barcodes, data matrices, and so-called 2D barcodes

OCR stands for optical character recognition and refers to the process of automating the reading of text such as serial numbers.

Measuring the dimensions of an item is an important part of gauging and metrology (e.g. in pixels, inches or millimeters)

Comparison of actual values to those specified in order to arrive at a pass or fail or go/no go conclusion. For instance, while verifying information using a code or bar code, the value that is read is compared to the goal value that has been saved. In the process of gauging, a measurement will be checked against the standard value and any applicable tolerances. When checking alphanumeric codes, the value that was generated by OCR is compared to the correct value, also known as the target value. When doing a check for defects, it is possible to compare the measurable size of the imperfections to the maximums that are permitted by quality standards.

Automatic inspection systems often provide pass/fail determinations as one of their outputs.

It is typical practice for machine vision to provide a robot with information on its location and orientation, so enabling the robot to correctly grasp the product. This capacity is also used to direct motion that is less complicated than robots, such as a motion controller with just one or two axes.

As late as 2006, a consultant to the industry estimated that the MV sector accounted for a market size of $1.5 billion in North America.

{End Chapter 1}

Chapter 2: 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