#VisionAutomation and RoboticSolution

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Learning Approaches

Machine Learning, an exciting branch of Artificial Intelligence, is all around us in this modern world. Machine Learning brings out the power of data in a new way, like Facebook suggesting the stories in your feed and Amazon recommending your products. Machine Learning enables computer systems to learn and improve from experience continuously as it is primarily developed with the ability to access data and perform tasks automatically through predictions and detections.

Machine Learning is one of the core sub-areas of Artificial Intelligence. ML applications learn from experience and data like humans without explicit programming for the same. When exposed to newer and newer data, these algorithms learn, evolve, transform, and develop all by themselves. In other words, with Machine Learning, computers find insightful information without the need to tell them where to find it. Instead, they achieve this by leveraging algorithms that learn from continuous data in a process that is iterative.

Related Article: ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

There are only a few main learning styles or learning models that a machine learning algorithm can have:

1. SUPERVISED LEARNING

In supervised learning, input data is known as the training data. It has a known label or result such as spam/not-spam or a stock price at a time. A model is developed through training in which it is required to make predictions and is corrected when those predictions are inaccurate. The training process is reiterated until the model achieves the desired level of accuracy on the training data.

Example problems are classification and regression. Logistic Regression and the Back Propagation Neural Network are some popular examples of supervised learning algorithms.  

2. UNSUPERVISED LEARNING

In unsupervised learning, input data is not labeled and does not have a known result. A model is developed by deducing structures evident in the input data. This is generally done to extract general rules and may be carried out through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

Example problems in unsupervised learning are clustering, dimensionality reduction, and association rule learning. The Apriori algorithm and K-Means algorithms are some of the popular unsupervised learning algorithms.

3. CLUSTERING

Clustering algorithms are used to describe the class of problem and the class of methods. Clustering methods are usually organized by the modeling approaches namely centroid-based and hierarchal. All methods are concerned with using the evident patterns in the data to best organize the data into groups of maximum similarity.

The most popular clustering algorithms are:

k-Means

k-Medians

Expectation Maximisation (EM)

Hierarchical Clustering

Also Read: Differences Between Machine Learning and Rule-Based Systems

4. ASSOCIATION

Association rule learning algorithms are used to extract rules that best explain observed relationships between variables in a dataset. These rules can prove to be useful in discovering important and commercially useful associations in large multi-dimensional datasets that can be exploited by an organization to increase profitability.

The most popular association rule learning algorithms are:

Apriori algorithm

Eclat algorithm

5. SEMI-SUPERVISED LEARNING

In semi-supervised learning, input data is a mixture of labeled and unlabelled features. There is a desired prediction problem. However, the model must learn the structures to organize the data as well as make predictions accurately.

Example problems include classification and regression. Most semi-supervised algorithms are extensions to other flexible methods that make assumptions about how to model the unlabelled data.

6. GAN (GENERATIVE ADVERSARIAL NETWORKS)

The focus for GAN (Generative Adversarial Networks) is to generate data from scratch, mostly images but other domains including music have been done. However, the scope of application is far bigger than this. In reinforcement learning, it helps a robot to learn much faster.

Also Read: MACHINE VISION PROCESS FLOW

7. REINFORCEMENT LEARNING

Reinforcement learning is a specialized subfield of machine learning, which is known as approximate dynamic programming or neuro-dynamic programming. Learning concerned with agents who take some sort of action in an environment to maximize the cumulative reward. In reinforcement learning the environment is represented in Markov Decision Process (MDP). The learning algorithm tries to target the large MDPs where the model is infeasible by not assuming predetermined knowledge of a mathematical model.

Reinforcement learning can be understood with a simple example of a child in a living room. A child sees a fireplace and tries to approach that fire. It is warm, it is positive, the child feels good (positive reward +1). The child understands that fire is a positive thing. However, when the child tries to touch that fire, it burns the child’s hand (negative reward -1). The child just understood that fire is a good thing at a sufficient distance, but not too close. That is how reinforcement learning learns from some set of actions.

Read More: https://bit.ly/33Ucm3Q

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Man-made reasoning is set to upset the Machine Vision industry. Here are Qualitas, we've set out on this excursion almost 10 years back. With our skill in sending admirably over a 100 modern machine vision arrangements the whole way across the globe, we comprehend mechanical computerization. We convey massive incentive to use the intensity of Machine Vision and AI for our clients. Our clients additionally esteem key bits of knowledge that can be gotten from these frameworks so they can improve their assembling measures, while giving them unlimited oversight over framework support and execution simultaneously Vision Automation and Robotic Solution

[machinevision]

Machine Learning, an exciting branch of Artificial Intelligence, is all around us in this modern world. Machine Learning brings out the power of data in a new way, like Facebook suggesting the stories in your feed and Amazon recommending your products. Machine Learning enables computer systems to learn and improve from experience continuously as it is primarily developed with the ability to access data and perform tasks automatically through predictions and detections.

Read More: https://bit.ly/33Ucm3Q

[machinevision]

Machine Vision – Augment not replace Humans

What is Machine Vision (MV) ?

Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision is a term encompassing a large number of technologies, software and hardware products, integrated systems, actions, methods, and expertise.

Example 1

Piston Ring Counting – This machine is used to count piston rings and can count different models of rings ranging from a minimum thickness of 0.25mm.

(Picture credits – Qualitas Technologies)

Problems that are most likely to occur – To pack the stack of rings, the counting of the piston rings has to be done. And it is a tedious and time-consuming process. Also, the accuracy for lesser thickness can go down due to human errors.

Example 2

Gear teeth counting machine – This machine is used to count the number of teeth available on the machine gears and classify the gears based on the number

(Picture credits – Qualitas Technologies)

Problems that are most likely to occur – Counting the teeth of gears is highly essential because of it’s vital role in generating the required torque, but the diameter of the gears and patterns of the teeth varied over a wide range based on shape, teeth height, thickness etc and counting it is a challenging task.

Why Machine Vision?

While human inspectors working on assembly lines visually inspect parts to judge the quality of workmanship, machine vision systems use cameras and image processing software to perform similar inspections.  Machine Vision inspection plays an important role in achieving 100% quality control in manufacturing, reducing costs and ensuring a high level of customer satisfaction. Machine vision system inspection consists of narrowly defined tasks such as counting objects on a conveyor, reading serial numbers, and searching for surface defects. Manufacturers often prefer machine vision systems for visual inspections that require high speed, high magnification, around-the-clock operation, and/or repeatability of measurements.

Few other advantages of using Machine vision –

Accuracy – Today’s machine vision systems have a high degree of accuracy that can be achieved. With advances in learning as well as artificial intelligence you could actually build machines that can surpass human accuracy.

Reliability – This is another major advantage of Machine vision. Humans aren’t really designed for repetitive tasks. We are creative in nature. If you put a factory worker in assembly line and ask him to do the same thing over and over again for like 12 hours, he cannot be relied upon for giving accurate results. This won’t happen with Machine vision.

Inspection of the “invisible” – The human sight is limited to what’s in the visible spectrum. And that’s typically 400 to 700 nanometers. But with advanced multi spectral, hyper spectral imaging systems you could actually go beyond these ranges, see things which are not visible with the naked eye. Common applications of multi spectral imaging could be in food processing, health care, and pharmaceutical or even the military.                                                                                                                                                                                                                                      

Can it really replace humans?

Machine vision systems have made huge leaps in innovation in the past decade or two alone.  They’re used in everything from traffic and security cameras to food inspection and medical imaging – even the checkout counter at the grocery store uses a vision system!

When we look at each sub-component (ex: camera and Software), there’s no doubt that machines outperform humans.

Cameras

There are much faster cameras, they can reliably and with much higher precision capture images just not comparable to the human eye. HS and MS cameras can image scenes which are outside the visible spectral range.

Difference between human eye and cameraANGLE OF VIEW

With cameras, this is determined by the focal length of the lens (along with the sensor size of the camera). For example, a telephoto lens has a longer focal length than a standard portrait lens, and thus encompasses a narrower angle of view.                                                                                                        Unfortunately our eyes aren’t as straightforward. Although the human eye has a focal length of approximately 22 mm, this is misleading because

(i) the back of our eyes are curved,

(ii) the periphery of our visual field contains progressively less detail than the center, and

(iii) the scene we perceive is the combined result of both eyes.

RESOLUTION & DETAIL  

Most current digital cameras have 5-20 megapixels, which is often cited as falling far short of our own visual system. This is based on the fact that at 20/20 vision, the human eye is able to resolve the equivalent of a 52 megapixel camera (assuming a 60° angle of view).

However, such calculations are misleading. Only our central vision is 20/20, so we never actually resolve that much detail in a single glance. Away from the center, our visual ability decreases dramatically, such that by just 20° off-center our eyes resolve only one-tenth as much detail. At the periphery, we only detect large-scale contrast and minimal color.

Software

This is highly consistent for repetitive tasks and don’t fall prey to fatigue or boredom issues, etc. They are also consistent in decision making.For example, give 1000 images to a human at different days or times , the results will vary due to various factors and there is no consistency here. But the software will always give consistent results.Deep Learning is gaining much popularity due to its supremacy in terms of accuracy when trained with huge amounts of data.                                              Practically, Deep Learning is a subset of Machine Learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.For example,Language recognitionDeep learning machines are beginning to differentiate dialects of a language. A machine decides that someone is speaking English and then engages an AI that is learning to tell the differences between dialects. Once the dialect is determined, another AI will step in that specializes in that particular dialect. All of this happens without involvement from a human.Image caption generationAnother impressive capability of deep learning is to identify an image and create a coherent caption with proper sentence structure for that image just like a human would write.However, When It Comes To The System As A Whole, The Human Capability Is Still Largely Superior.

Multi-tasking – Humans can work on multiple responsibilities unlike machine vision where in the time required to teach system on each and everything is considerably high.

Decision making – Humans have the ability to make decisions from their past experience. But, even the most advanced robots can hardly compete with a 6 years old kid.

Augment Not Replace!

AI over the next few years only automates tasks, within broader processes, that are currently handled exclusively by humans. Organizations will divide many of their critical processes into a series of smaller tasks and see where they can benefit the most from automation and which tasks need to remain with humans. The goal here won’t be to displace people but to use AI to augment existing processes.

Machine Vision Is Reactive In Nature. It Only Tells You When Something Is Wrong Or Has A Defect.

For example,Finding the defects on the surface of gun parts. As this is a special case of analyzing the surface defects due to the visibility of defects only under UV light, the image acquisition was done using a color camera and UV light in the factory condition. The defects were clearly visible and trained accordingly.

(Picture credits – Qualitas Technologies)

Machine Vision can be used to segregate sure defects and unsure defects. Only unsure defects can be re-verified by humans.

One such example is, usage of Machine vision in defect detection.

Machine vision is used to detect surface defects on the UBS line (Under-body sealant) which is hard to inspect continuously by a human. Hence, AI based Machine vision is used here to do the task effectively and when a defect is identified, human inter-vision is needed re-verify the detected defect and fix it. This way humans and Machine vision technology join hands which results in augmentation.

Manual quality control to sample the output of machine vision systems identify gaps and errors.

An ideal example for this would be,

Online reading of QR code and characters on Blisters which was soporific in nature and most importantly less accurate.

(Picture credits – Qualitas Technologies)

Read More:https://bit.ly/304BIeu

[machinevision]

Vision Automation and Robotic Solution

ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

We Specialize In Industrial Vision-Based Solutions

Using Artificial Intelligence

Vision Automation and Robotic Solution

Automated Vision Inspection

Artificial Intelligence is set to disrupt the Machine Vision industry. Here are Qualitas, we’ve embarked on this journey nearly a decade ago. With our expertise in deploying well over a 100 industrial machine vision deployments all across the globe, we understand industrial automation. We deliver immense value to leverage the power of Machine Vision and AI for our customers. Our customers also value key insights that can be derived from these systems so they can improve their manufacturing processes, while giving them complete control over system maintenance and performance at the same time

Qualitas Technologies was founded in 2008 in Redmond, WA (USA), and later headquartered to Bangalore, India.

EagleEyeInspection System

Fully integrated (camera to cloud) vision system for industrial automation

Cloud Vision System

The Qualitas EagleEye is the latest product to be developed by Qualitas Technologies. The EagleEye comes with a fully customizable and modular image acquisition and processing unit. The image acquisition unit, with its flexible mounting arm, built in camera, and customizable illumination system is fully equipped to capture the clearest image for your specific requirement.

With it’s cloud based Deep Learning training module, you don’t need to invest in expensive AI training hardware and you can solve the most challenging applications at a fraction of the time and cost than current methods and products.

What We Do

Qualitas enables companies to automate visual processes in manufacturing. We don’t believe that humans can be replaced, but they need to be augmented with technology to realize the full benefit of automation.

Our expertise comes from more than a decade of experience in manufacturing and machine vision, starting from providing the best image acquisition design all the way to Deep Learning and AI software using the latest technology platforms and algorithms.

Read More:https://bit.ly/3fMER8L

[machinevision]

ARTIFICIAL INTELLIGENCE (AI) VS MACHINE LEARNING (ML) VS DEEP LEARNING (DL)

We Specialize In Industrial Vision-Based Solutions

Using Artificial Intelligence

Vision Automation and Robotic Solution

Automated Vision Inspection

Artificial Intelligence is set to disrupt the Machine Vision industry. Here are Qualitas, we’ve embarked on this journey nearly a decade ago. With our expertise in deploying well over a 100 industrial machine vision deployments all across the globe, we understand industrial automation.

Read More:https://bit.ly/3fMER8L

[machinevision]

Integrating Machine Vision & AI with Toyota Production System

Industrial Automation Solutions using Machine

Introduction to Toyota Production System

Toyota production system (TPS) is a lean manufacturing system, created by Taiichi Ohno. It focuses on the absolute elimination of waste, cost reduction, and producing high-quality products. TPS is implemented in industries for the following reasons:

It helps in monitoring quantity control to reduce costs by eliminating waste.

It enhances process and product quality.

Elements of Toyota production system

Just in time is a technique of supplying exactly the right quantity, at exactly the right time and at the exact location.

Jidoka is about building quality into the process. It uses tactics like poka-yoke, 5 whys, kaizens, and continuous improvement processes to improve quality. Machine vision and artificial intelligence solutions add value to jidoka tactics.

Machine Vision And Artificial Intelligence

Machine vision is the technology and methods used to provide image-based automatic inspection for industries. It uses a camera or multiple cameras to inspect and analyze objects automatically, usually in an industrial or production environment.

Artificial Intelligence refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Artificial intelligence supports the machine vision to make the right decision in the inspection.

Related Article: MACHINE VISION PROCESS FLOW

Using Jidoka tactics to integrate with machine vision and artificial intelligence

Here are the jidoka tactics that are poka-yoke, 5 whys, and Kaizens which is integrated with machine vision and artificial intelligence.

‘Poka-Yoke’s’ purpose is to achieve error proofing of processing activity and thereby make the process more robust. It is used in the inspection process to achieve a 100% inspection.  Machine vision and artificial intelligence can be used to ease the process of inspection as it is a non-contact technology and easy to integrate.  The speed being faster than manual labor is an added advantage. For example, identification of surface defects on doors by machine vision will reduce the outflow of defective parts to the next process, contributing to improved quality in the process within the industry. An average human would take about a minute to inspect, whereas a machine vision and artificial intelligence solution would inspect in just a few milliseconds, providing a high degree of accuracy.

‘5 Whys’ is an iterative interrogative technique used to explore the cause-effect relationships underlying a particular problem. The 5 whys technique is used to identify and get a clear understanding of the underlying problem. This enables us to provide customized solutions using machine vision and artificial intelligence to the clients that are accurate and consistent.

‘Kaizen’ is a Japanese word involving two concepts, KAI which means change and ZEN means better. Kaizen is the concept of improving a process with small continuous steps.  Kaizen is process-oriented. Improvisation of the process has to be done for the attainment of excellent results. It conglomerates innovation and continuous ongoing efforts in order to achieve continuous improvement and maintaining standards. Machine vision and artificial intelligence can be used as an innovative as well as practical solution to help industries continuously improve and maintain their standards by automating their inspection methodology and reduce their defect outflow.  This can be done by a four-step process, first is capturing images with the help of a specified camera. Second is solution development using annotation and deep learning algorithms. Next is the deployment of a solution for real-time inspection. Last is monitoring and fine-tune inspection accuracy. For example, ring counting is done with the help of a camera capturing rings using specialized lighting and our robust vision system validates the count and the equivalent result is shown in the display.  If the machine vision solution was not used, the ring counting inspection by humans would be inaccurate due to the small rings and thereby contributing to increasing the defects. Incorrect count of the rings would not be the right fit for the next part and would end up as scrap. Machine Vision and artificial intelligence solution benefit ring counting by improving its accuracy and at a much faster speed than humans.

Also Read: 3 Reasons for choosing Machine Vision in Manufacturing

Read More: https://bit.ly/35FW5C3

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Automated Inspection with the help of Machine Vision and AI Technology

Automated Inspection with the help of Machine Vision and AI Technology

Industries all around the globe compete hard to deliver products of the highest quality standards. To maintain or improve competitive inspection standards the methodology of quality control must be made efficient. Automation is an essential part of this improvement process as dependence on humans or manual processes would weaken the quality control process. A product’s inspection is of utmost importance in mass production industries and is mostly done through visual inspection. In order to achieve a 100% inspection, the industries spend a lot on the inspection. Often random sampling is used for inspection. Visual inspection is commonly done in the following ways i.e manually, semi-automated, or fully automated. The current trend of quality inspection adopted by most of the manufacturing companies is manual inspection.

In a production environment, there are two key activities that go into any quality inspection

Material Handling – transporting the products manufactured along the manufacturing supply chain to the next set of value-adding processes/tasks

Inspection – Which is the activity of visually inspecting the product/process for identifying defects.

Related Article: How to Improve Quality in Pharma Industry using Machine Vision

Manual Inspection

This is the least automated way of performing the inspection. The products or parts to be inspected are manually picked up and inspection is being done to identify various cosmetic defects like scratches, dents, burr, and other defects visible to the human eye. The performance of manual inspection is generally inadequate and inconsistent.

Why is Manual Inspection inefficient?

Endless routine jobs

Slow

Inaccurate

Ergonomic constraints

Also Read: Machine Vision is creating a new wave in the Automobile Industry

Semi-Automated Inspection

Semi-Automated Inspection utilizes automation for material handling and the manual operation here though is the inspection activity. Though there is an improvement from the manual inspection processes described above there still has to be constant coordination between the automation system and manual operator performing the inspection. If the speeds are high or if the human misses apart, defects can be missed and thus passed out to customers.

Related Article: How Can Food & Beverage Segment Benefit From Machine Vision Inspection

Fully Automated Inspection

In a fully automated system, the material handling as well as the inspection activity is automated. Many technology companies including Qualitas technologies develop cutting edge systems to automate the visual inspection process. Providing benefits like speed accuracy and traceability in the inspection process. Coupled with automated material handling, these systems can be fully autonomous reducing human dependence thus eliminating the errors that come with manual operations. Of late, AI technology has made significant strides in the computational aspects as well providing added advantage which was not available with earlier technologies.

Several practical reasons for automated inspection include:

Matching high-speed production with high-speed inspection

Higher accuracy

Freeing humans from their monotonous job

Lower expenditure on human labor

Performing inspection in unfavorable environments

No dependency on highly skilled human inspectors

Related Article: Integrating Machine Vision & AI with Toyota Production System

Machine Vision and AI Technology for Inspection

Machine vision focuses on image acquisition by various cameras with different resolutions. AI is used for image processing, where the software algorithms analyze the image to provide desirable results that match the preset inspection parameters. Machine vision and AI technology are increasing the speed and accuracy of the inspection process, thereby paving paths for new ways of improving quality aspects of the manufactured products.

Read More: https://bit.ly/3kaC6iC

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HOW TO IMPROVE QUALITY IN PHARMA INDUSTRY USING MACHINE VISION

Quality has always been the top concern in pharmaceutical manufacturing applications. Stringent FDA standards mean high levels of liability for errors in production. Machine vision plays a major role in delivering consistently high-quality products in the pharmaceutical industry, but machine vision can also deliver productivity gains, within the confines of strict quality demands.

Read More: https://bit.ly/35kOg4r

[machinevision]

PARAMETERS FOR LENS SELECTION

PARAMETERS FOR LENS SELECTION

The first step of any machine vision system is image acquisition. Image acquisition is the action of retrieving an image from a source, usually hardware systems like cameras, sensors, etc. It is the first and the most important step in the workflow sequence because, without an image, no actual processing is possible. In a machine vision system, the cameras are responsible for taking the light information from a scene and converting it into digital information i.e. pixels using CMOS or CCD sensors. The sensor is the foundation of any machine vision system. Many key specifications of the system correspond to the camera’s image sensor.

However, the light from the source has to be focused adequately by a lens on the sensor for it to capture the image with maximum clarity. The lens should provide appropriate working distance, image resolution, and magnification for your system.

Following are some of the important parameters to consider when selecting an appropriate lens for your system:

1. WORKING DISTANCE

Working Distance dictates the space in which the optical system of a machine vision system must work. Working distances generally get longer when objects are large, if they move through large distances, or if they need to be distanced from the camera for safety and environmental purposes. For example, for an object that resides in an ultra-high vacuum (UHV) chamber, the camera will have to be mounted outside the chamber and capture images through a window, as most cameras are incompatible with UHV. In such a case, the minimum working distance is the distance from the window to the object in the chamber.

Considering your working distance is important because, as the working distance changes, so does the image distance. This, in turn, increases or decreases the magnification.

Related Article: The Ultimate Guide to Machine Vision Camera Selection

The Field of View is a measurement of distance and is defined as the viewable area of the object under inspection. In machine vision applications, the lens focal length and the sensor size sets up a fixed relationship between the working distance and the field of view. It is the area of the inspection captured on the camera’s imager and that fills the camera sensor. The size of the field of view and the size of the camera’s imager directly affect the image resolution, which is one of the most important factors determining the accuracy of the system.

2. CAMERA SENSOR SIZE

The size of a camera sensor’s active area is crucial in determining the system’s field of view (FOV). Given a fixed primary magnification that is usually determined by the imaging lens, larger sensors yield greater FOVs. The standard sensor sizes are ¼”, 1/3″, ½”, 1/1.8″, 2/3″, 1″ and 1.2″, with larger available. The nomenclature of these standards dates back to the Vidicon vacuum tubes used for television broadcast imagers, so it is important to note that the actual dimensions of the sensors differ.

One issue that can potentially arise in an imaging application is the inability of an imaging lens to support certain sensor sizes. If the sensor is too large for the lens, the resulting image may appear to fade away and degrade towards the edges because of a phenomenon known as vignetting. This effect is also referred to as the tunnel effect since the edges of the field become dark. Smaller sensor sizes usually do not yield this vignetting issue.

Also Read, CAMERA FUNDAMENTALS IN MACHINE VISION

3. RESOLUTION

A high-resolution image can only be captured if a high-resolution lens is utilized. The lens also needs to be capable of resolving the pixel size accurately. The resolution of a lens is given in line pairs per millimeter and specifies how many lines on a millimeter appear as separate from one another. The more line pairs that appear as differentiated, the better is the resolution of the lens. Having a high-resolution lens can be beneficial as higher resolutions preserve details in the picture more accurately that would be completely lost in low-resolution images.

4. DEPTH OF FIELD

A prerequisite for a robust and accurate inspection is a sharp image in most of the cases. On the z-axis, only a small area within certain limits provides a sharp image: The depth of field is the scene space in depth, away from camera and optics, which appears sufficiently sharp in the video image generated by the camera. As an object is placed closer or farther than the set focus distance of a lens, the object blurs and both resolution and contrast suffer consequently. For machine vision applications, it is important that all features to be inspected are located within this depth of field area for maximum clarity and accuracy.

Related Article: IMAGE ACQUISITION COMPONENTS

Choosing the appropriate lens needed for a machine vision system requires an expert understanding of the application requirements and technology capabilities.

[machinevision]

Image processing is a method of performing specific operations on an image in order to get an enhanced image or to extract some information that is required from it. It is a type of signal processing in which input is an image and output may be image or information defining the features associated with that image. Nowadays, image processing is amongst the most rapidly growing technologies and is an essential part of machine vision systems. It forms a core research area within the computer science discipline.

Related Article: IMAGE ACQUISITION COMPONENTS

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