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Comparion between machine vision & human vision

Comparion between machine vision & human vision



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I hope this is the correct Stackexchange to ask this question.

I am trying to know : What is the current status of knowledge regarding human vision and pattern recognition.

More specifically,

  1. How does the human eye read signals from the cone cells? Is it row by row, column by column like a computer? or or there other things happening ?
  2. When detecting, say, an edge, or a connected component - does the human eye continue searching row by row? or does it jumpimmediately to the next neighbor? Are human cone cells arranged in arectangular gird with 8 neighbors per cone besides for the edges?(my guess is no). Then how are the neighbors addressed? Does somerace condition occur?

As you can see, I am not a biologist, I am a physicist working with computer vision. I have moderate knowledge of anatomy, but I am willing to learn.

Thank you


This is a potentially very very broad question, but I'll try to provide a simple answer that addresses the biggest misconceptions.

First of all, animal vision (and brains more generally) is massively parallel. There may be some serial processing steps, but these are also massively parallel. Computers need to digest information into stereotyped operations that can be executed on a CPU. The brain has separate dedicated machinery to each point in space for early vision, so there is no need to process individual "lines" or points in sequence: it all happens at once.

Photoreceptor inputs are converted into center-surround receptive fields in retinal ganglion cells, where light in the center excites and light in the surround suppresses (ON-center cells), or vice versa (OFF-center cells). These receptive fields are then transmitted to thalamus (the lateral geniculate nucleus), and from there to V1, primary visual cortex.

You can then combine many of these circular receptive fields to detect straight edges, like this:

From https://grey.colorado.edu/CompCogNeuro/index.php/CCNBook/Perception

The cells in V1 that respond to these "edges" are called "simple cells"; there are also "complex cells" that have more complicated receptive fields, and other sensitivity like to motion and color. Some computer vision strategies end up producing receptive fields that look a lot like the ones found in early visual areas, the earliest ones built out of Gaussian-filtered sin waves.

From V1, there are higher order visual areas that respond to things like shapes, motion, optic flow, etc.

Basic neuroscience textbooks tend to contain a lot of information on the early visual system, Purves Neuroscience is a good example, any edition is fine:

Purves, D., Augustine, G. J., Fitzpatrick, D., Hall, W. C., LaMantia, A. S., McNamara, J. O., & White, L. E. (2014). Neuroscience, 2008. De Boeck, Sinauer, Sunderland, Mass.


Abstract

In the present study, a machine vision based, online sorting system was developed, the aim being to sort Date fruits (Berhee CV.) based at different stages of maturity, namely Khalal, Rotab and Tamar to meet consumers’ demands. The system comprises a conveying unit, illumination and capturing unit, and sorting unit. Physical and mechanical features were extracted from the samples provided, and the detection algorithm was designed accordingly. An index based on color features was defined to detect Date samples. Date fruits were fed on a conveyor belt in a row. When they were at the center of the camera’s field of view, a snapshot was taken, the image was processed immediately and the maturity stage of the Date was determined. When the Date passed the sensor, positioned at the end of the conveyor belt, a signal was sent to the interface circuit and an appropriate actuator, driven by a step motor, was actuated, leading the Date toward an appropriate port. For validation of proposed system performance, entire samples were again sorted by experts visually. Detection rate of the system for Tamar and Khalal was satisfactory. Although the detection rate was insufficient for the Rotab stage, there was no a significant difference between system accuracy and that obtained by the experts. The speed of image processing system was 0.34 s. System capacity was 15.45 kg/h.


Difference Between Computer Vision And Human Vision

Difference Between Computer Vision And Human Vision. What is the difference between machine learning and computer vision? Such systems can help computers in learning and taking actions based computer vision takes images and videos as input and gives information such as size, shape, and color as output.

Adapting image processing algorithms and ground truth through active learning. The computer vision system uses an image processing algorithm to simulate human vision. Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text. With deep learning, a lot of new applications of. The complexity of human and computer vision.

SAYPOINT.NET: Know your computer OS and Application . from 4.bp.blogspot.com What is the difference between machine learning and computer vision? Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text. Examples of computer vision and algorithms. And this can be done by a human with the usage of the dedicated software (to name just. What is the difference between image processing and computer vision?

And that is the only common it means that at least one transformation is applied to an input file.

Computer vision describes the ability of machines to process and understand visual data By comparing computer vision and human vision, we find that the gap between the two needs to be addressed using machine learning, deep learning researchers from various german organizations and universities have worked on addressing the gap between computer vision and human vision. Welcome to the deep learning for computer vision course! Computer vision (cv) enables computers to see and understand digital images, such as photographs or videos. With deep learning, a lot of new applications of. Both these disciplines pertain to images. The resource person is dr.g.r.sinha professor, electronics and. Let me make an analogy. Computer vision is one of the hottest areas of computer science and artificial intelligence research, but it can't yet compete with the power of the human eye. Computers are trained using lots of images/videos and algorithms/models are built. Computer vision is one of the most important points in the autonomous vehicle race of the automotive industry. The complexity of human and computer vision. If you are a professor making final exam rules, for each.

Computer vision tries to do what a human brain does with the retinal input, it includes understanding and predicting the visual input. This difference makes computer vision transmission much faster than human vision, and is one of the reasons why in the future maverick, and all other and a final, trippy difference: For example, if the target is to enhance the. Computer vision is the field of computer science that focuses on replicating parts of the complexity of the human vision system and enabling computers early experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text. But in their paper, the researchers point out that most previous tests on neural network recognition gaps are based on.

What's the difference between human eyes and computer vision? from i0.wp.com Classically, many computer vision algorithms employed image processing and machine learning or sometimes other methods (e.g variational methods. And that is the only common it means that at least one transformation is applied to an input file. Computer vision is about enabling computers to see, perceive and understand the world around them. This is achieved through a combination of hardware and software. What is the difference between image processing and computer vision?

Though computer vision today has progressed much with algorithms for object detection.

Computer vision describes the ability of machines to process and understand visual data Automating the type of tasks the human similarly, computer vision is coming to the fore in 'smart cities' where it is being used to solve traffic and crime issues. It seems faces when they are not really there.computer vision system can be trained computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extracting numerical or symbolic. What is the difference between computer vision and human vision? What is the difference between image processing and computer vision? Previous experiments show a large difference between the image recognition gap in humans and deep neural networks. Let me make an analogy. It seems faces when they are not really there.computer vision system can be trained to see human faces but they have nothing like same inbuilt bias for seeing them when they may or may not even be there. The human brain is deeply wired for vision, to an extent which is not true at the moment in computer vision technology. Computer vision (cv) enables computers to see and understand digital images, such as photographs or videos. With deep learning, a lot of new applications of. Furthermore, we discuss different conceptual and basic levels of computer vision, including questions of difference between computer and human vision, and. Mesopic vision refers to an intermediate level between the two.

What is the difference between computer vision and human vision? If you are a professor making final exam rules, for each. Let's first discuss how digital cameras capture color information from images. Previous experiments show a large difference between the image recognition gap in humans and deep neural networks. Adapting image processing algorithms and ground truth through active learning.

Difference Between Computer Vision and Image Processing . from addepto.com What is the difference between image processing and computer vision? Previous experiments show a large difference between the image recognition gap in humans and deep neural networks. An understanding of human vision also. Here we unpack the differences between the two human inspectors are simply too risky for such detailed inspections and, when you compare human limitations with the capabilities of a computer. Both these disciplines pertain to images.

An understanding of human vision also.

Which is where computer vision comes in. Human vision is extremely sensitive to other human faces. Computer vision and the digital humanities: Let me make an analogy. Computer vision is about enabling computers to see, perceive and understand the world around them. A webinar organised by the department of bca, k.s.rangasamy college of arts and science. Computer vision systems like a digital camera or webcam contain a lens that focuses light onto a sensory surface made of doped semiconductor material sensitive to different wavelengths and intensities of light. These systems use cameras and. The resource person is dr.g.r.sinha professor, electronics and. It attempts to mimic human vision by recognizing objects in photographs, and then extricating information from. Adapting image processing algorithms and ground truth through active learning. Machine learning is the science of making computers learn and act like humans by feeding data and examples of cnn in computer vision are face recognition, image classification etc. This is achieved through a combination of hardware and software.

In the central part of the retina there is an area called the fovea, which contains the the human eye is very sensitive to colour differences, especially when these colours are observed in a controlled environment (typically a lighting cabinet. It seems faces when they are not really there.computer vision system can be trained to see human faces but they have nothing like same inbuilt bias for seeing them when they may or may not even be there. For example, if the target is to enhance the. What is the difference between image processing and computer vision? Machine learning is the science of making computers learn and act like humans by feeding data and examples of cnn in computer vision are face recognition, image classification etc.

Source: www.researchgate.net

What is the difference between computer vision and human vision? For example, if the target is to enhance the. How does computer vision work? Here we unpack the differences between the two human inspectors are simply too risky for such detailed inspections and, when you compare human limitations with the capabilities of a computer. It attempts to mimic human vision by recognizing objects in photographs, and then extricating information from.

How does computer vision work? Computer vision and the digital humanities: Computer vision enables cars to manage the relationship between the car and the. Let's first discuss how digital cameras capture color information from images. Computers are trained using lots of images/videos and algorithms/models are built.

Source: www.researchgate.net

What is the difference between image processing and computer vision? What is the difference between computer vision and human vision? It seems faces when they are not really there.computer vision system can be trained to see human faces but they have nothing like same inbuilt bias for seeing them when they may or may not even be there. It attempts to mimic human vision by recognizing objects in photographs, and then extricating information from. This requires the digital camera's sensor.

Automating the type of tasks the human similarly, computer vision is coming to the fore in 'smart cities' where it is being used to solve traffic and crime issues. Adapting image processing algorithms and ground truth through active learning. In fact, computer vision surpasses human vision in many applications such as pattern recognition. What is the difference between computer vision and human vision? Biological and computer vision, a book by harvard medical university professor gabriel kreiman, provides an accessible account of how humans kreiman's book helps understand the differences between biological and computer vision.

Computer vision systems like a digital camera or webcam contain a lens that focuses light onto a sensory surface made of doped semiconductor material sensitive to different wavelengths and intensities of light.

Computer vision is one of the most important points in the autonomous vehicle race of the automotive industry.

Previous experiments show a large difference between the image recognition gap in humans and deep neural networks.

Source: cdn.slidesharecdn.com

Classically, many computer vision algorithms employed image processing and machine learning or sometimes other methods (e.g variational methods.

Source: inteng-storage.s3.amazonaws.com

Both computer vision and machine vision use image capture and analysis to perform tasks with speed and accuracy human eyes can't match.


Computer Vision vs Machine Learning Global Trend – Past 5 Years

As said earlier, machine learning is a much mature and widely implemented technology as compared to computer vision. This also means that more people are aware of the use-case and applications of machine learning technology than computer vision. The same is deduced in the graph below highlighting the past trends of computer vision vs machine learning searches on Google search engine.

Different Computer Vision Applications Using Machine Learning Models

Today, machine learning and computer vision technology are frequently used in conjunction to create strong systems and algorithms capable of fast, and accurate results. Support Vector Machine (SVM), Neural Networks (NN), and Probabilistic graphical models are some examples of machine learning models for computer vision applications. Support vector machine is a supervised classification method that uses machine learning models to observe, analyze, and process datasets. Similarly, the Neural Network method includes layered networks of interconnected processing nodes. The advanced version of Neural Networks (Convolution Neural Network) is specifically used in the image recognition and classification processes.

Below we will be looking at some computer vision applications using machine learning models

Image processing involved manipulating or transform the image data to either improve the quality of the image or identify required information from it. The field of image processing has advanced considerably and today involved the use of complex machine learning, and computer vision algorithms that enable fast, and accurate processing of large datasets for the identification of hidden patterns. The AI image processing technique is used in various industries including remote sensing, agriculture, 3D mapping, forestry, water management, and others.

Some of the functionalities of AI image processing includes

Identifying objects & Patterns

Using machine learning and computer vision algorithm, the AI image processing technique is able to identify patterns, and objects of interest, which are otherwise unrecognizable to the naked eye.

Image Restoration

Image restoration functionality is meant to enhance the quality of image through transformation techniques for object identification

Image Tagging & Database Creation

AI image processing can also be used to tag images to facilitate the development of a dataset for easy retrieval and use at a later stage

Analyze and Alter Images

Automatically measuring, analyzing, and counting image objects through predefined rules

AI Image Processing Services for Enterprises and Businesses

Today, AI image processing technique has become invaluable for cross-industries, both private and public. Since AI image processing techniques can be used to identify patterns that otherwise go undetected through the naked eye, it’s widely implemented in medical, mining, petroleum, security, and other industries.

Some of the industries that greatly relies upon AI image processing includes

  • Life sciences research
  • Planning software
  • Retail
  • Agriculture
  • Manufacturing and assembly
  • Enterprise resource
  • Radiology
  • Forensics
  • Operations and logistics
  • Surveillance and monitoring
  1. AI-driven Software for Drones

AI-driven software for drones is another high-utility computer vision application that’s powered by machine learning models. AI drones software is a robust and powerful technology with wide-scale application in various industries from aerial mapping, to modeling, and analytics.

Applications of AI drones in real world

AI drones have quickly made inroads into various industries, automating the legacy systems for better efficiency and precision. The computer vision technology powered by robust machine learning algorithms makes it possible for the software to observe, process, analyze, and interpret drone imagery in real-time to identify and extract the required information.

AI drones powered by computer vision and machine learning technology are able to gather high-quality imagery, which is subsequently processed by AI-driven software. This near-real-time image acquisition and processing enable businesses across-industries to improve their operational performance and boost their productivity. These AI drones with the software are perfect tools to streamline operations in various fields including agriculture, terrain mapping, and others.

Livestock Management

Livestock management is a tedious and resource extensive industry, which requires high input from farmers/ranchers. However, advanced AI drones aided by powerful processing software can help streamline the processes involved in the industry. With AI drones, it’s easier than ever to count cattle and other livestock in real-time, even from remote places.

The technology has helped farmers to significantly improve their operational efficiencies, as well as, lower the cost of managing farms. The technology is also being used to identify unhealthy animals, and thereby, take timely actions to avoid further harm to healthy animals.

Terrain Mapping

Apart from livestock management, AI drones have also made significant inroads into civil engineering projects. Today, AI drones are excessively used across various civil engineering projects for faster, and precise terrain mapping, which is a prerequisite for projects.

AI drones are equipped with powerful sensors (LiDAR) and navigation systems to surveil the desired terrain and collect required data. The data is then processed using computer vision and machine learning models to create 3D models.

Precision Agriculture

Precision agriculture is yet another advanced application of AI drones. The agriculture industry is one of the most critical sectors for our survival. However, the sector has been facing various issues due to inefficient processes and legacy systems. To make it worse, the rapid increase in the world’s population is making it hard for the traditional agriculture industry to keep up with the food demand.

AI drones in recent years have become an indispensable tool for the agriculture sector, where the technology is used to automate various processes for increased efficiency, higher productivity, and lower costs. The technology today is used for crop planning, crop harvesting, soil monitoring, livestock management, crop health monitoring, and various other tasks. AI drones with powerful imaging systems are used to collect real-time visual data across vast cultivated areas, which is subsequently processed using computer vision and machine learning-based algorithms. This enables real-time data analysis and processing for farmers, improving efficiencies, and productivity while lowering the cost of the practice.

Image segmentation is the next evolutionary stage of image processing technique, powered by computer vision. The technique is already transforming the industry, paving the way for a high-tech future. The technique is also assisting the tech work to experiment in more challenging industries, making possible things that were once considered miracles.

Today, the image segmentation technique is already used in various futuristic applications including autonomous vehicles, robotics, drones, etc. Autonomous cars for once are the most realistic prospect of image segmentation. The technology has already matured considerably and has been rigorously tested by multiple companies. Once rolled-out for the public, this would significantly change the way humans commute.

Lastly, image annotation is yet another advance and highly in-demand application of computer vision with machine learning. The computer vision and machine learning algorithms enable image annotation software to visualize, process, analyze, and segment various objects in visual data (videos, and images). This subsequently helps the user to quickly and accurately annotate images on a massive scale.

Image annotation is also a highly useful technique that’s used for training AI and machine learning algorithms. This subsequently improves the accuracy of pattern recognition of the algorithms and thus helps in improving the quality of results through machine learning or AI algorithms.

Some common types of image annotations used in industry today include

  • Land-marking
  • Bounding box
  • 3D cuboid
  • Polygon annotation
  • Semantic segmentation

Machine Vision Functions

Machine vision systems perform tasks that can be organized around four basic categories or functions, which are:

Measurement functions are done through the comparison of a recorded dimension from a digital image against a standard valve to establish a tolerance or to determine if the observed value of the dimension is within acceptable levels of tolerance as called for in the design specification for that part.

Counting functions are used to establish whether the correct quantity of product is in place or whether the proper number of components or elements in a design has been produced. As an example, machine vision could be used to determine whether a six-pack of soft drinks coming off a production line at a bottling plant has six cans or bottles, or whether one or more is missing. At a manufacturing facility, machine vision might be used to inspect flanges that have been put through an automated drilling operation to determine if the proper number of holes has been drilled into each flange.

Decoding functions are used to decode or read one dimensional and two-dimensional symbologies used to uniquely tag products, such as linear bar codes, stacked symbologies, data matrix codes, QR codes, or Optical Character Recognition (OCR) fonts. This functional capability allows for the recording of historical data on a production process so that a record is available of a part&rsquos production. It can also enable automation of product sorting and serve to validate that the correct item is coming through the process at the correct time.

Location functions deal with establishing the position and orientation of a part in a process. This type of capability is valuable in automated assembly operations, as it can be used to verify that the component needed is in the correct place and is properly aligned within allowed tolerances, for the next step in the assembly process to occur. Machine vision systems can also be used to identify a specific part or component by locating a unique pattern or feature of that part, thus assuring that the item needed is not only in the correct position but that it is the correct item and not something else of similar appearance.

Periodic calibration of machine vision systems should take place just as with other types of metrology equipment.


You have probably heard of machine learning at this point, but have you heard the term machine vision?

While they seem similar, these terms actually mean totally different things. They can be implemented together for maximum efficiency, though!

Machine Learning

Machine learning is programming technology to be able to adapt on its own.

There are multiple techniques and strategies, but in the end the computer is able to use historical data while it functions.

Machine learning goes along with artificial intelligence and is used often in modern manufacturing.

Machine Vision

This technology supports artificial intelligence and machine learning.

Readwrite defined it by saying, "Machine vision joins machine learning in a set of tools that gives consumer- and commercial-level hardware unprecedented abilities to observe and interpret their environment."

When machines can properly observe the area around them they can become even more efficient and valuable.

Working together

So, how can these two technologies work together?

According to Readwrite, "Machine vision makes sensors throughout the IoT even more powerful and useful. Instead of providing raw data, sensors deliver a level of interpretation and abstraction that can be used in decision-making or further automation."

Machine vision can be used with sensors, cobots, and other IoT technologies.

It can also reduce waste! Just like other tech, machine vision can free up valuable employee time by performing repetitive, time consuming tasks.

It will be exciting to see how this technology evolves and grows in the future!


Advancing with Machine Vision

You may be very familiar with the area of “Machine Learning”, but what about “Machine Vision”? What comes to your mind when you first heard about it? As illustrated by the words, this is the “eyes” of a machine that can visualise objects appearing in front of it. This technology comes with a system utilising digital input captured by a camera to determine the next action. Machine vision has been contributing significantly to industrial automation and manufacturing, mainly by performing an automated inspection as part of the quality control procedures. Indeed, it has been practised in real operation since the 1950s and began gaining traction within the industry between 1980 and 1990s.

Let’s first look into a simple example fill-level inspection system at a brewery to understand the technology better.

Inspection sensor detects the presence of beer bottle passing through it, which triggers the vision system to lighten that specific area and capture an image of the bottle. Frame grabber (a digitising service) translates the image taken into digital output. The next step is followed by storing the digital file in memory to be analysed by the system software. In the end, direct comparison is made between the file and predetermined criteria to identify defects. If an incorrectly filled bottle is detected, a failed response is delivered signalling a diverter to reject the bottle. The operator can also view discarded bottle and real-time data on display.

Figure 1: Example of bottle fill-level inspection

This example demonstrates the usefulness of machine vision in automating daily inspection task carried out by workers, further boosting daily productivity, and bringing significance difference onto operational profit. However, such a system could only be realised by the combination of software and hardware, and the type of equipment needed in each vision system would be subjected to different requirements. Those typical components installed includes:

  • Sensors
  • Frame-grabber
  • Cameras
  • Lighting
  • Computer and software
  • Output screen or relevant mechanical components

Besides, there are currently three categories of measurement for the machine vision system:

  • 1D Vision System: Instead of looking at a whole image at once, 1D Vision analyses a digital signal one line at a time. This technique usually detects defection in materials manufactured in a continuous process, such as paper, cardboard and plastics.
  • 2D Vision System: Mostly involved inspections that require a range of measurements such as area, perimeter, shape, resolution, entre of gravity etc.
  • 3D Vision System: Made up of multiple cameras or one or more laser displacement sensors. The latter allows the measurement of volume, shape, surface quality and also 3D shape matching.

Uses and Advantages

Throughout the years, machine vision has been integrated with technologies such as machine learning and deep learning to better harness the usefulness of data to improve a machine’s autonomous behaviour on encountering variations. The figure below shows vivid examples of enhancing machine vision with artificial intelligence in the manufacturing and construction industry.


Figure 2: Examples of integration between machine vision and artificial intelligence

These examples show how artificial intelligence can lift the use of machine vision onto another level. Up until today, machine vision remains to have the highest coverage in industrial application due to the ease of use and multiple direct advantages offered to manufacturers, with the main ones listed as below.

  • Enhance product quality: Manufacturer can replace sample testing with 100% quality checks done via a camera system. Every batch of products can be reliably checked for flaws during the production process without any interruption.
  • Cut production cost: Through detailed visual inspection, defective parts are removed from the production process since the start. These faulty products do not proceed to the upcoming manufacturing stages and contribute to costs. Also, materials cost is saved by re-introducing them back to the production process at later stages. The system may also ‘self-learn’ to recognise recurrent defects. Such statistical information and behaviour would be absorbed into the system to understand the source of problems, further improving the system’s performance.
  • Improve the efficiency of production: Many products are still assembled manually, and machine vision integrated system can replace human labour. Workers could be allocated to other stages of production that require more workforce and human supervision. Moreover, machine vision works under excellent precision and speed for a long time, overcoming human’s disadvantage of feeling fatigue.
  • Error proofing: Human eye has its limitation in inspecting complex applications. The assistance on machine vision significantly brings downs the risk of misassembled products. Its system equipped with the right imaging specifications and software can quickly identify details that are hindered by the human eye.

Moving forward with machine vision

In the coming years, the global machine vision market is predicted to grow from USD 9.6 billion in 2020 to USD 13.0 billion by 2025, at a CAGR of 6.1% during the forecast period. This forecast is attributed to the growing demand for vision-guided robotic system and increasing application in pharmaceutical and food packaging industry in the wake of COVID-19. The COVID-19 pandemic has led manufacturers to realise the importance of automation in manufacturing that largely reduces human intervention involved in the process.

It is also notable that APAC countries such as China, South Korea and Japan are expected to hold major market share as they own some of the most extensive manufacturing facilities and autonomous manufacturing. It shall be a call for Malaysia to grab ample growth opportunities within the region by increasing the use of machine vision in the manufacturing industry. The fact that manufacturing has been contributing to the 2nd largest share in Malaysia’s GDP over the years shall build us a strong foundation to further apply this technology in scaling up the sector. Higher usage of machine vision would then drive down the capital cost required to acquire these software and hardware equipment tagged with considerable prices in the market. Regional factors, the unpredicted resurgence of COVID-19 and long-term benefits should urge Malaysian manufacturers to elevate its adoption level in machine vision before losing its competitive edge globally in producing electrical, electronic, rubber, chemical products etc.

Despite the positive outlook on market forecast, multiple challenges lie ahead on better and smarter usage of machine vision technology to unleash its vast potential. Development of technology has to keep in pace with human’s increasing demand over time, or even one step ahead. First, there are still uncertainties about the application of deep learning in machine vision, which uses convolutional neural networks (CNNs) to perform classification tasks, by learning from a set of training images in improving its identifying characteristics. Although processor and tool resources are considered sufficient, the number of available training images are still limited.

Next, the adoption level of machine vision in the non-industrial application remains at the infancy stage. Areas like driverless cars, autonomous farming, guided surgery and other non-industrial uses require more significant input of development and validation in ensuring its practicability to the market. These could be a vital part for the future growth of machine vision, instead of placing sole focus on the manufacturing industry that rides on the right track. Other than that, there are also challenges when it comes to the integration of 3D imaging for specific applications. Not all of the 3D machine vision applications are “ready for prime time”. For example, most 3D systems are capable of picking homogeneous (all the same) objects but picking heterogeneous and unknown objects possesses a challenge for 3D imaging.

Moreover, performing 3D imaging to reconstruct surface or object for measurement and differentiation purposes can be quite challenging at the scale of production. This is because a high volume of images is required to completely model and analyse the part. It is noted that there are also challenges in other areas such as embedded vision and robotics that were not laid out in this article to prevent over-enlarging the scope of discussion.

In conclusion, machine vision technology is leading its way into applications inside and outside of factory settings, gearing towards the path of Industry 4.0. It is a capability instead of an industry that can be integrated into various processes and technologies for greater convenience and business efficiency. We can expect to witness greater innovation and breakthrough in machine vision through the evolvement of artificial intelligence demonstrated over the years. In addition, the low possibility of social distancing measurements ending in near-term gives rise to a unique opportunity for machine vision in meeting business demands with reduced labour.

Written by Lim Khey Jian, Intern at 27 Advisory. Currently pursuing his degree in Chemical Engineering at The University of Manchester. He takes problems and difficulties as opportunities to grow. He enjoys badminton, football and books related to governance, economics and personal development and aims to contribute to society in any way possible. He believes that Malaysia has a lot of potentials to grow as a country and he is always ready to play his part as the nation moves forward.

Having more than 27 years in business, 27 Group is able to provide you with access to investors for competitive funding needs while providing better ways to operate your business through financial and corporate advisory. We are the only 100% Malaysian-owned local consulting firm that is fast, flexible and focused with unique expertise that blends of local socio-economic policy setting, engineering built assets globally and detailed in financial analysis.

We do project development integration to improve project returns and are committed to providing a sustainable environment for a better tomorrow. Our delivery model blends values important to humanity into business strategy through socio-economic transformation modules and we are passionate about building opportunities for the next generation to achieve their highest potential.

#rebuildinghumanity is 27 Group’s vision to collectively rebuild our nation through assets we build (eg. infrastructure, real estate, hospitals) and natural capital (gas resources, plantations, human talent) using innovative and sustainable methodologies.


Where is Machine learning used?

The use of machine learning systems happens all around us and is a mainstay of modern internet.

Machine learning systems serve to recommend a product you want to buy next on Amazon or a video you want to watch on Netflix.

With each Google search, several machine learning systems work together, ranging from understanding the language in which you&rsquore searching to tailoring your results so that « bass » fishing enthusiasts are not swamped with guitar results. Likewise, Gmail&rsquos spam and phishing recognition systems use auto-learning models to keep spam out of your inbox.

Among the most visible manifestations of the power of machine learning are virtual assistants, including Amazon&rsquos Alexa, Apple&rsquos Siri, Microsoft Cortana, and Google Assistant.

All of them strongly depend on machine learning in order to sustain their speech recognition as well as their skills to understand natural language, with an immense need for a corpus to answer questions.

In addition to these highly noticeable manifestations of machine learning, systems are starting to be used in almost every industry. Examples of such uses include : facial recognition for surveillance in countries such as China computer vision for driverless cars, drones and delivery robots, speech and language recognition and synthesis for chatbots and service robots providing assistance to radiologists to detect tumors with X-rays, Predictive maintenance of infrastructure through data analysis of IoT sensor data guiding researchers to identify genetic sequences linked to diseases and identifying molecules that could lead to more effective drugs in healthcare computer vision support that makes the Amazon Go supermarket possible without a checkout enabling reasonably accurate transcription and translation of speeches for business meetings and the list is endless.


Machine Vision 101

Machine vision uses sensors (cameras), processing hardware and software algorithms to automate complex or mundane visual inspection tasks and precisely guide handling equipment during product assembly. Applications include Positioning, Identification, Verification, Measurement, and Flaw Detection.

A machine vision system will work tirelessly performing 100% online inspection, resulting in improved product quality, higher yields and lower production costs. Consistent product appearance and quality drives customer satisfaction and ultimately market share.

A machine vision system consists of several critical components, from the sensor (camera) that captures a picture for inspection, to the processing engine itself (vision appliance) that renders and communicates the result. For any machine vision system to work reliably and generate repeatable results, it is important to understand how these crticial components interact.

The following sections will provide you with an introduction to lighting, staging, optics and cameras, all critical components of a successful machine vision solution. Additional help on these topics is available from your distributor or integrator, from IPD, and from vendors of lighting and lenses

Lighting Staging Lenses Cameras

Applications

Industrial applications of Machine Vision include:

Lighting

The human eye can see well over a wide range of lighting conditions, but a machine vision system is not as capable. You must therefore carefully light the part being inspected so that the machine vision system can clearly 'see' them.

The light must be regulated and constant so that the light changes seen by the machine vision system are due to changes in the parts being inspected and not changes in the light source.

You will want to select lighting that 'amplifies' the elements of the part that you want to inspect and 'attenuates' elements that you don't want to inspect. In the left picture, poor lighting makes it difficult to read the letters on this part. In the right picture, the lighting has been selected to clearly show the lettering.

Images courtesy of NER / RVSI, Inc.

Proper lighting makes inspection faster and more accurate. Poor lighting is a major cause of failure in machine vision inspection systems.

In general, the available or ambient light is poor lighting and will not work. For example, the overhead lights in a factory can burn out, dim or be blocked, and these changes might be interpreted as part failures by the machine vision system.

Selecting the proper lighting requires some knowledge and experience. Our distributors and lighting vendors will be able to do an analysis of the parts you want to inspect and recommend proper lighting.

Recommended Lighting Vendors

Teledyne DALSA works with the following lighting vendors:

Advanced Illumination
(www.advancedillumination.com)
24 Peavine Drive
Rochester, VT 05767
USA
802-767-3830 x221
CCS America
(www.ccsamerica.com)
CCS America, Inc.
48 Weston St.
Waltham, MA 02453
USA
781-899-2494
Metaphase Technologies
( www.metaphase-tech.com )
3580 Progress Drive
Bensalem, PA 19020
USA
215-639-8699
ProPhotonix Limited
( www.prophotonix.com )
32 Hampshire Road
Salem, NH 03870
USA
800-472-4633
Smart Vision Lights
(www.smartvisionlights.com)
2359 Holton Road
Muskegon, MI 49445
USA
231-722-1199

Machine Vision Lighting Techniques

Staging

Staging usually is mechanical. It also usually includes a Part-in-Place sensor that tells the machine vision system when a part is in front of the camera. This sensor is usually a simple light source and photoelectric detector, for example

Staging, sometimes called fixturing, holds the part to be inspected at a precise location in front of the camera for a Vision Appliance™ to 'see'. Staging is required for three reasons:

  1. To ensure that the surface of the part that you want to inspect is facing the camera. In some cases the 'parts' may be rotated to inspect multiple surfaces.
  2. To hold the part still for the brief moment required for the camera to take a picture of the part. If the part moves too much while the picture is taken, the image may blurr. In some cases the parts move so slowly that they do not need to be held still for a good picture. In other cases a 'détente' or other mechanism holds the part still for a brief moment. Generally, the motion of the part is 'frozen' by turning the light on very briefly or by using a high-speed electronic shutter, standard on the ipd recommended cameras.
  3. To speed up the processing by putting the part in a location known to the Vision Appliance. All machine vision systems must first search to find the part in the image, and this takes time. If you can arrange the staging to always put the part in about the same location, then the vision system 'knows' where the part is and can find it much more quickly.

Optics and Lenses

The lens gathers the light reflected (or transmitted) from the part being inspected, and forms an image in the camera sensor. The proper lens allows you to see the field-of-view you want and to place the camera at a convenient working distance from the part.

To pick the proper lens you will first need to know the field-of-view (FOV) and the working distance. The FOV is the size of the area you want to capture.

Here is a typical example: If the part to be inspected is 4" wide and 2" high, you would need a FOV that is slightly larger than 4", assuming your staging can position the part within this FOV. In specifying the FOV you have to also consider the camera's "aspect ratio" - the ratio of the width to height view. The cameras used with Vision Appliances™ have a 4:3 aspect ratio. In the previous example, the 4" x 2" part size would fit in a 4:3 aspect ratio, but a 4" x 3.5" part would require a larger FOV to be entirely seen.

The working distance is approximately the distance from the front of the camera to the part being inspected. A more exact definition takes into account the structure of the lens.

From the FOV and working distance and the camera specifications, the focal length of the lens can be estimated. The focal length is a common way to specify lenses and is, in theory, the distance behind the lens where light rays 'from infinity' (parallel light rays) are brought to a focus. Common focal lengths for lenses in machine vision are 12 mm, 16 mm, 25 mm, 35 mm and 55 mm. When the calculations are done, the estimated focal length will probably not exactly match any of these common values. We typically pick a focal length that is close and then adjust the working distance to get the desired FOV.

There are other important specifications for lenses, such as resolution (image detail - depends on the camera and the lens), the amount and type of optical distortion the lens introduces and how closely the lens can focus.

Given all of these issues, we recommend that you work closely with your DALSA IPD distributor to choose the appropriate lens for your application.


Show/hide words to know

Enzyme: a protein that changes the speed of chemical reactions.

Gene: a region of DNA that instructs the cell on how to build protein(s). As a human, you usually get a set of instructions from your mom and another set from your dad. more

Organ: a specialized or distinct structure that is made from groups of tissues (e.g., heart, brain, etc.).

Protein: a type of molecule found in the cells of living things, made up of special building blocks called amino acids.


R.C. Fong and W.J. Scheirer contributed equally to this work.

Affiliations

Department of Engineering Science, University of Oxford, Information Engineering Building, Oxford, OX1 3PJ, United Kingdom

Department of Computer Science and Engineering, University of Notre Dame, Fitzpatrick Hall of Engineering, Notre Dame, IN, 46556, USA

Department of Molecular and Cellular Biology, School of Engineering and Applied Sciences and Center for Brain Science, Harvard University, 52 Oxford St., Cambridge, MA, 02138, USA