From The Editor | February 15, 2024

Double Vision A Look At Machine And Computer Vision

John Headshot cropped  500 px wide

By John Oncea, Editor


Simply put, machine vision is a technology that gives industrial equipment the ability to “see” what it is doing while computer vision is an AI that processes and visualizes information, much like a human would. But things in this life aren’t always simple, are they?

Let’s say you run a roadside stand selling strawberries, corn, watermelon, and other produce. You start with one stand then, over a couple of years, add a few more. Emboldened by your success, you decide to open a brick-and-mortar supermarket.

Defying the odds, your supermarket is a hit. So much so that you open locations in neighboring towns and, lo and behold, you’ve got yourself a chain!

You’ve also got problems as you can no longer sort through your produce by hand and, instead, you need to automate your inspection process, as well as your ability to inventory, ship, and accomplish any other business-related tasks.

Among the technologies you choose to lessen your burdens are machine vision and computer vision, both of which involve using technology to enable machines to see, interpret, and understand visual information. In your budding food empire, machine vision can be used to automate warehousing operations and enable order fulfillment, simplifying your decision to offer online ordering.

In your traditional stores, you can lean on computer vision to mimic Amazon and create a shopping experience devoid of the need for registers and queues. By installing ceiling-mountain 3D imaging cameras customers can “walk in, pick up items off the shelves, and walk out without even reaching for their wallet,” writes Imaging & Machine Vision Europe. Your store can “track individual customers, automatically updating their online shopping cart as they go, and charge the bill to their … account once they leave.”

Now that you’ve installed machine and computer vision you’ve created a seamless shopping experience, reduced or eliminated operational inefficiencies, and created a new problem – how to spend your new-found profits.

So, admittedly, our example is a simplified, sanitized version of reality. But machine vision and computer vision are very real technologies being used across multiple industries to provide machines with the ability to perceive and interpret the world around them much like humans do.

Let’s dig a bit deeper into these two ocular technologies that have rapidly advanced in recent years, leading to groundbreaking applications and promises of more to come.

The Same, But Different

As technologies go, machine and computer vision tend to stand out, even in an era where the lines between science fiction and reality are being blurred. Let’s look first at machine vision which can detect defects, position and measure parts, and identify, sort, and track products.

It also can, as part of a system, improve the accuracy and efficiency of the inspection process, often finding flaws that a human would otherwise miss. Also known as industrial vision, it is used by many industries – from manufacturing to medicine, security to transportation – and allows industrial equipment to “see” what it is doing and make decisions based on its observations.

This is accomplished by the use of digital sensors protected inside industrial cameras with specialized optics to acquire images. The computer hardware and software then process, analyze, and measure various characteristics for decision-making.

Machine vision aims to utilize image capture and image processing to identify and define actions. It encompasses both industrial and non-industrial applications where a combination of hardware and software provides operational guidance to devices based on the capture and processing of images.

Computer vision, on the other hand, is a broader field that encompasses the development of algorithms and techniques for interpreting visual information across various applications. These algorithms analyze visual data, extract features, and learn from vast amounts of labeled images to improve accuracy and performance over time.

Inspired by the human visual system, computer vision systems aim to recognize patterns, objects, and scenes in visual data. This involves tasks such as image classification, object detection, facial recognition, and image segmentation.

Computer vision and machine vision systems generally have the same components, such as an imaging device, usually a camera that contains an image sensor and a lens. They are also often used simultaneously and play a crucial role in advancing technology and enabling machines to perceive and understand the visual world. For instance, machine vision systems use computer vision to improve and automate industrial processes and work on a set of rules and parameters to support manufacturing applications such as quality assurance.

Although computer vision and machine vision have distinct applications, there is considerable intersection between the two fields. The progress made in computer vision, particularly in deep learning, has had an impact on the growth of machine vision systems, improving their abilities in areas like object recognition and classification. Conversely, machine vision technologies have aided the development of computer vision algorithms by providing datasets from real-world scenarios and offering insights into industrial applications.

The integration of computer vision and machine vision technologies has huge potential for unlocking new possibilities across various industries. By leveraging the strengths of both fields, businesses can develop more robust and versatile systems that can address complex challenges in areas such as robotics, logistics, and augmented reality.

How The Secret Sauce Is Made

Machine vision systems typically consist of cameras, lenses, lighting, image processing software, and sometimes specialized hardware such as frame grabbers or vision processors. These components work together to capture, process, and analyze images to perform specific tasks.

Machine vision techniques often involve a combination of image-processing algorithms tailored to the specific requirements of industrial applications. These techniques may include edge detection, feature extraction, template matching, and machine learning for classification or regression tasks.

While computer vision has a broad scope of applications, machine vision is a specialized form tailored specifically for industrial automation. Machine vision plays a crucial role in industries such as automotive, electronics, pharmaceuticals, and food and beverage, where precision and reliability are paramount. It enables manufacturers to detect defects, verify product dimensions, and automate tasks that were once labor-intensive and prone to human error.

Computer vision techniques involve a combination of image processing, pattern recognition, machine learning, and deep learning algorithms. These techniques are used to extract features from images, train models, and make predictions or decisions based on visual data.

The applications of computer vision are diverse and far-reaching. In the field of healthcare, computer vision is revolutionizing medical imaging, aiding in the diagnosis of diseases such as cancer, detecting anomalies in X-rays and MRIs, and assisting in surgical procedures. In autonomous vehicles, computer vision enables cars to perceive their surroundings, identify objects, and make real-time decisions to navigate safely on the roads.

In retail and e-commerce, computer vision powers recommendation systems, allowing businesses to personalize shopping experiences based on customer preferences and behavior. Additionally, computer vision is employed in quality control processes in manufacturing, monitoring for defects in products, and ensuring consistency in production.

Challenges and Considerations

Despite the remarkable progress in computer vision and machine vision, several challenges remain. One of the primary concerns is the ethical implications of widespread adoption, particularly regarding privacy and bias in algorithms. Ensuring transparency, fairness, and accountability in the development and deployment of vision systems is essential to mitigate potential risks and promote trust among users and stakeholders.

Furthermore, the performance of vision systems can be influenced by factors such as lighting conditions, occlusions, and variations in the environment. Addressing these challenges requires ongoing research and innovation to develop robust algorithms and hardware solutions capable of operating reliably in diverse real-world scenarios.

As we continue to push the boundaries of technological innovation, the future of computer vision and machine vision holds immense promise. From enhancing healthcare diagnostics to optimizing industrial processes, these technologies have the potential to reshape entire industries and improve countless aspects of our lives.

However, realizing this potential requires collaboration and responsible stewardship from researchers, engineers, policymakers, and society at large. By harnessing the power of vision technology while remaining mindful of ethical considerations and societal impacts, we can leverage its benefits to create a brighter and more inclusive future for all.