Learn more about getting started with visual recognition and IBM Maximo Visual Inspection. Explore resources and courses for developers.

Two essential technologies are used to accomplish this: a type of machine learning called deep learning and a convolutional neural network (CNN).

2. https://hackernoon.com/a-brief-history-of-computer-vision-and-convolutional-neural-networks-8fe8aacc79f3 (link resides outside ibm.com)

Sign up for 15% off your next order (some exclusions apply). As a subscriber, you will receive a monthly newsletter with Platypod updates including special offers, kickstarter campaigns and other fun announcements!

Disposable Camera

Computer vision needs lots of data. It runs analyses of data over and over until it discerns distinctions and ultimately recognize images. For example, to train a computer to recognize automobile tires, it needs to be fed vast quantities of tire images and tire-related items to learn the differences and recognize a tire, especially one with no defects.

Put the power of computer vision into the hands of your quality and inspection teams. IBM Maximo Visual Inspection makes computer vision with deep learning more accessible to business users with visual inspection tools that empower.

1974 saw the introduction of optical character recognition (OCR) technology, which could recognize text printed in any font or typeface.3 Similarly, intelligent character recognition (ICR) could decipher hand-written text that is using neural networks.4 Since then, OCR and ICR have found their way into document and invoice processing, vehicle plate recognition, mobile payments, machine conversion and other common applications.

6. 7 Amazing Examples of Computer And Machine Vision In Practice, Bernard Marr, Forbes, April 8, 2019 (link resides outside ibm.com)

Paper Shoot camera review

Computer vision trains machines to perform these functions, but it must do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.

7. The 5 Computer Vision Techniques That Will Change How You See The World, James Le, Heartbeat, April 12, 2018 (link resides outside ibm.com)

Camp Snap camera

Ultra is flat, light, and compact. At the size of a smartphone and only 3.2 oz, Ultra fits in your pocket. Designed for small to medium equipment, like mirrorless, GoPro, and compact DSLRs. Not sure if Ultra or eXtreme is for you?

Digital Camera

Many organizations don’t have the resources to fund computer vision labs and create deep learning models and neural networks. They may also lack the computing power that is required to process huge sets of visual data. Companies such as IBM are helping by offering computer vision software development services. These services deliver pre-built learning models available from the cloud—and also ease demand on computing resources. Users connect to the services through an application programming interface (API) and use them to develop computer vision applications.

IBM Research is one of the world’s largest corporate research labs. Learn more about research being done across industries.

In 1982, neuroscientist David Marr established that vision works hierarchically and introduced algorithms for machines to detect edges, corners, curves and similar basic shapes. Concurrently, computer scientist Kunihiko Fukushima developed a network of cells that could recognize patterns. The network, called the Neocognitron, included convolutional layers in a neural network.

Machine learning uses algorithmic models that enable a computer to teach itself about the context of visual data. If enough data is fed through the model, the computer will “look” at the data and teach itself to tell one image from another. Algorithms enable the machine to learn by itself, rather than someone programming it to recognize an image.

Much like a human making out an image at a distance, a CNN first discerns hard edges and simple shapes, then fills in information as it runs iterations of its predictions. A CNN is used to understand single images. A recurrent neural network (RNN) is used in a similar way for video applications to help computers understand how pictures in a series of frames are related to one another.

Unleash the power of no-code computer vision for automated visual inspection with IBM Maximo Visual Inspection—an intuitive toolset for labelling, training, and deploying artificial intelligence vision models.

Ultra proves that powerful tools come in small packages. At just 3.2 oz, and 5.08" x 3.36", and 4.3 mm thick it provides rock-solid camera support. Don’t be fooled by Ultra’s slim stature—it will be the most reliable tool in your gear bag and we offer a 5-year warranty to back it up.

5. A Brief History of Computer Vision (and Convolutional Neural Networks), Rostyslav Demush, Hacker Noon, February 27, 2019 (link resides outside ibm.com)

Paper Shoot camera

By 2000, the focus of study was on object recognition; and by 2001, the first real-time face recognition applications appeared. Standardization of how visual data sets are tagged and annotated emerged through the 2000s. In 2010, the ImageNet data set became available. It contained millions of tagged images across a thousand object classes and provides a foundation for CNNs and deep learning models used today. In 2012, a team from the University of Toronto entered a CNN into an image recognition contest. The model, called AlexNet, significantly reduced the error rate for image recognition. After this breakthrough, error rates have fallen to just a few percent.5

At about the same time, the first computer image scanning technology was developed, enabling computers to digitize and acquire images. Another milestone was reached in 1963 when computers were able to transform two-dimensional images into three-dimensional forms. In the 1960s, AI emerged as an academic field of study and it also marked the beginning of the AI quest to solve the human vision problem.

Paper Camera

Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving or something is wrong with an image.

There is a lot of research being done in the computer vision field, but it doesn't stop there. Real-world applications demonstrate how important computer vision is to endeavors in business, entertainment, transportation, healthcare and everyday life. A key driver for the growth of these applications is the flood of visual information flowing from smartphones, security systems, traffic cameras and other visually instrumented devices. This data could play a major role in operations across industries, but today goes unused. The information creates a test bed to train computer vision applications and a launchpad for them to become part of a range of human activities:

Computer vision is used in industries that range from energy and utilities to manufacturing and automotive—and the market is continuing to grow. It is expected to reach USD 48.6 billion by 2022.1

A CNN helps a machine learning or deep learning model “look” by breaking images down into pixels that are given tags or labels. It uses the labels to perform convolutions (a mathematical operation on two functions to produce a third function) and makes predictions about what it is “seeing.” The neural network runs convolutions and checks the accuracy of its predictions in a series of iterations until the predictions start to come true. It is then recognizing or seeing images in a way similar to humans.

IBM has also introduced a computer vision platform that addresses both developmental and computing resource concerns. IBM Maximo® Visual Inspection includes tools that enable subject matter experts to label, train and deploy deep learning vision models—without coding or deep learning expertise. The vision models can be deployed in local data centers, the cloud and edge devices.

Scientists and engineers have been trying to develop ways for machines to see and understand visual data for about 60 years. Experimentation began in 1959 when neurophysiologists showed a cat an array of images, attempting to correlate a response in its brain. They discovered that it responded first to hard edges or lines and scientifically, this meant that image processing starts with simple shapes like straight edges.2

1. https://www.forbes.com/sites/bernardmarr/2019/04/08/7-amazing-examples-of-computer-and-machine-vision-in-practice/#3dbb3f751018 (link resides outside ibm.com)

While it’s getting easier to obtain resources to develop computer vision applications, an important question to answer early on is: What exactly will these applications do? Understanding and defining specific computer vision tasks can focus and validate projects and applications and make it easier to get started.

Computer vision is a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs—and to make recommendations or take actions when they see defects or issues.