Digital cameras with color image sensors are now commonplace. The same is true for the computing power and device interfaces necessary to handle the additional data from color images. What’s more, as users become familiar and comfortable with machine vision technology, they seek to tackle more difficult or previously unsolvable applications. These circumstances combine to make color machine vision an area of mounting interest. Color machine vision poses unique challenges, but it also brings some unique capabilities for manufacturing control and inspection.

The color challenge

Color is the manifestation of light from the visible part of the electromagnetic spectrum. It is perceived by an observer and is therefore subjective – two people may discern a different color from the same object in the same scene. This difference in interpretation also extends to camera systems with their lenses and image sensors. A camera system’s response to color varies not only between different makes and models for its components but also between components of the same make and model. Scene illumination adds further uncertainty by altering a color’s appearance. These subtleties come about from the fact that light emanates with its own color spectrum. Each object in a scene absorbs and reflects (i.e., filters) this spectrum differently and the camera system responds to (i.e., accepts and rejects) the reflected spectrum in its own way. The challenge for color machine vision is to deliver consistent analysis throughout a system’s operation – and between systems performing the same task – while also imitating a human’s ability to discern and interpret colors.

The majority of today’s machine vision systems successfully restrict themselves to grayscale image analysis. In certain instances, however, it is unreliable or even impossible to just depend upon intensity and/or geometric (i.e., shape) information. In these cases, the flexibility of color machine vision software is needed to:

•  optimally convert an image from color to monochrome for proper analysis using grayscale machine vision software tools
•  calculate the color difference to identify anomalies
•  compare the color within a region in an image against color samples to assess if an acceptable match exists or to determine the best match
•  segment an image based on color to separate object or features from one another and from the background

Color images contain a greater amount of data to process (i.e., typically three times more) than grayscale images and require more intricate handling. Efficient and optimized algorithms are needed to analyze these images in a reasonable amount of time. This is where Matrox Design Assistant X color analysis tools come to the fore.

Matrox Design Assistant X color analysis steps

Matrox Design Assistant X includes a set of tools to identify parts, products, and items using color, assess quality from color, and isolate features using color.

The ColorMatcher step determines the best matching color from a collection of samples for each region of interest within an image. A color sample can be specified either interactively from an image—with the ability to mask out undesired colors—or using numerical values. A color sample can be a single color or a distribution of colors (i.e., a histogram). The color matching method and the interpretation of color differences can be manually adjusted to suit particular application requirements. The ColorMatcher step can also match each image pixel to color samples to segment the image into appropriate elements for further analysis using other steps such as BlobAnalysis.

                                              Color Matcher step

The ImageProcessing step includes operations to calculate the color distance and perform color projection. The distance operation reveals the extent of color differences within and between images, while the projection operation enhances color to grayscale image conversion for analysis using other grayscale processing steps.

The color analysis tools included in the Matrox Design Assistant X interactive development environment (and the Matrox Imaging Library (MIL) software development kit) offer the accuracy, robustness, flexibility, and speed to tackle color applications with confidence. The color tools are complemented with a comprehensive set of field‐proven grayscale analysis tools (i.e., pattern recognition, blob analysis, gauging and measurement, ID mark reading, OCR, etc.). Moreover, application development is backed by the Matrox Imaging Vision Squad, a team dedicated to helping developers and integrators with application feasibility, best strategy and even prototyping.

Assistant X

The use of artificial intelligence (specifically, machine learning by way of deep learning) in machine vision is an incredibly powerful technology with an impressive range of practical applications, including:

• Giving virtual assistants the ability to process natural language;
• Enhancing the e-commerce experience through recommendation engines;
• Assisting medical practitioners with computer-aided diagnoses; and
• Performing predictive maintenance in the aerospace industry.

Deep learning technology is also fundamental to the fourth industrial revolution, the ongoing automation of traditional manufacturing and industrial processes with smart technology, a movement in which machine vision has much to contribute.

Deep learning alone, however, cannot tackle all types of machine vision tasks, and requires careful preparation and upkeep to be truly effective. In this article we look at how machine vision—the automated computerized process of acquiring and analyzing digital images primarily for ensuring quality, tracking and guiding production—benefits from deep learning as the latter is making the former more accessible and capable.

Machine vision and deep learning: The challenges

Machine vision deals with identification, inspection, guidance and measurement tasks commonly encountered in the manufacturing and processing of consumer and industrial goods. Conventional machine vision software addresses these tasks with specific algorithm and heuristic-based methods, which often require specialized knowledge, skill and experience to be implemented properly. Moreover, these methods or tools sometimes fall short in terms of their ability to handle and adapt to complex and varying conditions. Deep learning is of great help but requires a painstaking training process based on previously collected sample data to produce results generally required in industry. Furthermore, more training is occasionally needed to account for unforeseen situations that can adversely affect production. It is important to appreciate that deep learning is primarily employed to classify data and not all machine learning tasks lend themselves to this approach.

Where deep learning does and does not excel

As noted, deep learning is the process through which data—such as images or their constituent pixels—are sorted into two or more categories. Deep learning is particularly well suited to recognizing objects or objects traits, such as identifying that widget A is different from widget B The technology is also especially good at detecting defects, whether the presence of a blemish or foreign substance, or the absence of a critical component in or on a widget that is being assembled. It also comes in handy for recognizing text characters and symbols such as expiry dates and lot codes.

While deep learning excels in complex and variable situations such as finding irregularities in non-uniform or textured image backgrounds or within an image of a widget whose presentation changes in a normal and acceptable manner, deep learning alone cannot locate patterns with an extreme degree of positional accuracy and position. Analysis using deep learning is a probability based process and is, therefore, not practical or even suitable for jobs that require exactitude. High-accuracy, high-precision measurement is still very much the domain of traditional machine vision software. The decoding of barcodes and two-dimensional symbologies, which is inherently based on specific algorithms, is also not an area appropriate for deep learning technology.

Where deep learning excels: Identification (left), detect defection (middle) and OCR (right)

Where deep learning does not excel: High-accuracy, high-precision pattern matching (left), metrology (middle), and code reading (right)

Matrox Imaging software

Matrox Imaging offers two established software development packages that include classic machine vision tools as well as image classification tools based on deep learning. Matrox Imaging Library (MIL) X is a software development kit for creating applications by writing program code. Matrox Assistant X is an integrated development environment where applications are created by constructing and configuring flowcharts (see graphic below). Both software packages include image classification models that are trained using the MIL CoPilot interactive environment, which also has the ability to generate program code. Users of either software development packaged get full access to the Matrox Vision Academy online portal, offering a collection of video tutorials on using the software, including image classification, that are viewable on demand. Users can also opt for Matrox Professional Services to access application engineers as well as machine vision and machine learning experts for application-specific assistance.

Answering the question “What is deep learning?” requires us to stick our heads down a rabbit hole. We say this because deep learning is a type of machine learning—which, in turn, is a type of artificial intelligence (AI). You now get the reference to the rabbit hole . . . Time now for some definitions to provide clarity.

Artificial intelligence: The simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind, such as learning and problem-solving.

Machine learning: The use and development of computer systems (hardware and software) that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.

Deep learning: A subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data.

What distinguishes deep learning is that it empowers  machines to learn from unstructured, unlabeled data, as well as labeled and categorized data. With all the rapid developments in deep learning, a lot of new applications  for machine vision have been introduced.  Time now for another definition:

Machine vision: 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 refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. A machine vision system uses a camera to view an image. Computer vision algorithms then process and interpret the image, before instructing other components in the system to act upon that data. Computer vision can be used alone, without needing to be part of a larger machine system.

GPUs for computer vision applications

Many technology companies have discovered the benefit of using GPUs (Graphical Processer Units) for computer vision applications due to their ability to handle the rapid parallel processing of images. Traditional GPUs from companies like NVIDIA are large, power-hungry PCIe boards running in the cloud or temperature-conditioned environments.   So how do industrial companies take advantage of GPU technology in the field, or what’s often called ‘the edge’?

NVIDIA Jetson

Introducing NVIDIA Jetson, the world’s leading small-footprint GPU platform for running AI in harsh environments at the edge of the action.  Its high-performance, low-power computing for deep learning and computer vision makes it the ideal platform for compute-intensive projects in the field. Some of Integrys’ most valued partners provide nimble solutions in this space. But before we look at these companies and their products, it’s advisable to ask, and answer, the question below.

What’s the difference between carriers and Jetson modules?

A carrier board is specifically designed to work with one of the NVIDIA Jetson modules allowing users to connect IO, cameras, power, etc., to their devices.  Together with JetPack SDK, the combination of the carrier and module is used to develop and test software for specific use needs.

Our Deep Learning Partners

Stevie: Carrier for Nvidia Jetson AGX Xavier. Used in PPE and temperature monitoring, robotics, deep learning, and smart intersections/ traffic control.   Featured product: JETBOX-STEVIE JETSON AGX XAVIER SYSTEM Learn More	Floyd: Carrier for Nvidia Jetson Nano & Xavier NX. Used in industrial safety, drone video surveillance and facial recognition. Featured product: JETBOX-FLOYD JETSON NANO / NX SYSTEM Learn More
Sentry-X Rugged Embedded System: Built for the NVIDIA® Jetson AGX Xavier™, Sentry-X is ideal for aerospace and defense applications, or for any market that can benefit from the Jetson AGX Xavier’s incredible performance in a rugged enclosure. Featured product: SENTRY-X RUGGED EMBEDDED SYSTEM POWERED BY NVIDIA® JETSON AGX XAVIER™ Learn More	Rogue: a full featured carrier board for the NVIDIA® Jetson™ AGX Xavier™ module, the Rogue is specifically designed for commercially deployable platforms, and has an extremely small footprint of 92mm x 105mm.   Featured product: ROGUE CARRIER FOR NVIDIA® JETSON™ AGX XAVIER™ Learn More
Leveraging convolutional neural network (CNN) technology, the Matrox classification tool within their Computer Vision library, MIL (Matrox Imaging Library) categorizes images of highly textured, naturally varying, and acceptably deformed goods. The inference is performed exclusively by Matrox Imaging-written code on a mainstream CPU, eliminating the dependence on third-party neural network libraries and the need for specialized GPU hardware.           Learn More	Featured product: MATROX IMAGING LIBRARY X
The Condor product line of GPGPU and video capture cards feature NVIDIA Quadro® GPUs with Pascal™ and Turing™ architecture. These processing powerhouses leverage the latest GPGPU advancements from NVIDIA for machine-learning and artificial intelligence applications, as well as standard rendering pipelines.           Learn More	Featured product: CONDOR 4107XX XMC XMC GRAPHICS & GPGPU CARD
FREE OFFER We have a NVIDIA Jetson AGX Xavier AI-at-the-edge computing platform (diamondsystems.com) Jetbox-Stevie from Diamond in our DEMO Lab. I would like to promote it and offer a “FREE” Demo by filling a form Free Offer

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