The Vision Show is North America’s leading showcase of machine vision and imaging components and solutions. This show brings you up-to-date with the latest technologies from the top suppliers! Also offered are high-quality, yet affordable training sessions at The Vision Conference.

This is a must-attend event for:

• Users of machine vision and imaging technologies
• OEMs
• System Integrators
• Any company with in-house integration capabilities
• Automation Integrators
• Companies that want to learn how machine vision can benefit them
• Machine Builders

The Imaging Source will be exhibiting at Stand 413. See you there!

Please Note: This blog post is part of a series of four posts altogether. The posts include: Introduction, Part 1, Part 2 and Part 3.

Computer

Machine vision in the domains of industry, medicine and science are dominated by PCs and the operating system Windows®. The use of modern interfaces, such as USB and FireWire requires Windows® XP and Windows® Vista. Efficient visualization requires graphics hardware with on-board memory. If image sequences should be recorded, the computer configuration should be similar to that of video editing systems (fast processor, fast separate hard disk). The requirements of a computer configuration for automatic image analysis vary considerably. In case of simple applications with one camera and a slow sequence of images a simple low-end computer may be sufficient. However, increasing complexity, number of cameras and number of frames may lead to a processing load that has to be distributed amongst several PCs.

Software

The software has to perform four tasks. A driver integrates the camera into the operating system, while a programming tool supports the setting of the camera’s parameters as well as the transmission of the images.

The third task is the analysis of the images. Since there is no off-the-shelf analysis software for special cases, like the early detection of skin cancer, we have to develop it ourselves. The use of a programming tool may be very helpful. Independently of the tools, this work is based on two important requirements:

1. An expert has to be able to describe the problem (e.g. “incorrect drill-holes”) in a way that allows the realization of an algorithm.
2. The illumination has to be designed in a way that the problem is represented reliably by the reflected light.

The fourth task is the result’s visualization. This may vary from a LED which shines red or green to a complex visualization and archiving in a database.

Summary

These are the most important, very basic rules for the development of an machine vision system:

• The image is created by the interaction between light and object.
• The cameras has only one function, namely the electrical representation of the image in order to let it be analyzed by a computer.
• The software for this automatic image analysis usually is not available “off-the-shelf”. Therefore, it has to be written by a system engineer.
• If the object is not easy to describe, automatic image analysis tends to become complex. In these cases expert knowledge has to be converted to algorithms.

Please Note: This blog post is part of a series of four posts altogether. The posts include: Introduction, Part 1, Part 2 and Part 3.

Camera

As already discussed in the overview, the camera does not create the image but only transforms optical signals (light) into electrical ones (voltage) and digitizes them (raw digital image). Thus, a camera is not able to compensate for incorrectly chosen illumination and/or optics. On the other hand, it is important to select an appropriate camera to avoid a malfunction of the system and/or unnecessary costs. Please find an overview of the most important decision criterion below:

Monochrome / color

Rule of thumb: color cameras are only used, if the different colors of an image “carry” information. For all other applications, it is recommended to use monochrome cameras since color cameras bring along some disadvantages:

• They are less sensitive in comparison to monochrome cameras.
• Assuming the same number of pixels, the effective resolution of a color CCD is lower than that of a monochrome CCD. Every second pixel of a color CCD is sensitive to green, while blue and red share the remaining pixels.
• Since we expect a green, blue and red value for every pixel the raw digital image of a color CCD has to undergo a color interpolation. This interpolation requires extra processing power and bandwidth during the data transfer.

We will describe in detail how color cameras work in one of the next articles.

IR cut filter

In contrast to the human eye, CCDs are also sensitive to the near infrared (IR). To approximate the human eye, cameras are equipped with IR cut filters. However, cameras without IR cut filters offer more flexibility, since the user is able to adapt them to his or her requirements using custom filters.

Therefore, usually the manufacturers of modern machine vision cameras do not equip their monochrome cameras with IR cut filters and also offer a variation of color cameras without IR cut filters.

Format

The format describes the CCD’s size. It is an important parameter when deciding upon which lens to choose. The table to the right shows the formats of The Imaging Source FireWire cameras. In one of the next articles we will offer some general advice on how to select and setup lenses.

Resolution

To avoid unnecessarily high costs, as well as a large amount of data, the resolution should be as small as possible. For two typical application areas of machine vision there are the following rules of thumb: the measurement of a distance requires at least 10 pixels, while checking the presence of an object requires at least 2 or 3 pixels. Please note that these rules of thumb serve only as a basic indicator.

Frame rate (fps)

For visualization purposes, as well as for the setup of illumination, optics and a frame rate of 15 fps (frames per second) are usually sufficient. Automatic image analysis often requires a much lower frame rate.

I/O and trigger

Digital I/Os (Input/Output) and appropriate software allow the camera to control external devices and to respond to signals from external devices. The “Trigger” is a special input, which is comparable to the shutter button of a photo camera. A pulse appearing at this input starts the exposure of an image. After the output of this image the camera waits for a new trigger pulse.

Interface

The machine vision pioneers had to make their first steps with pick-up tube cameras. They were based on the analog video standards NTSC and PAL. One of the principal issues was the digitalization of the analog video signals.

Meanwhile, pick-up tubes have almost been completely replaced by CCD chips. These chips already provide a digital signal. Additionally, today we have PCs with fast and easy to handle digital connectors, such as FireWire. Therefore, new projects in the domains of industry, medicine and science are mainly based on FireWire cameras.

Please Note: This blog post is part of a series of four posts altogether. The posts include: Introduction, Part 1, Part 2 and Part 3.

Illumination

There is no image without light. An image is created due to the interaction between an object and photons. In contrast to this banality, in practice illumination is a complex technology. This is not only true for industrial, medical and scientific applications, but also for aesthetically oriented applications. Professional photo studios are not dominated by cameras but by the various types of illumination. The quick growth of the machine vision market permits some component manufacturers to concentrate fully on illumination. These manufacturers offer LED illumination in various versions. These component’s range of variation, in addition to their high price reflect the complexity of this subject.

Optics

Cameras used in the domains of industry, medicine and science are usually shipped without a lens. Therefore, the user is able to adapt the optics to his or her special requirements. Besides normal lenses microscopes, endoscopes and telescopes are also used. Due to their standardized mount, in machine vision, the so-called C mount lenses are widely used. The calculation of these lenses only requires three parameters, an addition, a multiplication and a division. Nevertheless, the parameter’s determination often leads to misunderstandings. We will show in one of the next articles how to avoid them.

To be continued…

Please Note: This blog post is part of a series of four posts altogether. The posts include: Introduction, Part 1, Part 2 and Part 3.

Authors

• Sebastian Bollhorst: General Manager of The Imaging Source Taiwan Branch Office. He holds a B.S. degree in International Business and Economics from the University of North Carolina at Charlotte, USA.

• Xiao Sun: Senior System Specialist at The Imaging Source Europe GmbH. He holds an M.S. degree in Electrical Engineering from University of Bremen, Germany, and a B.S. degree in Electrical Engineering from Beijing University of Aeronautics and Astronautics, China.

Introduction

I am looking for a camera which rejects parts with incorrect drillholes.

Everyday, we receive such inquiries. Often prospective customers are surprised when we answer:

Sorry, but cameras can’t do that.

This article will show why a complete machine vision system is required to solve the above mentioned problem and which part of the work is allocated to the camera. The article mainly address system engineers who are about to take first steps in the world of machine vision.

Overview

A machine vision system consists of 5 basic components: illumination, optics, camera, computer and software. At first glance, the camera is the source of the image. But actually, the image is created by the illumination. The CCD chip transforms the photons encountering it into an electrical signal. The camera electronics digitizes this electrical signal and make it available as a “Raw digital image”. The computer - or rather the software it runs - uses this raw digital image for two basic tasks: Visualization and/or automatic image analysis. Automatic image analysis is, however, only reliable in the case of controlled illumination and simple objects. In the context of visualization, the illumination’s influence on the resulting image should not be underestimated.