Machine Vision Filters

[Kody Chavva]

Machine vision is the application of computer vision and analysis. Vision systems are programmed to perform narrowly defined tasks such as counting objects on a conveyor, reading serial numbers and inspecting objects for surface defects. Common uses of this technology extend to many industries including semiconductors, automobiles, food, recyclable materials and pharmaceuticals. In many instances, machine vision performs roles previously handled by human beings. Often times, they are implemented in inspection systems requiring high speed, high magnification, 24-hour operation and/or repeatability measurements.

By selectively isolating the spectral signature of various objects, they can be carefully analyzed and inspected. Frequently, the sensors used in machine vision have detection wavelengths over a broad range of the spectrum from the UV through near infrared. Without proper filtering and attenuation of unwanted signal, the sensors would be ineffective as the registration of unwanted light creates high levels of noise. Filters increase the signal to noise ratio allowing for proper discrimination of desired wavelengths while blocking all other light.

As machine vision systems are used in a wide range of applications and cover a large portion of the electromagnetic spectrum, Omega Optical offers filters ranging from 410nm through 790nm. These filters are applicable to the configuration of many instruments and, with the ability to custom design and manufacture filters as well, we can meet the needs of any application.

Machine Vision Direct is an authorized distributor of Smart Vision Lights, a leading industrial machine vision lighting supplier. When you contact us, our engineers answer the phone and are more than happy to assist you in picking the vision system or accessories that best suit your system. You can even send us application images and details to have us assist in picking out an appropriate vision system.

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Machine vision, also sometime referred to as computer vision, is an extremely important field of computer science that is likely to play a gigantic role in the direction of technology and society moving forward.

But while many people will focus on the software aspect of machine vision, it is all-too-easy to forget the equally important practical elements that will influence overall performance. One such example is the use of optical coatings and filtering: both of which will either extend or severely limit the possible applications for this highly exciting technology.

When you think of computer vision, you might think about robotics: specifically, robots that move through a room. This is one application to be sure, but others also include VR, facial recognition, digital assistants, data processing, social media, and much more.

VR for example uses computer vision in order to understand 3D space, thereby keeping the user safe while they enjoy their immersive experience, while also tracking their virtual movements to their real-world ones.

The role of these filters is several fold. In some cases, filtering might be used in order to help protect the substrate underneath. For instance, a coating can prevent bright sun from damaging machinery and this could be important for a drone that is being flown in harsh weather conditions.

Another type of filter might be used in order to provide data not visible to the human eye. For example, an IR light can create a false color on a camera that can degrade the color reproduction and therefore many imaging cameras will use an IR-cut filter for the sensor.

There are many types of coated filters used in this technology. Typically, coated filters are intended to offer sharper cut on and off transitions and higher transmissions. They are superior in these ways to other colored glass filters.

Every coated filter will go through a unique manufacturing process that ensures it meets performance targets. Wavelength-selective filters are manufactured using the deposition of dielectric layers added to the substrate. These have high and low refraction indices respectively and can combine to produce a range of desired results.

As with all things though, it is important to consider the precise application and other goals of the project. There are huge varieties of different types of machine vision, and using the right filter will depend on the environment, the goals, and the type of image analysis.

Machine vision systems are now commonplace in many factory automation systems. In building these systems, developers must carefully evaluate the type of product to be inspected and how the proper choice of illumination and optics can be used to increase the contrast of captured images. In doing so, the throughput of the machine vision system will be improved since less time will be spent on processing the captured images to extract relevant information.

Before embarking on any specific illumination choice for any particular application, however, it is important to know what type of illumination system should be used. Today, many manufacturers offer LED illumination products in various types of configurations depending on the type of product to be inspected. For example, while flat diffuse surfaces may best be illuminated using a ring-light, the flat specular objects may require diffuse on-axis lights or cloudy day illuminators to increase the contrast of features to be inspected.

While the choice of such lighting is important, so is the choice of the wavelength of light to illuminate the part. Which frequency to use will be dependent on the properties of the part since these will determine which wavelengths will be reflected and absorbed. By selecting a wavelength that matches the feature of interest to be extracted and choosing a light source of the same color, the feature will appear brighter and vice versa.

Rather than experiment with multiple wavelengths, systems developers can choose to use optical filters to perform this task. By illuminating an object with a broad spectrum white light for example, band-pass filters can be placed in front of the camera to select a specific wavelength that offers the optimum contrast. In this way, the correct choice of illumination wavelength can be determined inexpensively. To ease the task of choosing the correct filter, companies such as Thorlabs (Newton, NJ, USA; www.thorlabs.com), Midwest Optical Systems (Palatine, IL; www.midopt.com) and Edmund Optics (Barrington, NJ, USA; www.edmundoptics.com) offer kits with a variety of different types of filters. Other filter manufacturers such as Chroma Technology (Bellows Falls, VT, USA; ) specializes in custom filter solutions, providing in-house engineering and production capabilities.

Optical filters also prove useful in reducing luminous intensity, increasing system resolution, eliminating glare, separating colors, color correction and, of course, increasing the contrast of the object being imaged. In machine vision applications such as arc welding inspection, it may be necessary to reduce the amount of light being captured by the camera. In such applications, neutral density (ND) filters can be used to reduce all the wavelengths of light equally. Using such filters, cameras with larger apertures can be deployed reducing the depth of field of the scene and thus better separating the object image from its background.

Because of this, a number of companies offer such ND filters especially tailored for machine vision and scientific applications. The TECHSPEC UV-NIR neutral density filters from Edmund Optics, for example, can attenuate light from the UV to the NIR (190-1700 nm) and are available in 12.5, 25, and 50 mm diameter sizes and optical densities of 0.3, 0.5, 1.0, 1.3, 1.5 and 2.0.

While neutral density filters are used to reduce the light impinging on the sensor of the cameras, band-pass filters can be used to increase image contrast while at the same time selectively passing wavelengths within a certain range. Image contrast is effectively increased because the axial or longitudinal chromatic aberration of the optical system will be reduced. In systems that exhibit such axial chromatic aberration, the refractive index of the lens varies slightly as a function of the wavelength. Since the refractive index of the lens is greater for blue light (shorter wavelengths) than red light (longer wavelengths) different wavelengths will be focused at different focal points along the axis (Figure 1).

Systems designers can compensate for this effect by choosing achromatic or apochromatic lenses to focus two or three wavelengths of light, respectively onto the same plane. Using less expensive lenses, band-pass filters that eliminate the UV and IR spectrum can also be used to reduce this type of chromatic aberration. In many imaging applications, such band-pass filters are used to enhance the contrast of a specific feature within an image.

If the color of this feature is known, the proper choice of lighting and filter allows a monochrome camera to be used to increase the contrast of the desired object (Figure 2). Here a number of green, blue and red band-pass filters from Midwest Optical Systems are used to filter an image of push pins illuminated with white light. As can be seen, each of the colors can be selectively highlighted using these filters. An added benefit of using monochrome cameras over color cameras in such applications is the increased resolution that can be obtained.

Today, most color cameras developed for machine vision systems use monochrome imagers with Bayer filter arrays that pass red, green and blue light to selected pixels. To obtain a color image, a Bayer interpolation algorithm is then performed (often in the FPGA) of the camera. This interpolation results in a somewhat lower quality color image with a loss of resolution and edge artifacts.

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