Do we still need Frame Grabber?

A frame grabber is a key element in an image processing system. It is used to acquire video information and to pipe that information to the succeeding stages of the system. This article describes how frame grabbers work and what to look for when choosing one.

Frame grabbers convert video images from cameras into digital format and transfer these digital images to PCs, which use the converted data to make decisions about the object(s) being inspected. While performing these functions quickly and reliably is critically important to the success of a machine vision inspection, frame grabbers are much more than mere data converters and transfer conduits. Frame grabbers offer a range of capabilities that can compensate for poor lighting, optics and ways that camera sensors present information to the frame grabber, increasing the quality of the images acquired and, ultimately, helping the machine vision system perform more reliable inspections.

Shedding light on the illumination compensation capabilities of frame grabbers
Garbage in, garbage out is true for any computing application, and machine vision is no exception. Unless the machine vision system has a clear, complete and non-distorted image to work with, it cannot provide a reliable inspection. When lighting conditions are insufficient to properly illuminate the object, or when the reflectance attributes of the object being inspected are highly variable, the machine vision system cannot perform a reliable inspection. Lighting might seem like a simple enough concept but, as anyone who has tried to take his or her own pictures for the new company catalog or the family Christmas card knows, achieving proper lighting is anything but simple. With a range of lighting concerns to address, even seasoned machine vision professionals who have a range of techniques like front lighting, back lighting and structured lighting and technologies like fluorescent bulbs, incandescent lights, strobes and LEDs at their disposal find that properly lighting each object can be challenging. High-performance frame grabbers can help compensate for poor lighting, optics and data presentation from the camera's sensor to the frame grabber.

Common machine vision lighting issues include:
- Varying levels of contrast on the image being inspected; for example, a polished wafer that has gone through CMP has a nonuniformity reflective surface, making it difficult to capture consistently illuminated images. Tracking or locating fiducials under these conditions is extremely challenging and has a direct impact on manufacturing quality

- Background noise is a common problem in packaging inspections, where the product being inspected is packaged in a colorful, graphical container or displayed on a busy background, making it difficult to pick up the area of interest for inspection

- Insufficient light

- Excessive light

- Uneven light across the camera's field of view

Certain frame grabber capabilities and techniques, however, can compensate for poor lighting conditions and for the camera sensor's inability to redress these issues, ensuring that the machine vision system has a reliable image to work with. These include:

Input signal conditioning
Input signal conditioning is crucial in minimizing the effects from camera variability or lighting fluctuations. In an analog frame grabber, range and offset controls compensate for either too much or too little lighting, and maximize the digitization of the image at certain intensities. By maximizing the digitization over the video range desired, the machine vision system has more accurate data to analyze, resulting in better system performance.

Some other lighting and camera effects that can deteriorate the quality of the images are the uniformity of the lighting field and the gamma efficiency of each pixel on the camera sensors. In the former, light intensity decreases from the center to the edges of the lighting field, while in the latter, gamma efficiency (an individual pixel's ability to convert photons to electrical charge) can vary by up to 10%. Real-time flat field correction (FFC), a feature available on some of the more sophisticated frame grabbers, can perform an FFC operation on the image data before it is transferred to the host to compensate for the effects of non-uniform lighting and variable gamma efficiency, providing a better quality image for the system to inspect.

Look-up tables
Lookup tables (LUTs) are frequently used to modify the input signal data for easier signal processing or better display capabilities. A LUT is basically an array of registers. The index of a particular register is a pixel's value, so, if a digitized pixel has value 128, it would point to register 128 in the array, and the value of the register would now be the new pixel value. For example, assume that an application involves inspecting objects that are acquired as 8 bit-per-pixel monochrome images (meaning that pixel values can range from 0 to 255). To compensate for the light/dark contrast of each one, some pixels need to be made darker and some lighter. You would review the image, one pixel at a time, using each pixel's value as an input index to your LUT. Once you have located the correct position in the table for a given pixel, you replace the original pixel's value with the corresponding new pixel value.

LUTs are extremely effective tools for equalizing or normalizing images captured under poor lighting conditions. Unfortunately, when implemented in software, LUTs can consume a considerable amount of processing time due to the extensive amounts of memory read/write operations. By using a frame grabber with LUTs implemented in the hardware, however, this time-consuming task can be off-loaded from the host to the frame grabber board, speeding processing times.

Histogram equalization
By depicting the dark/light value of each pixel and the contrast among pixels in an acquired image, histogram graphs are useful guides for changing the appearance of the image. Histogram equalization allows you to create an image with optimal lighting and contrast. By spreading out (or "equalizing") the pixels in a histogram of a dark, low-contrast image, for example, you can obtain a uniform pixel density to improve contrast—and give the machine vision system a better quality image to work with.

Take a look at your optics
You get new glasses, you see better, right? Not if the prescription is wrong, or the optician grinds the lenses for an astigmatism that you don't have. Similarly, investing in new, top-of-the-line optics by no means assures that all of the images a camera acquires and hands off to a frame grabber will automatically be as sharp as you want them to be.

A frame grabber cannot compensate for a bad optical design or for a poorly matched lens/camera combination. However, choosing the right kind of frame grabber for each optical system can maximize the potential of more powerful optics—while choosing the wrong one simply wastes money without achieving the desired increase in resolution. If, for example, a system designer specifies a high-resolution camera and lens with the needed resolving power, but selects a low-end frame grabber that cannot digitize high-resolution data, then there was no point investing in high-end optics. Machine vision system designers must properly match the optical system to the frame grabber to achieve the desired resolution.

Another use for a LUT
Every lens system is like a pair of sunglasses in that it affects different wavelengths of light that pass through the optical path in different ways. Certain wavelengths can pass unaffected, while others may be attenuated. If, for example, your machine vision system is inspecting for red apples and the lens attenuates red light, then the frame grabber can compensate by boosting the red signal through an RGB LUT to make sure that all pertinent information reaches the machine vision system.

Warping
Image distortion, a side effect of some optical systems, causes warping of the image, especially towards the edges. Typical examples are squares that curve inwards or outwards. If the inspection goal is to measure the square and make sure it's the exact same size as all the other squares, a distorted image will yield flawed inspection results. Or, if the object of the inspection is to search for a cross-hair fiducial and the distortion at the edges of the lens are causing the cross-hair to turn into a crossbow, then the pattern recognition software will never locate the fiducial.

Distortion can be adjusted for by warping algorithms that unwarp the image so that it looks "real" again, enabling the machine vision system to gauge the square based on the correct parameters. When standard cameras are used in a machine vision application, warping is usually done in software. However, when high-end cameras, i.e., linescan cameras that generate images at rates of up to 160 MB/sec, are used (as they are increasingly), the camera/software combination cannot warp images in real time. High-speed frame grabbers can, and warping is performed directly in the frame grabber hardware—typically, in an embedded processor.

What good is the data if you can't read it?
The manufacturers of CCDs for high-end, multiple-tap cameras are concerned about their sensors' light-gathering capabilities, image quality and the speed with which acquired data can be transferred to the frame grabber. CCDs are, therefore, designed to maximize these three characteristics. They are not designed to acquire and present data to the frame grabber in a user-friendly format, meaning that, when the image of an inspection object or feature is displayed, it often doesn't look anything like the object or feature that you are trying to inspect.

One common occurrence in machine vision applications is that information received from the camera is upside down, backwards (mirror image) or worse, as different sections of the image can be affected in different ways. Fortunately, using on-the-fly resequencing, some frame grabbers can compensate for this poor data presentation. For example, some multiple-tap cameras break data down into four quadrants. High-speed frame grabbers read data from multiple-tap sensors at the intersection of these four quadrants and resequence the data in real time, at speeds of nearly 1000 frames per second. Performing this function within the frame grabber takes a huge load off the host computer, freeing it up for other processing tasks.

Open your eyes!
There's a lot more to frame grabbers than meets the eye. These powerful machine vision components do not just blindly acquire and transfer data from CCDs. Frame grabbers can also compensate for sub-optimal lighting, optics and methods of receiving information from camera sensors, transforming incomplete, distorted, dark or otherwise poor quality data captures into complete, clear, high-contrast images. Better image quality ensures more reliable inspections, which in turn leads to fewer product defects, higher profits and all of those other reasons why users purchase a machine vision system in the first place.

Reference
Frame Grabbers Increase Inspection Reliability,
(Author: Philip Colet)

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