popular application of DCT. Image restoration may also use Fourier transforms to remove noise and blurring. Photogrammetry employs some kind of feature identification to extract measurements from images. These measurements can include 3D information when multiple images of the same scene have been obtained from different positions. The simplest photogrammetry systems measure the distance between two points in an image employing a scale. Including a known scale reference in the image is normally required for this purpose. Feature detection lets computers identify edges and corners or points in an image. This is a required first step for photogrammetry as well as the identification of objects and motion. Blob detection can identify regions with edges that
Figure 2: Machine vision gives systems (industrial or otherwise) high-level understanding of an environment setting from images. Image source: Wikimedia
How machine vision is advancing automation now Written by Jody Muelaner Figure 1: Use of machine vision for more sophisticated robotics applications is on the rise. Image source: John6863373 | Dreamstime.com
more established and efficient mathematical methods of extracting information from images. In contrast, the term computer vision typically describes more modern and computationally demanding systems – including
form of digital-signal processing involving image enhancement, restoration, encoding, and compression. Advantages over analog image processing include minimized noise and distortion as well as the availability of far more algorithms. One early image- enhancement use was correction of the first close-range images of the lunar surface. This used photogrammetric mapping as well as noise filters and corrections for geometric distortions arising from the imaging camera’s alignment with the lunar surface. Digital image enhancement often involves increasing contrast and may also make geometric corrections for viewing angle and lens distortion. Compression is typically achieved by approximating a complex signal to a combination of cosine functions – a type of Fourier transform known as a discrete cosine transform or DCT. The JPEG file format is the most
black-box approaches using machine learning or artificial
Machine vision is a collection of technologies that give automated equipment (industrial or otherwise) high-level understanding of the immediate environment from images. Without machine-vision software, digital images would be nothing more than simple unconnected pixel collections having various color values and tone intensities to such equipment. Machine vision lets computers (typically connected to machine controls) detect edges and shapes within such images to in turn let higher-level processing routines identify predefined objects of interest. Images in this sense aren’t necessarily limited to photographic
images in the visible spectrum; they can also include images obtained using infrared, laser, X-ray, and ultrasound signals. One fairly common machine-vision application in industrial settings is to identify a specific part in a bin containing a randomly arranged (jumbled) mix of parts. Here, machine vision can help pick- and-place robots automatically pick up the right part. Of course, recognizing such parts with imaging feedback would be relatively straightforward if they were all neatly arranged and oriented the same way on a tray. However, robust machine vision algorithms can recognize objects
at different distances from the camera (and therefore appearing as different sizes at the imaging sensor) as well as in different orientations. The most sophisticated machine vision systems have enabled new and emerging designs far more sophisticated than bin picking – perhaps no more recognizable than in autonomous vehicles, for example. Technologies related to machine vision The term machine vision is sometimes reserved to reference
intelligence (AI). However, machine vision can also serve as a catch-all term encompassing all methods of high-level information extraction from images; in this context, computer vision describes its underlying theories of operation. Technologies to extract high-level meaning from images abound. Within the research community, such technologies are often considered as distinct from machine vision. However, in a practical sense, all are different ways of achieving machine vision … and in many cases, they overlap.
Figure 3: The DLPC350 integrated circuit (IC) controller provides input and output trigger signals for synchronizing displayed patterns with a camera. It works with digital micromirror devices (DMDs) designed to impart 3D machine vision to industrial, medical, and security equipment. In fact, applications include 3D scanning as well as metrology systems. Image source: Texas Instruments
Digital image processing is a
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