IMAGE PROCESSING
Advancing inline inspection
Matthew Dale investigates the technology behind inline computational imaging
R
esearchers at the AIT Austrian Institute of Technology are seeing increase demand from industry for
imaging technology that can deliver both 2D and 3D capabilities in one platform. It is for this reason that over the past five years the institute has been developing inline computational imaging. Computational imaging is itself a
fast-growing research field in which new image acquisition technologies are being combined with intelligent algorithms. By doing this, image information can be extracted, which conventional machine vision can’t usually capture. Two prominent examples of
computational imaging are photometric stereo and light field. Photometric stereo involves multiple images of a stationary object taken by a single camera with a fixed point of view. In each image, the scene is illuminated from a different angle. Light field imaging, on the other hand, consists of multiple images of an object taken from different viewing angles, which is often achieved using either multiple cameras, or by putting a microlens array between the lens and image sensor. Combining these multiple views with advanced algorithms gives more accurate and robust depth information. Inline computational imaging uses a
single area scan camera that captures several views of an illuminated moving object simultaneously by exploiting the relative motion between the object and camera. A number of images are taken, with the light coming from a different direction in each, resulting in a stack of images containing varying light field data. High-performance computational processing of this data then makes it possible to derive depth information and obtain an all-in-focus image with increased signal-to-noise ratio. Tis approach also captures photometric stereo data as the illumination angle varies, thanks to the relative movement between illumination
A 3D point cloud of a coin, which has metallic, glossy surfaces and a really fine surface structure
and object. By analysing the reflectance properties, the slope of the object’s surface – as well as information about the material – can be obtained. Te result is a full 3D reconstruction of the object as a point cloud, as well as enhanced, rectified 2D images of the object. ‘By simultaneously acquiring light field and photometric information, this approach provides a simple, scalable framework for simultaneous, high-speed 2D and 3D inline inspection,’ explains Petra Tanner, a senior research engineer for high-performance vision systems at the AIT’s Center for Vision, Automation and Control. ‘Tis enables a whole manner of both 2D and 3D inspection tasks to be performed together with a single camera, sensor and
‘Both 2D and 3D inspection tasks can be performed with a single camera, sensor and lens’
16 IMAGING AND MACHINE VISION EUROPE AUGUST/SEPTEMBER 2021
lens – that’s a really high benefit,’ Tanner adds. She says that integration, setup and maintenance is a lot simpler compared to other solutions using an array of cameras, such as light field imaging. An additional advantage over
photometric solutions is that, conventionally, the object has to be stopped underneath a central camera within a bulky light dome consisting of several illumination sources, each of which needs to be activated individually in a sequence as the images are captured. Inline computational imaging differs from this, as it uses four LED lights strobed at high frequency, which allows images to be captured as the object moves under the camera.
Tilting a circuit board Asked what the motivation for the AIT developing such a solution was, Tanner explains: ‘Tere are lots and lots of requests from industry for this technology, as there’s an increasing number of requirements or tasks that can’t be solved by 2D or 3D g
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