Employing 3D surface orientation, mainly the effect it has on reflected light, photometric stereo for industrial applications creates a contrast image which emphasizes local 3D surface variations.
Courtesy of dedicated new algorithms, increasing awareness of the need for good lighting to create machine vision success, and cost-effective multi-light solutions, this method is gaining more attention.
The photometric stereo technique can show surface defects on textured surfaces such as synthetic leather.
Real-world objects possess three dimensions: height, depth, and width. In order for automated systems like robots to function effectively, they have to be able to “see” in these three dimensions. They are provided this “sight’ by machine vision systems, which include a camera, lighting, and a PC for image processing.
However, decreasing the amount of data which must be processed so as to locate and examine an object correctly creates one of the major challenges to the machine vision sector.
So as to shrink the data, machine vision designers would utilize lights, filters, and black-and-white cameras. These take care of color machine vision applications. The grayscale images which are created as a result can be processed more rapidly as they comprise of lesser data.
In a similar manner, engineers would design motion control systems and mechanical fixtures so as to solve a traditional 3D application with a 2D machine vision solution.
Designers are offered greater processing power with present-day’s field programmable gate arrays (FPGAs), microprocessors, and graphics processor units (GPUs). However, processing power is still limited. The most economical solution for 3D applications may be provided by a nascent machine vision method called “photometric stereo”.
3D Vision at a Glance
The need to minimize the amount of data required in color and 3D applications has been relieved by inexpensive processing power.
An example is provided by integrated laser triangulation systems for conveyor-based 3D systems, which have been assisted by inexpensive data processing, optics, and lasers. These systems are able to produce tens of thousands of 2D profiles per second in the process of creating a 3D object map.
Another option is offered by new time-of-flight cameras. These provide low-resolution 3D maps for a range of applications, without the safety hazards of laser illumination.
For 3D projects with a larger area, several pictures can be captured of the same object from various locations by fixing single camera photogrammetric systems on the end of a robot.
With these images, the 3D position of every pixel in the image can be calculated based on a predetermined geometric relationship between the camera and the object. Corresponding to large-area 3D inspections, two cameras are aligned side-by-side in order to imitate human eyes and trap 3D information.
However, in order to examine objects without a large field of view at high-speed, qualitative data is potentially very beneficial, whereas quantitative 3D data isn’t always essential for measurement purposes. This is where the photometric stereo technique comes into play.
Photometric Stereo Advantages
The main concern of photometric stereo is not measuring the height of any given pixel. Instead, this method forms a contrast image accentuating local 3D surface distinctions by using 3D surface orientation and its effect on reflected light. The distinctions revealed are potentially invisible when employing traditional 2D imaging.
When employing photometric stereo solutions, it is not essential to know the precise 3D relationship between the object tested and the camera, nor is it essential to use two cameras to capture 3D data. Instead, a single camera with several illumination sources is used.
By seeing an object under a variety of lighting conditions, its surface is assessed during the photometric stereo method. The basis of this technique is the observation that the amount of light a surface reflects relies upon the surface’s orientation with respect to the light source and the observer.
Thanks to new dedicated algorithms, a growing awareness of the need for good lighting to ensure the success of machine vision, and cost-effective multi-light solutions such as Smart Vision Lights’ LED Light Manager (LLM) (which allows four lights to be regulated via a simple browser-based interface at a lower cost than a smart camera break-out box or frame grabber), the application of photometric stereo in industrial applications is gaining more and more attention.
Presently, the exclusive benefits of photometric stereo applications are enabling many common industrial inspection applications which were formerly difficult, or impossible, to solve.
Application: Clips and Tires
Machine vision systems have continually had problems reading raised letters on parts. This example illustrates a plastic connector with many functional surface features, as well as a directional symbol and the number two. There is no contrast, as there is no variance between the raised letter and material of which the clip is comprised of.
Manufacturers have employed laser triangular systems on larger objects, like tires, so as to produce a 3D surface map. These laser scanning systems are typically a complex and expensive solution for 3D measurements, even if they have become a lot more integrated and effective recently.
Figure 1. (Photo courtesy of Matrox Imaging)
Figure 2. (Photo courtesy of Matrox Imaging)
Figure 3. (Photo courtesy of Matrox Imaging)
Figure 4. (Photo courtesy of Matrox Imaging)
In Figures 1-4, the photos, Smart Vision Lights’ linear miniature (LM) LED lights are situated at 90-, 180-, 270-, and 360-degrees around the tire’s perimeter so as to illuminate the black plastic clip. They are regulated by an LLM. As each exposure is activated by the Matrox camera, a light is activated from a different direction by the LLM.
Figure 5. (Photo courtesy of Matrox Imaging)
The camera feeds each image into a PC running an image library photometric stereo registration algorithm. This joins all corresponding pixels, defining local surface properties and creating one or more types of composite images from these. Examples can include a contrast image of the local 3D geometries or an albedo image as seen in Figure 5.
More is exposed by these composite images than by just any of the constituent images alone. The edges developing the black-on-black lettering on the surface of the clips is plainly revealed in the resulting composition. Furthermore, the edges of the different injection molded parts which comprise of the whole are exposed.
Application: Synthetic Leather Perforations
In the following instance, four more pictures of a synthetic leather material are illustrated (Figures 6—9). Leatherette, similar to the organic material which it imitates, has significant surface texture. It is nearly impossible for the naked eye to visualize 100% surface texture over the full image, let alone a computer.
Figure 6. (Photo courtesy of Matrox Imaging)
Figure 7. (Photo courtesy of Matrox Imaging)
Figure 8. (Photo courtesy of Matrox Imaging)
Figure 9. (Photo courtesy of Matrox Imaging)
In each constituent image, robust shadows are developed by the warp of the material while it lies on a supporting substrate, while, because of the strong light reflection, other parts of the image are inclined toward saturation.
The photometric stereo registration algorithm (Figure 10) creates a final composition which illustrates a texture which is uniformly illuminated over the full field of the camera’s view, with the highlighting of holes and a sharp contrast across each crevice.
Figure 10. (Photo courtesy of Matrox Imaging)
It is also possible to use the photometric stereo method on pores on metal-machined surfaces, such as engine heads. Other areas which will obviously be at an advantage because of these economical photometric stereo solutions are laser marking, cast parts, and direct part marking systems such as dot peen.
Photometric Stereo Outlook
The 3D realm in which man lives in will continue to be reliant on 3D vision solutions. A new forecast from DBMR Research estimated growth in the international 3D machine vision market at a CAGR of 9.5% between 2017 and 2024, increasing its yearly value from $15.4 billion to almost $32 billion.
However, high installation costs and a dearth of technical knowledge create the major challenges to 3D machine vision market growth. As the packaged photometric stereo registration tool and one-click programming of the LLM LED light manager reveal, the machine vision sector is ready for the next major step in 3D machine vision.
This information has been sourced, reviewed and adapted from materials provided by Smart Vision Lights.
For more information on this source, please visit Smart Vision Lights.