Calibration Patterns

Are you looking to pre-calibrate your camera with a custom calibration target to be as accurate as possible? Our custom calibration targets are printed directly onto a rigid on premium 1/8 inch Dibond, making them the perfect calibration boards to calibrate cameras.

Direct to substrate printing at 1000 dpi with anti-reflective surface "flood white" authentic matte finish allows for easy detection in your computer vision pipeline because of its optical performance and physical robustness.

Dibond signage has forever been an industry standard for indoor and outdoor business signs. The material is water, weather, warp resistant and rust proof. It's ultra flat and rigid structure makes Dibond the perfect material for calibration targets.

• Light weight
• Ultra flat surface
• Anti-reflective surface (flood white finish) pitch black on pure white

• Material Thickness: .125"
• Panel Type: Aluminium Composite Panel
• Temperature Range: from -50°C to +80°C
• Fire Classification: CLASS P/0/1
• Physical Weight: 0.78125 lbs per ft.²

Binarization of the camera's image followed by locating the black chessboard fields (quadrilaterals) allows the finding of chessboard corner candidates. A filtering step keeps only quads of a particular size. These get organized in a structured grid with dimensions akin to user specifications.

Corners (saddle points) are typically tiny and unbiased under lens distortion parameters or perspective transformations. Hence, the determination of corner locations with high accuracy is possible after initial pattern detection.

The whole checkerboard should be present in multiple images for detection in OpenCV, making it difficult to get information from the extreme image edges.

Subpixel refinement can occur after the pattern detection to determine the corners with subpixel accuracy. This utilizes exact pixel gray values around a particular corner position for high accuracy.

While their algorithm is a bit complex, CharuCo patterns overcome some shortcomings of traditional checkerboards. Nevertheless, they are easily detectable in OpenCV 3.x making it easier to use them.

CharuCo patterns have their identifiable and uniquely-coded checker field as their primary advantage allowing non-suitable and partly occluded images ideal for calibrating. For example, powerful ring lights can create non-uniform lighting on a calibration target, failing Checker Board detection. However, CharuCo patterns permit the use of the remaining saddle point detections to create high accuracy calibration.

Since we can position the pattern to be only partly visible to the camera, we can obtain information from the extreme corners of the camera image resulting in the adequate identification of lens distortion camera parameters.

In image processing, we can identify circles as "blobs" on an image plane. We can apply some conditions on binary blob regions like convexity, area, circularity, spacing, and more to eliminate bad feature candidates.

After identifying ideal candidates, the features' regular structure could also help in pattern determination and filtration. Since all pixels on the circle edges are usable in circle determination, image noise is much lower, resulting in more accuracy. However, unlike a checkerboard's saddle points, circles get pictured as ellipses in camera perspective. Image rectification accounts for this perspective.

Additionally, the unidentifiable lens distortion signifies the circles don't get imaged as perfect ellipses, resulting in a slight additional bias. Nevertheless, we can take the distortion model to be locally linear (abiding by homography/ perspective transformation). So, the error is negligible in most lenses.