RCDN -- Robust X-Corner Detection Algorithm based on Advanced CNN Model

Accurate detection and localization of X-corner on both planar and non-planar patterns is a core step in robotics and machine vision. However, previous works could not make a good balance between accuracy and robustness, which are both crucial criteria to evaluate the detectors performance. To address this problem, in this paper we present a novel detection algorithm which can maintain high sub-pixel precision on inputs under multiple interference, such as lens distortion, extreme poses and noise. The whole algorithm, adopting a coarse-to-fine strategy, contains a X-corner detection network and three post-processing techniques to distinguish the correct corner candidates, as well as a mixed sub-pixel refinement technique and an improved region growth strategy to recover the checkerboard pattern partially visible or occluded automatically. Evaluations on real and synthetic images indicate that the presented algorithm has the higher detection rate, sub-pixel accuracy and robustness than other commonly used methods. Finally, experiments of camera calibration and pose estimation verify it can also get smaller re-projection error in quantitative comparisons to the state-of-the-art.Comment: 15 pages, 8 figures and 4 tables. Unpublished further research and experiments of Checkerboard corner detection network CCDN (arXiv:2302.05097) and application exploration for robust camera calibration (https://ieeexplore.ieee.org/abstract/document/9428389

Geometrically-driven underground camera modeling and calibration with coplanarity constraints for Boom-type roadheader

The conventional calibration methods based on perspective camera model are not suitable for underground camera with two-layer glasses, which is specially designed for explosion-proof and dust removal in coal mine. The underground camera modeling and calibration algorithms are urgently needed to improve the precision and reliability of underground visual measurement system. This paper presents a novel geometrically-driven underground camera calibration algorithm for Boom-type roadheader. The underground camera model is established under coplanarity constraints, considering explicitly the impact of refraction triggered by the two-layer glasses and deriving the geometrical relationship of equivalent collinearity equations. On this basis, we perform parameters calibration based on a geometrically-driven calibration model, which is a 2D-2D correspondences between the image points and object coordinates of the plannar target. A hybrid LM-PSO algorithm is further proposed in terms of the dynamic combination of the Levenberg-Marqurdt (LM) and Particle Swarm Optimization (PSO), which optimize the underground camera calibration results by minimizing the error of the nonlinear underground camera model. The experiment results demonstrate that the pose errors caused by the two-layer glass refraction are well corrected by the proposed method. The accuracy of the cutting-head pose estimation has increased by 55.73%, meeting the requirements of underground excavations

ADVANCING U.S. NAVY LOW-LIGHT UNDERWATER OPERATIONS

U.S. Navy research on extremely low-light (ELL) cameras in nighttime underwater operations is limited. This study aims to address this limitation in capability by quantifying the Teledyne Bowtech Limited Explorer Pro Low Light Monochrome Camera’s performance in the field as a function of water depth at night in the coastal ocean. To reach this goal, proven techniques like modulation transfer function (MTF) and contrast transfer function (CTF) analyses were applied to modified target patterns for lower-quality images. The new target pattern was tested on land using commercial cameras against a commercial test pattern chart for high-resolution cameras. The ELL camera vertical casts, including measures of surface lux and the water column characteristics, were performed at California’s Monterey Harbor and Bay in the presence of bioluminescence. The MTF results from the target pattern showed a steady MTF as the spatial frequency increased; the MTF decayed with increasing depth and decreasing lux. Furthermore, the MTFs showed that bioluminescence improves the MTF at depths > 24.5 m versus the MTF with no bioluminescence. The target pattern was detected at a maximum depth of 37 m. However, predicted maximum depths using a linear regression model were > 37 m with and without bioluminescence. The new ideal target pattern for the ELL video camera provides a foundation for nighttime underwater operations and the future development of underwater night vision goggles for the U.S. Navy.Office of Naval Research (Arlington, VA 22203-1995)Lieutenant Commander, United States NavyApproved for public release. Distribution is unlimited

Projector calibration method based on optical coaxial camera

This paper presents a novel method to accurately calibrate a DLP projector by using an optical coaxial camera to capture the needed images. A plate beam splitter is used to make imaging axis of the CCD camera and projecting axis of the DLP projector coaxial, so the DLP projector can be treated as a true inverse camera. A plate having discrete markers on the surface will be designed and manufactured to calibrate the DLP projector. By projecting vertical and horizontal sinusoidal fringe patterns on the plate surface from the projector, the absolute phase of each marker’s center can be obtained. The corresponding projector pixel coordinate of each marker is determined from the obtained absolute phase. The internal and external parameters of the DLP projector are calibrated by the corresponding point pair between the projector coordinate and the world coordinate of discrete markers. Experimental results show that the proposed method accurately obtains the parameters of the DLP projector. One advantage of the method is the calibrated internal and external parameters have high accuracy because of uncalibrating the camera. The other is the optical coaxes geometry gives a true inverse camera, so the calibrated parameters are more accurate than that of crossed-optical-axes, especially the principal points and the radial distortion coefficients of the projector lens

Compact single-shot hyperspectral imaging using a prism

We present a novel, compact single-shot hyperspectral imaging method. It enables capturing hyperspectral images using a conventional DSLR camera equipped with just an ordinary refractive prism in front of the camera lens. Our computational imaging method reconstructs the full spectral information of a scene from dispersion over edges. Our setup requires no coded aperture mask, no slit, and no collimating optics, which are necessary for traditional hyperspectral imaging systems. It is thus very cost-effective, while still highly accurate. We tackle two main problems: First, since we do not rely on collimation, the sensor records a projection of the dispersion information, distorted by perspective. Second, available spectral cues are sparse, present only around object edges. We formulate an image formation model that can predict the perspective projection of dispersion, and a reconstruction method that can estimate the full spectral information of a scene from sparse dispersion information. Our results show that our method compares well with other state-of-the-art hyperspectral imaging systems, both in terms of spectral accuracy and spatial resolution, while being orders of magnitude cheaper than commercial imaging systems