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 Unpublished further research and experiments of Checkerboard corner detection network CCDN (arXiv:2302.05097) and application exploration for robust camera calibration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

Periodic Optical Variability of Radio Detected Ultracool Dwarfs

A fraction of very low mass stars and brown dwarfs are known to be radio active, in some cases producing periodic pulses. Extensive studies of two such objects have also revealed optical periodic variability and the nature of this variability remains unclear. Here we report on multi-epoch optical photometric monitoring of six radio detected dwarfs, spanning the $\sim$M8 - L3.5 spectral range, conducted to investigate the ubiquity of periodic optical variability in radio detected ultracool dwarfs. This survey is the most sensitive ground-based study carried out to date in search of periodic optical variability from late-type dwarfs, where we obtained 250 hours of monitoring, delivering photometric precision as low as $\sim$0.15%. Five of the six targets exhibit clear periodicity, in all cases likely associated with the rotation period of the dwarf, with a marginal detection found for the sixth. Our data points to a likely association between radio and optical periodic variability in late-M/early-L dwarfs, although the underlying physical cause of this correlation remains unclear. In one case, we have multiple epochs of monitoring of the archetype of pulsing radio dwarfs, the M9 TVLM 513-46546, spanning a period of 5 years, which is sufficiently stable in phase to allow us to establish a period of 1.95958 $\pm$ 0.00005 hours. This phase stability may be associated with a large-scale stable magnetic field, further strengthening the correlation between radio activity and periodic optical variability. Finally, we find a tentative spin-orbit alignment of one component of the very low mass binary LP 349-25.Comment: Accepted to The Astrophysical Journal; 22 pages; 12 figure

Discovery and Correction of Bias in Precision Landmark Location

Precision Landmark Location (PLL) estimation is an integral part of 3D motion tracking. Circular landmark location estimation is one method of PLL. Current methods of estimation lead to systematic errors with a magnitude of up to .02 pixels. Estimation inaccuracies of this magnitude lead to unacceptable errors in depth measurement, the largest source of error. In the scope of this thesis, inadequacies in circular landmark location are uncovered and techniques to correct these errors are analyzed, tested, and demonstrated. Deviations in simulated images are seen to be reduced by a factor of three and the variances of real-world data were reduced by half. This thesis predicts and observes increased accuracy in the 3D motion tracking technology

Astrometry and Photometry with Coronagraphs

We propose a solution to the problem of astrometric and photometric calibration of coronagraphic images with a simple optical device which, in theory, is easy to use. Our design uses the Fraunhofer approximation of Fourier optics. Placing a periodic grid of wires (we use a square grid) with known width and spacing in a pupil plane in front of the occulting coronagraphic focal plane mask produces fiducial images of the obscured star at known locations relative to the star. We also derive the intensity of these fiducial images in the coronagraphic image. These calibrator images can be used for precise relative astrometry, to establish companionship of other objects in the field of view through measurement of common proper motion or common parallax, to determine orbits, and to observe disk structure around the star quantitatively. The calibrator spots also have known brightness, selectable by the coronagraph designer, permitting accurate relative photometry in the coronagraphic image. This technique, which enables precision exoplanetary science, is relevant to future coronagraphic instruments, and is particularly useful for `extreme' adaptive optics and space-based coronagraphy.Comment: To appear in ApJ August 2006, 27 preprint style pages 4 figure

Discovering Regularity in Point Clouds of Urban Scenes

Despite the apparent chaos of the urban environment, cities are actually replete with regularity. From the grid of streets laid out over the earth, to the lattice of windows thrown up into the sky, periodic regularity abounds in the urban scene. Just as salient, though less uniform, are the self-similar branching patterns of trees and vegetation that line streets and fill parks. We propose novel methods for discovering these regularities in 3D range scans acquired by a time-of-flight laser sensor. The applications of this regularity information are broad, and we present two original algorithms. The first exploits the efficiency of the Fourier transform for the real-time detection of periodicity in building facades. Periodic regularity is discovered online by doing a plane sweep across the scene and analyzing the frequency space of each column in the sweep. The simplicity and online nature of this algorithm allow it to be embedded in scanner hardware, making periodicity detection a built-in feature of future 3D cameras. We demonstrate the usefulness of periodicity in view registration, compression, segmentation, and facade reconstruction. The second algorithm leverages the hierarchical decomposition and locality in space of the wavelet transform to find stochastic parameters for procedural models that succinctly describe vegetation. These procedural models facilitate the generation of virtual worlds for architecture, gaming, and augmented reality. The self-similarity of vegetation can be inferred using multi-resolution analysis to discover the underlying branching patterns. We present a unified framework of these tools, enabling the modeling, transmission, and compression of high-resolution, accurate, and immersive 3D images

Pose Invariant Gait Analysis And Reconstruction

One of the unique advantages of human gait is that it can be perceived from a distance. A varied range of research has been undertaken within the field of gait recognition. However, in almost all circumstances subjects have been constrained to walk fronto-parallel to the camera with a single walking speed. In this thesis we show that gait has sufficient properties that allows us to exploit the structure of articulated leg motion within single view sequences, in order to remove the unknown subject pose and reconstruct the underlying gait signature, with no prior knowledge of the camera calibration. Articulated leg motion is approximately planar, since almost all of the perceived motion is contained within a single limb swing plane. The variation of motion out of this plane is subtle and negligible in comparison to this major plane of motion. Subsequently, we can model human motion by employing a cardboard person assumption. A subject's body and leg segments may be represented by repeating spatio-temporal motion patterns within a set of bilaterally symmetric limb planes. The static features of gait are defined as quantities that remain invariant over the full range of walking motions. In total, we have identified nine static features of articulated leg motion, corresponding to the fronto-parallel view of gait, that remain invariant to the differences in the mode of subject motion. These features are hypothetically unique to each individual, thus can be used as suitable parameters for biometric identification. We develop a stratified approach to linear trajectory gait reconstruction that uses the rigid bone lengths of planar articulated leg motion in order to reconstruct the fronto-parallel view of gait. Furthermore, subject motion commonly occurs within a fixed ground plane and is imaged by a static camera. In general, people tend to walk in straight lines with constant velocity. Imaged gait can then be split piecewise into natural segments of linear motion. If two or more sufficiently different imaged trajectories are available then the calibration of the camera can be determined. Subsequently, the total pattern of gait motion can be globally parameterised for all subjects within an image sequence. We present the details of a sparse method that computes the maximum likelihood estimate of this set of parameters, then conclude with a reconstruction error analysis corresponding to an example image sequence of subject motion

Temporal unpredictability detection of real-time video sequence

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