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In the last several years, airborne topographic light detection and ranging (LiDAR) has emerged as an important remote-sensing technology supporting a wide variety of applications. In that time, researchers have conducted studies to determine the optimal sampling densities required to support their respective applications. This natural progression of experimentation and analysis has resulted in several recommended sampling density stratifications for LiDAR point cloud products. Recognizing the need for point clouds of varying sample densities provides at least two motivations for creating level of details (LOD) for high-resolution point clouds. First, from the perspective of LiDAR data consumers, there is a desire to use the coarsest sampling that supports the application to reduce procurement costs, storage constraints, and processing times. Second, from the perspective of LiDAR data providers, there is a desire to collect data once at the highest supported fidelity to minimize recollection costs and redundancy in data holdings. In this article, we present an approach for generating point cloud LODs by constraining samples to regular discrete lattices to optimize the coverage of the volumes represented by each sample. We compare our approach to the two most common point cloud sampling methods: random sampling and rectangular lattice sampling. We discuss two approaches for representing the point cloud LODs. Finally, we propose an extension of our sampling approach for processing single-photon and Geiger-mode avalanche photodiode (GmAPD) LiDAR. [ABSTRACT FROM AUTHOR]

During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is one of the core tasks in many applications such as autonomous driving and augmented reality. However, to train CNNs requires a considerable amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNNs on photo-realistic synthetic imagery with computer-generated annotations. Despite this, the domain mismatch between real images and the synthetic data hinders the models’ performance. Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation. The curriculum domain adaptation solves easy tasks first to infer necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban scenes have strong idiosyncrasies (e.g., the size and spatial relations of buildings, streets, cars, etc.). We then train a segmentation network, while regularizing its predictions in the target domain to follow those inferred properties. In experiments, our method outperforms the baselines on two datasets and three backbone networks. We also report extensive ablation studies about our approach. [ABSTRACT FROM AUTHOR]

Named entities such as people, locations, and organizations play a vital role in characterizing online content. They often reflect information of interest and are frequently used in search queries. Although named entities can be detected reliably from textual content, extracting relations among them is more challenging, yet useful in various applications (e.g., news recommending systems). In this paper, we present a novel model and system for learning semantic relations among named entities from collections of news articles. We model each named entity occurrence with sparse structured logistic regression, and consider the words (predictors) to be grouped based on background semantics. This sparse group LASSO approach forces the weights of word groups that do not influence the prediction towards zero. The resulting sparse structure is utilized for defining the type and strength of relations. Our unsupervised system yields a named entities’ network where each relation is typed, quantified, and characterized in context. These relations are the key to understanding news material over time and customizing newsfeeds for readers. Extensive evaluation of our system on articles from TIME magazine and BBC News shows that the learned relations correlate with static semantic relatedness measures like WLM, and capture the evolving relationships among named entities over time. [ABSTRACT FROM PUBLISHER]

Target detection of potential threats at night can be deployed on a costly infrared focal plane array with high resolution. Due to the compressibility of infrared image patches, the high resolution requirement could be reduced with target detection capability preserved. For this reason, a compressive midwave infrared imager (MWIR) with a low-resolution focal plane array has been developed. As the most probable coefficient indices of the support set of the infrared image patches could be learned from the training data, we develop stochastically trained least squares (STLS) for MWIR image reconstruction. Quadratic correlation filters (QCF) have been shown to be effective for target detection and there are several methods for designing a filter. Using the same measurement matrix as in STLS, we construct a compressed quadratic correlation filter (CQCF) employing filter designs for compressed infrared target detection. We apply CQCF to the U.S. Army Night Vision and Electronic Sensors Directorate dataset. Numerical simulations show that the recognition performance of our algorithm matches that of the standard full reconstruction methods, but at a fraction of the execution time. [ABSTRACT FROM PUBLISHER]

Numerous applied problems of two-dimensional (2-D) and 3-D imaging are formulated in continuous domain. They place great emphasis on obtaining and manipulating the Fourier transform in polar and spherical coordinates. However, the translation of continuum ideas with the discrete sampled data on a Cartesian grid is problematic. There exists no exact and fast solution to the problem of obtaining discrete Fourier transform for polar and spherical grids in the literature. In this paper, we develop exact algorithms to the above problem for 2-D and 3-D, which involve only 1-D equispaced fast Fourier transform with no interpolation or approximation at any stage. The result of the proposed approach leads to a fast solution with very high accuracy. We describe the computational procedure to obtain the solution in both 2-D and 3-D, which includes fast forward and inverse transforms. We find the nested multilevel matrix structure of the inverse process, and we propose a hybrid grid and use a preconditioned conjugate gradient method that exhibits a drastic improvement in the condition number. [ABSTRACT FROM PUBLISHER]

Template warping is a popular technique in vision-based 3D motion tracking and 3D pose estimation due to its flexibility of being applicable to monocular video sequences. However, the method suffers from two major limitations that hamper its successful use in practice. First, it requires the camera to be calibrated prior to applying the method. Second, it may fail to provide good results if the inter-frame displacements are too large. To overcome the first problem, we propose to estimate the unknown focal length of the camera from several initial frames by an iterative optimization process. To alleviate the second problem, we propose a tracking method based on combining complementary information provided by dense optical flow and tracked scale-invariant feature transform (SIFT) features. While optical flow is good for small displacements and provides accurate local information, tracked SIFT features are better at handling larger displacements or global transformations. To combine these two pieces of complementary information, we introduce a forgetting factor to bootstrap the 3D pose estimates provided by SIFT features, and refine the final results using optical flow. Experiments are performed on three public databases, i.e., the Biwi Head Pose dataset, the BU dataset, and the McGill Faces datasets. The results illustrate that the proposed solution provides more accurate results than baseline methods that rely solely on either template warping or SIFT features. In addition, the approach can be applied in a larger variety of scenarios, due to circumventing the need for camera calibration, thus providing a more flexible solution to the problem than existing methods. [ABSTRACT FROM AUTHOR]

Goal: In refractive surgery, astigmatism-correcting treatments are generally planned with the aid of some diagnostic imaging device and often executed by some computer guided laser system. In the transition from sitting down at a diagnostic device to lying down beneath a laser system, a phenomenon known as cyclotorsion (rotation of the eye within the socket) occurs. Hence, registration between lasers and diagnostic devices is necessary. The purpose of this paper is to present a newly developed algorithm that accomplishes robust registration using images of the patient's iris in the context of laser-assisted cataract surgery, and evaluate its efficacy. Methods: The proposed iris registration algorithm was tested on real cataract patient images obtained from commercially available devices. Accuracy was measured against manual registrations performed by trained humans. Conservative bounds on success and failure rates were computed using novel statistical methods. Results: The algorithm better approximated the cyclotorsion as averaged over manual measurements from three trained humans than any of the three individual humans, with a 95\% tolerance interval of \pm 1.36^{\circ } . In addition, a success rate {\geq}99.0\%$ was observed for an acceptance threshold setting that allowed for a false registration rate {\leq}1.00*10^{-3}\%. Conclusion: The proposed iris registration algorithm accurately and consistently compensates for cyclotorsion in laser-assisted cataract surgery. Significance: This paper details the first algorithm to be used for iris registration in laser-assisted cataract surgery. Enabling surgeons to make use of this algorithm in real surgeries is expected to have a significant impact on astigmatism management in cataract surgery. [ABSTRACT FROM PUBLISHER]