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We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can...
In this paper, we propose a novel method to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we first build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, we alternate between layout estimation and layout-based global registration processes in iterative...
In experimental fluid dynamics, the flow in a volume of fluid is observed by injecting high-contrast tracer particles and tracking them in multi-view video. Fluid dynamics researchers have developed variants of space-carving to reconstruct the 3D particle distribution at a given time-step, and then use relatively simple local matching to recover the motion over time. On the contrary, estimating the...
This paper proposes a pseudo-dolly-in video generation method that reproduces motion parallax by applying image reconstruction processing to multi-view videos. Since dolly-in video is taken by moving a camera forward to reproduce motion parallax, we can present a sense of immersion. However, at a sporting event in a large-scale space, moving a camera is difficult. Our research generates dolly-in video...
Nutrition is an important factor in the prevention and treatment of many diseases. Nutrition is a key factor for obesity, which is a risk factor for cardio-vascular diseases, type-2 diabetes and even cancer. Many non-communicable diseases require patients to keep track of nutrition accurately. Diabetes type-2 and type-1 require tracking carbohydrate intake accurately. However, keeping track and even...
We present a machine learning algorithm that takes as input a 2D RGB image and synthesizes a 4D RGBD light field (color and depth of the scene in each ray direction). For training, we introduce the largest public light field dataset, consisting of over 3300 plenoptic camera light fields of scenes containing flowers and plants. Our synthesis pipeline consists of a convolutional neural network (CNN)...
SAR Tomography (TomoSAR) regards multi-baseline observation data as array observation data and realizes three-dimensional imaging by applying direction of arrival (DOA) estimation processing. To improve angular resolution of the TomoSAR with suppressing ambiguity, many repeat-pass data are required. However, it is often difficult to increase the number of data because of the cost. In this paper, we...
In recent years, several new methods for missing data estimation have been developed. Real world datasets possess the properties of big data being volume, velocity and variety. With an increase in volume which includes sample size and dimensionality, existing imputation methods have become less effective and accurate. Much attention has been given to narrow Artificial Intelligence frameworks courtesy...
Highway tunnel environment is dim, with the interaction of multi-color light sources, which forms a light haze. Such a complicated scene makes the image blurred and arouses difficulties in the image enhancement. According to the law of haze imaging, a haze removal algorithm for nighttime scene is applied to enhance the tunnel image in this paper. First, the global atmospheric light is estimated as...
While recovery of hyperspectral signals from natural RGB images has been a recent subject of exploration, little to no consideration has been given to the camera response profiles used in the recovery process. In this paper we demonstrate that optimal selection of camera response filters may improve hyperspectral estimation accuracy by over 33%, emphasizing the importance of considering and selecting...
Human face exhibits an inherent hierarchy in its representations (i.e., holistic facial expressions can be encoded via a set of facial action units (AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown great results in unsupervised extraction of hierarchical latent representations from large amounts of image data, while being robust to noise and other undesired artifacts. Potentially,...
Analysis sparse representation (ASR) and synthesis sparse representation (SSR) are two representative approaches for sparsity-based image modeling. An image is described mainly by the non-zero coefficients in SSR, while is mainly characterized by the indices of zeros in ASR. To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional...
In this letter, an attitude estimation method is presented for space targets by using an inverse synthetic aperture radar (ISAR) image sequence. The line structures, like the boundaries of planar payloads, are extracted from the ISAR image sequence and associated from frame to frame. With the accommodation of the radar looking angle information from the trajectory, the threedimensional attitude of...
In this paper, we present a novel image reconstruction algorithm for positron emission tomography(PET). Almost all of existing reconstruction approaches assume that the measurement model for PET is linear equation with Gaussian white noise or energy-bounded noise, which only approximates the emission and detection of PET very roughly. In fact, the real situation is much more complicated than the one...
Shape reconstruction techniques using structured light have been widely researched and developed due to their robustness, high precision, and density. Because the techniques are based on decoding a pattern to find correspondences, it implicitly requires that the projected patterns be clearly captured by an image sensor, i.e., to avoid defocus and motion blur of the projected pattern. Although intensive...
Far-field pattern of an antenna above the earth is different from the characteristics in free space. We propose an estimation method of antenna far-field above the earth from near-field data in free space. The method reconstructs current distribution on a surface of an antenna from near-field data in free space and estimates the far-field pattern above the earth using the reconstructed current distribution...
In recent years, accurate location and characterization of damage has motivated the engineering community to develop several damage identification techniques. Many of the nondestructive evaluations and structural health monitoring techniques are based on the analysis of huge amount of data collected from acousto-ultrasonic sensors. Such analysis is typically a very time-consuming process. Therefore,...
As the world's largest radio telescope, the Square Kilometer Array (SKA) will provide radio interferometric data with unprecedented detail. Image reconstruction algorithms for radio interferometry are challenged to scale well with TeraByte image sizes never seen before. In this work, we investigate one such 3D image reconstruction algorithm known as MUFFIN (MUlti-Frequency image reconstruction For...
In this paper, a joint-domain dictionary learning-based error concealment approach is proposed. We extend the existing joint-domain dictionary learning methods to make more suitable scheme for error concealment. The main idea is to train an offline over-complete dictionary pair and learn two mapping matrices using two sets from the original and corrupted patches in a coupled manner, such that the...
Many man-made objects have intrinsic symmetries and Manhattan structure. By assuming an orthographic projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, which occur when the input is single-or multiple-image from the same category, e.g., multiple different cars. Specifically, analysis on the single image case...
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