Multi-view passive 3D face acquisition device

Approaches to acquisition of 3D facial data include laser scanners, structured light devices and (passive) stereo vision. The laser scanner and structured light methods allow accurate reconstruction of the 3D surface but strong light is projected on the faces of subjects. Passive stereo vision based approaches do not require strong light to be projected, however, it is hard to obtain comparable accuracy and robustness of the surface reconstruction. In this paper a passive multiple view approach using 5 cameras in a ’+’ configuration is proposed that significantly increases robustness and accuracy relative to traditional stereo vision approaches. The normalised cross correlations of all 5 views are combined using direct projection of points instead of the traditionally used rectified images. Also, errors caused by different perspective deformation of the surface in the different views are reduced by using an iterative reconstruction technique where the depth estimation of the previous iteration is used to warp the windows of the normalised cross correlation for the different views

A Robust Quasi-dense Matching Approach for Underwater Images

While different techniques for finding dense correspondences in images taken in air have achieved significant success, application of these techniques to underwater imagery still presents a serious challenge, especially in the case of “monocular stereo” when images constituting a stereo pair are acquired asynchronously. This is generally because of the poor image quality which is inherent to imaging in aquatic environments (blurriness, range-dependent brightness and color variations, time-varying water column disturbances, etc.). The goal of this research is to develop a technique resulting in maximal number of successful matches (conjugate points) in two overlapping images. We propose a quasi-dense matching approach which works reliably for underwater imagery. The proposed approach starts with a sparse set of highly robust matches (seeds) and expands pair-wise matches into their neighborhoods. The Adaptive Least Square Matching (ALSM) is used during the search process to establish new matches to increase the robustness of the solution and avoid mismatches. Experiments on a typical underwater image dataset demonstrate promising results

Development of a Computer Vision-Based Three-Dimensional Reconstruction Method for Volume-Change Measurement of Unsaturated Soils during Triaxial Testing

Problems associated with unsaturated soils are ubiquitous in the U.S., where expansive and collapsible soils are some of the most widely distributed and costly geologic hazards. Solving these widespread geohazards requires a fundamental understanding of the constitutive behavior of unsaturated soils. In the past six decades, the suction-controlled triaxial test has been established as a standard approach to characterizing constitutive behavior for unsaturated soils. However, this type of test requires costly test equipment and time-consuming testing processes. To overcome these limitations, a photogrammetry-based method has been developed recently to measure the global and localized volume-changes of unsaturated soils during triaxial test. However, this method relies on software to detect coded targets, which often requires tedious manual correction of incorrectly coded target detection information. To address the limitation of the photogrammetry-based method, this study developed a photogrammetric computer vision-based approach for automatic target recognition and 3D reconstruction for volume-changes measurement of unsaturated soils in triaxial tests. Deep learning method was used to improve the accuracy and efficiency of coded target recognition. A photogrammetric computer vision method and ray tracing technique were then developed and validated to reconstruct the three-dimensional models of soil specimen

PVR: Patch-to-Volume Reconstruction for Large Area Motion Correction of Fetal MRI

In this paper we present a novel method for the correction of motion artifacts that are present in fetal Magnetic Resonance Imaging (MRI) scans of the whole uterus. Contrary to current slice-to-volume registration (SVR) methods, requiring an inflexible anatomical enclosure of a single investigated organ, the proposed patch-to-volume reconstruction (PVR) approach is able to reconstruct a large field of view of non-rigidly deforming structures. It relaxes rigid motion assumptions by introducing a specific amount of redundant information that is exploited with parallelized patch-wise optimization, super-resolution, and automatic outlier rejection. We further describe and provide an efficient parallel implementation of PVR allowing its execution within reasonable time on commercially available graphics processing units (GPU), enabling its use in the clinical practice. We evaluate PVR's computational overhead compared to standard methods and observe improved reconstruction accuracy in presence of affine motion artifacts of approximately 30% compared to conventional SVR in synthetic experiments. Furthermore, we have evaluated our method qualitatively and quantitatively on real fetal MRI data subject to maternal breathing and sudden fetal movements. We evaluate peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), and cross correlation (CC) with respect to the originally acquired data and provide a method for visual inspection of reconstruction uncertainty. With these experiments we demonstrate successful application of PVR motion compensation to the whole uterus, the human fetus, and the human placenta.Comment: 10 pages, 13 figures, submitted to IEEE Transactions on Medical Imaging. v2: wadded funders acknowledgements to preprin

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance

The Causes of Political Integration: An Application to School Districts

This paper examines the forces behind political integration through the lens of school district consolidations, which reduced the number of school districts in the United States from around 130,000 in 1930 to under 15,000 at present. Despite this large observed decline, many districts resisted consolidation before ultimately merging and others never merged, choosing to remain at enrollment levels that nearly any education cost function would deem inefficiently small. Why do some districts voluntarily integrate while others remain small, and how do those districts that do merge choose with which of their neighbors to do so? In addressing these questions, we empirically examine the role of potential economies and diseconomies of scale, heterogeneity between merger partners, and the role of state governments. We first develop a simulation-based estimator that is rooted in the economics of matching and thus accounts for three important features of typical merger protocol: two-sided decision making, multiple potential partners, and spatial interdependence. We then apply this methodology to a wave of school district mergers in the state of Iowa during the 1990s. Our results highlight the importance of economies of scale, diseconomies of scale, state financial incentives for consolidation, and a variety of heterogeneity measures.