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Plant epidermal cell walls maintain the mechanical integrity of plants and restrict organ growth. Mechanical analyses can give insights into wall structure and are inputs for mechanobiology models of plant growth. To better understand the intrinsic mechanics of epidermal cell walls and how they may accommodate large deformations during growth, we analyzed a geometrically simple material, onion epidermal strips consisting of only the outer (periclinal) cell wall, ~7 μm thick. With uniaxial stretching by >40%, the wall showed complex three-phase stress–strain responses while cyclic stretching revealed reversible and irreversible deformations and elastic hysteresis. Stretching at varying strain rates and temperatures indicated the wall behaved more like a network of flexible cellulose fibers capable of sliding than a viscoelastic composite with pectin viscosity. We developed an analytic framework to quantify nonlinear wall mechanics in terms of stiffness, deformation, and energy dissipation, finding that the wall stretches by combined elastic and plastic deformation without compromising its stiffness. We also analyzed mechanical changes in slightly dehydrated walls. Their extension became stiffer and more irreversible, highlighting the influence of water on cellulose stiffness and sliding. This study offers insights into the structure and deformation modes of primary cell walls and presents a framework that is also applicable to tissues and whole organs. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the relationship between perceived teacher support and learning engagement and exploring the mediation role played by technology acceptance and learning motivation. It adopted a structural equation modeling (SEM) approach, with sampling 467 students from four middle schools in eastern China. The research findings showed that perceived teacher support is significantly associated with learning engagement. Learning motivation plays a mediating role in the relationship between perceived teacher support and learning engagement. There is the chain mediating effect of technology acceptance and learning motivation on the relationship between perceived teacher support and learning engagement. All of these are of great importance for the teachers in the middle schools, as they help to increase students’ engagement with learning activities considering the background of the deep integration of information technology and education teaching. [ABSTRACT FROM AUTHOR]

The classical aerodynamic derivative model is widely used in flight dynamics, but its application is extremely limited in cases with complicated nonlinear flows, especially at high angles of attack. A modified nonlinear aerodynamic derivative model for predicting unsteady aerodynamic forces and moments at a high angle of attack is developed in this study. We first extend the higher-order terms to describe the nonlinear characteristics and then introduce three more influence parameters, the initial angle of attack, the reduced frequency, and the oscillation amplitude, to correct the constant aerodynamic derivative terms that have higher-order polynomials for these values. The improved nonlinear aerodynamic derivative model was validated by using the NACA 0015 airfoil and the F-18 model. The results show that the improved model has a higher prediction ability at high angles of attack and has the ability to predict the aerodynamic characteristics of other unknown states based on known unsteady aerodynamic data, such as the initial angle of attack, reduced frequency, and oscillation amplitude. [ABSTRACT FROM AUTHOR]

Introduction: This study was conducted to compare the clinical effects of unilateral pedicle screw fixation and interbody fusion with PEEK cage (UPSFC) and standalone expandable fusion cage (SAEFC) on unilateral lumbar disc herniation.Material and Methods: From September 2011 to July 2014, a respective investigation was performed on 130 lumbar disc herniation patients treated with SAEFC or UPSFC. The hospital stay, operating time, blood loss, Japanese orthopaedic association scores (JOA), and visual analogue score (VAS) in the two groups were compared using Student's t-test.Results: The average of follow-up time was 25.6 ±6.4 and 25.2 ±5.8 months, respectively. No significant difference in the postoperative hospitalizsation, intraoperative blood loss, operative time, and postoperative fusion rate was detected between the two groups. VAS score in the UPSFC group was significantly lower than in the SAEFC group at 6 and 12 months after operation (p = 0.014, p = 0.004). X-ray images indicated that the subsidence rate was 8.1% (5/62) in the SAEFC group, while no subsidence was detected in UPSFC group 12 month after operation.Conclusions: Both SAEFC and UPSFC are effective techniques. UPSFC may be a better choice for patients with lumbar disc herniation and unilateral limb symptoms of nerve root in view of the advantages of better low back pain relief and low subsidence rate. [ABSTRACT FROM AUTHOR]

Robust extraction of consensus sets from noisy data is a fundamental problem in robot vision. Existing multimodel estimation algorithms have shown success on large consensus sets estimations. One remaining challenge is to extract small consensus sets in cluttered multimodel data set. In this article, we present an effective multimodel extraction method to solve this challenge. Our technique is based on smallest consensus set random sampling, which we prove can guarantee to extract all consensus sets larger than the smallest set from input data. We then develop an efficient model competition scheme that iteratively removes redundant and incorrect model samplings. Extensive experiments on both synthetic data and real data with high percentage of outliers and multimodel intersections demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

In the recent big data era, massive spatial related data are continuously generated and scrambled from various sources. Acquiring accurate geographic information is also urgently demanded. How to accurately retrieve desired geographic information has become the prominent issue, needing to be resolved in high priority. The key technologies in geographic information retrieval are modeling document footprints and ranking documents based on their similarity evaluation. The traditional spatial similarity evaluation methods are mainly performed using a MBR (Minimum Bounding Rectangle) footprint model. However, due to its nature of simplification and roughness, the results of traditional methods tend to be isotropic and space-redundant. In this paper, a new model that constructs the footprints in the form of point-sets is presented. The point-set-based footprint coincides the nature of place names in web pages, so it is redundancy-free, consistent, accurate, and anisotropic to describe the spatial extents of documents, and can handle multi-scale geographic information. The corresponding spatial ranking method is also presented based on the point-set-based model. The new similarity evaluation algorithm of this method firstly measures multiple distances for the spatial proximity across different scales, and then combines the frequency of place names to improve the accuracy and precision. The experimental results show that the proposed method outperforms the traditional methods with higher accuracies under different searching scenarios. [ABSTRACT FROM AUTHOR]

Heavy occlusions in cluttered scenes impose significant challenges to many computer vision applications. Recent light field imaging systems provide new see-through capabilities through synthetic aperture imaging (SAI) to overcome the occlusion problem. Existing synthetic aperture imaging methods, however, emulate focusing at a specific depth layer, but are incapable of producing an all-in-focus see-through image. Alternative in-painting algorithms can generate visually-plausible results, but cannot guarantee the correctness of the results. In this paper, we present a novel depth-free all-in-focus SAI technique based on light field visibility analysis. Specifically, we partition the scene into multiple visibility layers to directly deal with layer-wise occlusion and apply an optimization framework to propagate the visibility information between multiple layers. On each layer, visibility and optimal focus depth estimation is formulated as a multiple-label energy minimization problem. The layer-wise energy integrates all of the visibility masks from its previous layers, multi-view intensity consistency and depth smoothness constraint together. We compare our method with state-of-the-art solutions, and extensive experimental results demonstrate the effectiveness and superiority of our approach. [ABSTRACT FROM AUTHOR]

Diverse scene stitching is a challenging task in aerial video surveillance. This paper presents a hybrid stitching method based on the observation that aerial videos captured in real surveillance settings are neither totally ordered nor completely unordered. Often, human operators apply continuous monitoring of the drone to revisit the same area of interest. This monitoring mechanism yields to multiple short, successive video clips that overlap in either time or space. We exploit this property and treat the aerial image stitching problem as temporal sequential grouping and spatial cross-group retrieval. We develop an effective graph-based framework that can robustly conduct the grouping, retrieval and stitching tasks. To evaluate the proposed approach, we experiment on the large-scale VIRATaerial surveillance dataset, which is challenging for its heterogeneity in image quality and diversity of the scene. Quantitative and qualitative comparisons with state-of-the-art algorithms show the efficiency and robustness of our technique. [ABSTRACT FROM AUTHOR]

The highly efficient and robust stitching of aerial video captured by unmanned aerial vehicles (UAVs) is a challenging problem in the field of robot vision. Existing commercial image stitching systems have seen success with offline stitching tasks, but they cannot guarantee high-speed performance when dealing with online aerial video sequences. In this paper, we present a novel system which has an unique ability to stitch high-frame rate aerial video at a speed of 150 frames per second (FPS). In addition, rather than using a high-speed vision platform such as FPGA or CUDA, our system is running on a normal personal computer. To achieve this, after the careful comparison of the existing invariant features, we choose the FAST corner and binary descriptor for efficient feature extraction and representation, and present a spatial and temporal coherent filter to fuse the UAV motion information into the feature matching. The proposed filter can remove the majority of feature correspondence outliers and significantly increase the speed of robust feature matching by up to 20 times. To achieve a balance between robustness and efficiency, a dynamic key frame-based stitching framework is used to reduce the accumulation errors. Extensive experiments on challenging UAV datasets demonstrate that our approach can break through the speed limitation and generate an accurate stitching image for aerial video stitching tasks. [ABSTRACT FROM AUTHOR]

The depth of field (DoF) effect is a useful tool in photography and cinematography because of its aesthetic value. However, capturing and displaying dynamic DoF effect were until recently a quality unique to expensive and bulky movie cameras. A computational approach to generate realistic DoF effects for mobile devices such as tablets is proposed. We first calibrate the rear-facing stereo cameras and rectify the stereo image pairs through FCam API, then generate a low-res disparity map using graph cuts stereo matching and subsequently upsample it via joint bilateral upsampling. Next, we generate a synthetic light field by warping the raw color image to nearby viewpoints, according to the corresponding values in the upsampled high-resolution disparity map. Finally, we render dynamic DoF effect on the tablet screen with light field rendering. The user can easily capture and generate desired DoF effects with arbitrary aperture sizes or focal depths using the tablet only, with no additional hardware or software required. The system has been examined in a variety of environments with satisfactory results, according to the subjective evaluation tests. [ABSTRACT FROM AUTHOR]

The figures included in many of the biomedical publications play an important role in understanding the biological experiments and facts described within. Recent studies have shown that it is possible to integrate the information that is extracted from figures in classical document classification and retrieval tasks in order to improve their accuracy. One important observation about the figures included in biomedical publications is that they are often composed of multiple subfigures or panels, each describing different methodologies or results. The use of these multimodal figures is a common practice in bioscience, as experimental results are graphically validated via multiple methodologies or procedures. Thus, for a better use of multimodal figures in document classification or retrieval tasks, as well as for providing the evidence source for derived assertions, it is important to automatically segment multimodal figures into subfigures and panels. This is a challenging task, however, as different panels can contain similar objects (i.e., barcharts and linecharts) with multiple layouts. Also, certain types of biomedical figures are text-heavy (e.g., DNA sequences and protein sequences images) and they differ from traditional images. As a result, classical image segmentation techniques based on low-level image features, such as edges or color, are not directly applicable to robustly partition multimodal figures into single modal panels. In this paper, we describe a robust solution for automatically identifying and segmenting unimodal panels from a multimodal figure. Our framework starts by robustly harvesting figure-caption pairs from biomedical articles. We base our approach on the observation that the document layout can be used to identify encoded figures and figure boundaries within PDF files. Taking into consideration the document layout allows us to correctly extract figures from the PDF document and associate their corresponding caption. We combine pixel-level representations of the extracted images with information gathered from their corresponding captions to estimate the number of panels in the figure. Thus, our approach simultaneously identifies the number of panels and the layout of figures. In order to evaluate the approach described here, we applied our system on documents containing proteinprotein interactions (PPIs) and compared the results against a gold standard that was annotated by biologists. Experimental results showed that our automatic figure segmentation approach surpasses pure caption-based and image-based approaches, achieving a 96.64% accuracy. To allow for efficient retrieval of information, as well as to provide the basis for integration into document classification and retrieval systems among other, we further developed a web-based interface that lets users easily retrieve panels containing the terms specified in the user queries. [ABSTRACT FROM AUTHOR]

We present a novel image-based technique for modeling complex unfoliaged trees. Existing tree modeling tools either require capturing a large number of views for dense 3D reconstruction or rely on user inputs and botanic rules to synthesize natural-looking tree geometry. In this paper, we focus on faithfully recovering real instead of realistically-looking tree geometry from a sparse set of images. Our solution directly integrates 2D/3D tree topology as shape priors into the modeling process. For each input view, we first estimate a 2D skeleton graph from its matte image and then find a 2D skeleton tree from the graph by imposing tree topology. We develop a simple but effective technique for computing the optimal 3D skeleton tree most consistent with the 2D skeletons. For each edge in the 3D skeleton tree, we further apply volumetric reconstruction to recover its corresponding curved branch. Finally, we use piecewise cylinders to approximate each branch from the volumetric results. We demonstrate our framework on a variety of trees to illustrate the robustness and usefulness of our technique. [ABSTRACT FROM AUTHOR]

We present a new algorithm for efficient rendering of high-quality depth-of-field (DoF) effects. We start with a single rasterized view (reference view) of the scene, and sample the light field by warping the reference view to nearby views. We implement the algorithm using NVIDIA's CUDA to achieve parallel processing, and exploit the atomic operations to resolve visibility when multiple pixels warp to the same image location. We then directly synthesize DoF effects from the sampled light field. To reduce aliasing artifacts, we propose an image-space filtering technique that compensates for spatial undersampling using MIP mapping. The main advantages of our algorithm are its simplicity and generality. We demonstrate interactive rendering of DoF effects in several complex scenes. Compared to existing methods, ours does not require ray tracing and hence scales well with scene complexity. [ABSTRACT FROM AUTHOR]

Dietary fat accumulates in lipid droplets or endolysosomal compartments that undergo selective expansion under normal or pathophysiological conditions. We find that genetic defects in a peroxisomal β-oxidation pathway cause size expansion in lipid droplets that are distinct from the lysosome-related organelles in Caenorhabditis elegans. Expansion of lipid droplets is accompanied by an increase in triglycerides (TAG) that are resistant to fasting- or TAG lipase-triggered lipolysis. Nevertheless, in mutant animals, a diet poor in vaccenic acid reduced the TAG level and lipid droplet size. Our results implicate peroxisomal dysfunction in pathologic lipid droplet expansion in animals and illustrate how dietary factors modulate the phenotype of such genetic defects. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method for robustly determining the vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for vignetting estimation. Within each image region, our method capitalizes on the frequency characteristics and physical properties of vignetting to distinguish it from other sources of intensity variation. Rejection of outlier pixels is applied to improve the robustness of vignetting estimation. Comprehensive experiments demonstrate the effectiveness of this technique on a broad range of images with both simulated and natural vignetting effects. Causes of failures using the proposed algorithm are also analyzed. [ABSTRACT FROM AUTHOR]

Abstract  We present a novel framework for real-time multi-perspective rendering. While most existing approaches are based on ray-tracing, we present an alternative approach by emulating multi-perspective rasterization on the classical perspective graphics pipeline. To render a general multi-perspective camera, we first decompose the camera into piecewise linear primitive cameras called the general linear cameras or GLCs. We derive the closed-form projection equations for GLCs and show how to rasterize triangles onto GLCs via a two-pass rendering algorithm. In the first pass, we compute the GLC projection coefficients of each scene triangle using a vertex shader. The linear raster on the graphics hardware then interpolates these coefficients at each pixel. Finally, we use these interpolated coefficients to compute the projected pixel coordinates using a fragment shader. In the second pass, we move the pixels to their actual projected positions. To avoid holes, we treat neighboring pixels as triangles and re-render them onto the GLC image plane. We demonstrate our real-time multi-perspective rendering framework in a wide range of applications including synthesizing panoramic and omnidirectional views, rendering reflections on curved mirrors, and creating multi-perspective faux animations. Compared with the GPU-based ray tracing methods, our rasterization approach scales better with scene complexity and it can render scenes with a large number of triangles at interactive frame rates. [ABSTRACT FROM AUTHOR]

Line-art illustrations are effective tools for conveying shapes and shading of complex objects. We present a set of new algorithms to render line-art illustrations of dynamic and specular (reflective and refractive) surfaces. We first introduce a real-time principal direction estimation algorithm to determine the line stroke directions on dynamic opaque objects using neighboring normal ray triplets. To render reflections or refractions in a line-art style, we develop a stroke direction propagation algorithm by using multi-perspective projections to propagate the stroke directions from the nearby opaque objects onto specular surfaces. Finally, we present an image-space stroke mapping method to draw line strokes using the computed or propagated stroke directions. We implement these algorithms using a GPU and demonstrate real-time illustrations of scenes with dynamic and specular 3D models in line-art styles. [ABSTRACT FROM AUTHOR]

We present an interactive GPU-based algorithm for accurately rendering high-quality, dynamic glossy reflection effects from both HDR environment maps and local scene objects. Our method uses hardware rasterization to produce primary pixels, and GPU-based BRDF importance sampling [ CK07 ] to quickly generate reflected rays. We utilize a fast GPU ray tracer proposed by Carr et al. [ CHCH06 ] to compute reflection hits. Our main contribution is an adaptive level-of-detail (LOD) control algorithm that greatly improves ray tracing performance during reflection shading. Specifically, we use the solid angle represented by each reflected ray to adaptively pick the level of termination in the BVH traversal step during ray tracing. This leads to 2 ∼ 3x speedup over an unmodified implementation of [ CHCH06 ]. Based on the same solid angle measure, we derive a texture filtering formula to reduce reflection aliasing artifacts, taking advantage of hardware MIP mapping. This extends the filtering algorithm presented in [ CK07 ] from environment mapping to local scene reflection. Using our algorithm, we demonstrate interactive rendering rates for several scenes featuring dynamic lighting and material changes, spatially varying BRDF parameters, and rigid-body object movement. [ABSTRACT FROM AUTHOR]

The article presents information on a method for capturing geometric features of realworld scenes, multiflash imaging, which relies on a simple capture setup modification. The system might conceivably be packaged into a portable, self-contained device. Sharp discontinuities in a depth map, or depth edges, are extremely useful 2.5D entities. The ability to localize and highlight depth discontinuities makes it possible to produce stylized photography and videos, which are useful in technical applications such as medical imaging, and nonphotorealistic rendering. In addition, depth edges are important low-level features for many computer vision tasks, such as visual recognition. When a rich 3D model of a scene is available, identifying and localizing depth discontinuities is a relatively well-understood task. Extending this approach to real scenes by first capturing 3D models, however, remains difficult. Multiflash imaging method bypasses 3D geometry acquisition and directly acquires depth edges from images. In the place of expensive, elaborate equipment for geometry acquisition, a camera with multiple strategically positioned flashes is used. Instead of having to estimate the full 3D coordinates of points in the scene and then look for depth discontinuities, our technique reduces the general 3D problem of depth edge recovery to one of 2D intensity edge detection.

We present a non-photorealistic rendering approach to capture and convey shape features of real-world scenes. We use a camera with multiple flashes that are strategically positioned to cast shadows along depth discontinuities in the scene. The projective-geometric relationship of the camera-flash setup is then exploited to detect depth discontinuities and distinguish them from intensity edges due to material discontinuities. We introduce depiction methods that utilize the detected edge features to generate stylized static and animated images. We can highlight the detected features, suppress unnecessary details or combine features from multiple images. The resulting images more clearly convey the 3D structure of the imaged scenes. We take a very different approach to capturing geometric features of a scene than traditional approaches that require reconstructing a 3D model. This results in a method that is both surprisingly simple and computationally efficient. The entire hardware/software setup can conceivably be packaged into a self-contained device no larger than existing digital cameras. [ABSTRACT FROM AUTHOR]

Endothelial cells can function differently in vitro and in vivo; however, the degree of microenvironmental modulation in vivo remains unknown at the molecular level largely because of analytical limitations. We use multidimensional protein identification technology (MudPIT) to identify 450 proteins (with three or more spectra) in luminal endothelial cell plasma membranes isolated from rat lungs and from cultured rat lung microvascular endothelial cells. Forty-one percent of proteins expressed in vivo are not detected in vitro. Statistical analysis measuring reproducibility reveals that seven to ten MudPIT measurements are necessary to achieve =95% confidence of analytical completeness with current ion trap equipment. Large-scale mapping of the proteome of vascular endothelial cell surface in vivo, as demonstrated here, is advisable because distinct protein expression is apparently regulated by the tissue microenvironment that cannot yet be duplicated in standard cell culture. [ABSTRACT FROM AUTHOR]

The performance of CdZnTe X/γ-ray detectors is strongly affected by the electric field distribution in terms of charge transport and charge collection. Factors which determine the electric field distribution are not only electric contact, but also intrinsic defects, especially grown-in twin boundaries. Here, the electric field distribution around twin boundaries is investigated in a CdZnTe bicrystal detector with a {111}–{111} twin plane using the Pockels electro-optic effect. The results of laser beam induced current pulses are also obtained by the transient current technique, and we discuss the influence of the twin boundary on the electric field evolution. These studies reveal a significant distortion of the electric field, which is attributed to the buildup of space charges at twin boundaries. Also, the position of these space charge regions depends on the polarity of the detector bias. An energy band model based on the formation of an n–n+–n junction across the twin boundary has been established to explain the observed results. [ABSTRACT FROM AUTHOR]

We present a novel stereo imaging technique called dual-focus stereo imaging or DFSI. DFSI uses a pair of images captured from different viewpoints and at different foci, but with identical wide aperture size. Each image in an DFSI pair exhibits different defocus blur, and the two images form a defocused stereo pair. To model defocus blur, we introduce a defocus kernel map (DKM) that computes the size of the blur disk at each pixel. We derive a novel disparity defocus constraintfor computing the DKM in DFSI, and integrate DKM estimation with disparity map estimation to simultaneously recover both maps. We show that the recovered DKMs provide useful guidance for segmenting the in-focus regions. We demonstrate using DFSI in a variety of imaging applications, including low-light imaging, automatic defocus matting, and multifocus photomontage. [ABSTRACT FROM AUTHOR]