Influence of Pitch Angle Errors in 3D Scene Reconstruction Based on U-V Disparity: A Sensitivity Study

U-V disparity is a technique that is commonly used to detect obstacles in 3D scenes, modeling them as a set of vertical planes. In this paper, the authors describe the general lines of a method based on this technique for fully reconstructing 3D scenes, and conduct an analytical study of its performance and sensitivity to errors in the pitch angle of the stereoscopic vision system. The equations of the planes calculated for a given error in this angle yield the deviation with respect to the ideal planes (with a zero error in the angle) for a large test set consisting of planes with different orientations, which is represented graphically to analyze the method’s qualitative and quantitative performance. The relationship between the deviation of the planes and the error in the pitch angle is observed to be linear. Two major conclusions are drawn from this study: first, that the deviation between the calculated and ideal planes is always less than or equal to the error considered in the pitch angle; and second, that even though in some cases the deviation of the plane is zero or very small, the probability that a plane of the scene deviates from the ideal by the greatest amount possible, which matches the error in the pitch angle, is very high.

Quantitative Performance Analysis of Flexible Ink-jet Printed Low-cost DNA Sensors Based on a CNT Network

We report the design, fabrication and quantitative performance analysis of a low-cost, flexible carbon nanotube (CNT) network-based deoxyribonucleic acid (DNA) sensor. These sensors comprise an array of ink-jet printed silver (Ag) electrodes on a transparent polyethylene terephthalate (PET) flexible substrate, where a CNT network acts as a sensing layer. The DNA hybridization is studied by immobilizing single-stranded DNA (ssDNA) probes on the CNT surface; these probes recognize their complementary DNA target. Further, we have carried out a quantitative performance analysis of the flexible CNT biosensors using the analytic hierarchy process (AHP). We have identified various influencing factors and sub-factors (performance indicators), and quantified the performance of the flexible CNT biosensors in different measured states (before bending, during bending and after bending). Additionally, the noise and other external factors contributing to the measured real signal have been quantified. The interpretation of the overall outcome will enable the improvement of the performance of flexible biosensors fabricated through large-scale manufacturing for possible commercialization.

Structural Investigation of Export Assistance and Performance of Handloom Exporters

The previous literature highlighted the importance of export assistance to uplift export activities of small and medium scale enterprises (SMEs). However, those studies could not test the feasibility of introduced export assistance for the targeted population. This study focuses on export assistance provided by the government institutions to the handloom exporters and its relationship with their perceived quantitative export performance. A detailed literature review of the existing export assistance programmes led to the categorization in four aspects, that is, financial support, informational support, facilitating support, and education/training support. A model is proposed based on identified variables depicting the relationship between export assistance and handloom exporters’ perceived quantitative export performance. In order to develop the model, first-order confirmatory factor analysis (CFA) was initially applied for validity measures. Subsequently, second-order CFA was applied due to the presence of multicollinearity among the variables due to the undistinguishable response of the chosen sample units towards export assistance. Thus, the study investigated the relationship of each of the four identified export assistance elements with the quantitative performance of Handloom exporters.

Mesenteric cyst detection and segmentation by multiple K-means clustering and iterative Gaussian filtering

<span lang="EN-US">In this article a fully automated machine-vision technique for the detection and segmentation of mesenteric cysts in computed tomography (CT) images of the abdominal space is presented. The proposed technique involves clustering, filtering, morphological operations and evaluation processes to detect and segment mesenteric cysts in the abdomen regardless of their texture variation and location with respect to other surrounding abdominal organs. The technique is comprised of various processing phases, which include K-means clustering, iterative Gaussian filtering, and an evaluation of the segmented regions using area-normalized histograms and Euclidean distances. The technique was tested using 65 different abdominal CT scan images. The results showed that the technique was able to detect and segment mesenteric cysts and achieved 99.31%, 98.44%, 99.84%, 98.86% and 99.63% for precision, recall, specificity, dice score coefficient and accuracy respectively as quantitative performance measures which indicate very high segmentation accuracy.</span>

Harmonization of PET image reconstruction parameters in simultaneous PET/MRI

Objective Simultaneous PET/MRIs vary in their quantitative PET performance due to inherent differences in the physical systems and differences in the image reconstruction implementation. This variability in quantitative accuracy confounds the ability to meaningfully combine and compare data across scanners. In this work, we define image reconstruction parameters that lead to comparable contrast recovery curves across simultaneous PET/MRI systems. Method The NEMA NU-2 image quality phantom was imaged on one GE Signa and on one Siemens mMR PET/MRI scanner. The phantom was imaged at 9.7:1 contrast with standard spheres (diameter 10, 13, 17, 22, 28, 37 mm) and with custom spheres (diameter: 8.5, 11.5, 15, 25, 32.5, 44 mm) using a standardized methodology. Analysis was performed on a 30 min listmode data acquisition and on 6 realizations of 5 min from the listmode data. Images were reconstructed with the manufacturer provided iterative image reconstruction algorithms with and without point spread function (PSF) modeling. For both scanners, a post-reconstruction Gaussian filter of 3–7 mm in steps of 1 mm was applied. Attenuation correction was provided from a scaled computed tomography (CT) image of the phantom registered to the MR-based attenuation images and verified to align on the non-attenuation corrected PET images. For each of these image reconstruction parameter sets, contrast recovery coefficients (CRCs) were determined for the SUVmean, SUVmax and SUVpeak for each sphere. A hybrid metric combining the root-mean-squared discrepancy (RMSD) and the absolute CRC values was used to simultaneously optimize for best match in CRC between the two scanners while simultaneously weighting toward higher resolution reconstructions. The image reconstruction parameter set was identified as the best candidate reconstruction for each vendor for harmonized PET image reconstruction. Results The range of clinically relevant image reconstruction parameters demonstrated widely different quantitative performance across cameras. The best match of CRC curves was obtained at the lowest RMSD values with: for CRCmean, 2 iterations-7 mm filter on the GE Signa and 4 iterations-6 mm filter on the Siemens mMR, for CRCmax, 4 iterations-6 mm filter on the GE Signa, 4 iterations-5 mm filter on the Siemens mMR and for CRCpeak, 4 iterations-7 mm filter with PSF on the GE Signa and 4 iterations-7 mm filter on the Siemens mMR. Over all reconstructions, the RMSD between CRCs was 1.8%, 3.6% and 2.9% for CRC mean, max and peak, respectively. The solution of 2 iterations-3 mm on the GE Signa and 4 iterations-3 mm on Siemens mMR, both with PSF, led to simultaneous harmonization and with high CRC and low RMSD for CRC mean, max and peak with RMSD values of 2.8%, 5.8% and 3.2%, respectively. Conclusions For two commercially available PET/MRI scanners, user-selectable parameters that control iterative updates, image smoothing and PSF modeling provide a range of contrast recovery curves that allow harmonization in harmonization strategies of optimal match in CRC or high CRC values. This work demonstrates that nearly identical CRC curves can be obtained on different commercially available scanners by selecting appropriate image reconstruction parameters.

Construction of Evaluation Model of Surface Soil Erodibility Factors in Black Soil Area

The increasing soil erosion in black soil area has caused widespread concern from all walks of life. Based on this background, the evaluation model of surface soil erodibility factor in black soil area is studied and constructed. The data of erosion gully is from the general survey data of surface soil erosion gully in black soil area. After quantifying the morphological characteristics of the data, the vector data of erosion gully are gridded by fractal theory. The number of non-empty grids is calculated by the attribute query function of ArcGIS, and the pixel size is transformed in turn to obtain different coverage grids and corresponding fractal parameters. The surface soil runoff and soil erosion process in black soil area are simulated by PESERA model. To build the surface soil erosion model of black soil area, in the process of building the user-defined model, it needs to carefully select the parameters used for modeling, and it needs to consider all the factors that may play a role in the whole process of soil erosion. The factors of surface soil erodibility in black soil area are analyzed, including spatial distribution characteristics of soil erodibility’s K value, semivariance function analysis of soil erodibility’s K value, and spatial distribution characteristics analysis of soil erodibility’s K value. Finally, the evaluation model of surface soil erodibility factor in black soil area is constructed. By testing the quantitative performance and evaluation accuracy of erodibility factors, it is proved that this method has good quantitative performance and evaluation accuracy of erodibility factors, and has strong practicability.

Multi-Touch Querying on Data Physicalizations in Immersive AR

Data physicalizations (3D printed terrain models, anatomical scans, or even abstract data) can naturally engage both the visual and haptic senses in ways that are difficult or impossible to do with traditional planar touch screens and even immersive digital displays. Yet, the rigid 3D physicalizations produced with today's most common 3D printers are fundamentally limited for data exploration and querying tasks that require dynamic input (e.g., touch sensing) and output (e.g., animation), functions that are easily handled with digital displays. We introduce a novel style of hybrid virtual + physical visualization designed specifically to support interactive data exploration tasks. Working toward a "best of both worlds" solution, our approach fuses immersive AR, physical 3D data printouts, and touch sensing through the physicalization. We demonstrate that this solution can support three of the most common spatial data querying interactions used in scientific visualization (streamline seeding, dynamic cutting places, and world-in-miniature visualization). Finally, we present quantitative performance data and describe a first application to exploratory visualization of an actively studied supercomputer climate simulation data with feedback from domain scientists.

Quantitative Performance Analysis of Flexible Ink-jet Printed Low-cost DNA Sensors Based on a CNT Network

We report the design, fabrication and quantitative performance analysis of a low-cost, flexible carbon nanotube (CNT) network-based deoxyribonucleic acid (DNA) sensor. These sensors comprise an array of ink-jet printed silver (Ag) electrodes on a transparent polyethylene terephthalate (PET) flexible substrate, where a CNT network acts as a sensing layer. The DNA hybridization is studied by immobilizing single-stranded DNA (ssDNA) probes on the CNT surface; these probes recognize their complementary DNA target. Further, we have carried out a quantitative performance analysis of the flexible CNT biosensors using the analytic hierarchy process (AHP). We have identified various influencing factors and sub-factors (performance indicators), and quantified the performance of the flexible CNT biosensors in different measured states (before bending, during bending and after bending). Additionally, the noise and other external factors contributing to the measured real signal have been quantified. The interpretation of the overall outcome will enable the improvement of the performance of flexible biosensors fabricated through large-scale manufacturing for possible commercialization.