Invalid Regions Research Articles

Molecular Surface Remeshing with Local Region Refinement.

Molecular surface mesh generation is a prerequisite for using the boundary element method (BEM) and finite element method (FEM) in implicit-solvent modeling. Molecular surface meshes typically have small angles, redundant vertices, and low-quality elements. In the implicit-solvent modeling of biomolecular systems it is usually required to improve the mesh quality and eliminate low-quality elements. Existing methods often fail to efficiently remove low-quality elements, especially in complex molecular meshes. In this paper, we propose a mesh refinement method that smooths the meshes, eliminates invalid regions in a cut-and-fill strategy, and improves the minimal angle. We compared our method with four different state-of-the-art methods and found that our method showed a significant improvement over state-of-the-art methods in minimal angle, aspect ratio, and other meshing quality measurements. In addition, our method showed satisfactory results in terms of the ratio of regular vertices and the preservation of area and volume.

Sea-skyline-based image stabilization of a buoy-mounted catadioptric omnidirectional vision system

Marine monitoring systems have the requirements of a large field of view, low power consumption, real-time viewing, and economical and automatic functionality. This paper establishes an omnidirectional vision system used in marine buoys that meets these requirements. We present a framework for image stabilization, which is achieved by omnidirectional sea-skyline detection in a marine environment. We propose an optimal edge estimation method to calculate the sea-skyline ellipsis according to the sea-skyline characteristics in panoramic images. We construct a compact panoramic image stabilization model based on the sea-skyline and propose a reconstruction method for the invalid regions using the key frame. The experimental results and analysis show that the proposed approach is capable of acquiring stable video in real-time marine monitoring tasks and that the target detection is sufficiently effective, efficient, and accurate for a real-time ship target detection application.

Monitoring and evaluation in freeze stress of winter wheat (Triticum aestivum L.) through canopy hyperspectrum reflectance and multiple statistical analysis

In order to evaluate the freeze stress of the winter wheat, field and pot experiments were conducted. The crop variables including plant height, above ground dry biomass (AGDB), chlorophyll content (CHLC), peroxidase (POD), superoxide dismutase (SOD), malondialdehyde (MDA), photosynthetic indexes, and canopy spectra were selected and assessed. Statistical methods of correlation analysis, principal component analysis (PCA), partial least squares (PLS), importance of variables analysis, and multiple linear regression (MLR) were implemented to establish freeze injury comprehensive evaluation index (FICEI), extract sensitive bands, and build the freeze injury predictive model. The results demonstrated that FICEI can be used to evaluate the damage degree. The major spectral regions were selected with the B-coefficient, variable importance in projection (VIP), and the frequencies of selection (FS) parameters derived from the PLS analysis. The combination of all three approaches can be utilized to reduce invalid spectral regions. According to the importance of variables analysis, the seven sensitive bands were extracted, which were 574nm, 702nm, 1093nm, 1111nm, 1210nm, 1302nm, and 1333nm, respectively. The calibrated MLR model based on the selected bands demonstrated an excellent performance as the R2 and RMSE were 0.924, 0.279 for FICEI. The model also illustrated an accurate and robust performance by using the pot validation data set since the higher value of coefficient of the determination and lower root mean squared error in validation model were confirmed. Our findings show that the estimation model for FICEI of winter wheat is reliable.

A convergence algorithm for correlation of breech face images based on the congruent matching cells (CMC) method

The Congruent Matching Cells (CMC) method was invented at the National Institute of Standards and Technology (NIST) for accurate firearm evidence identification and error rate estimation. The CMC method is based on the principle of discretization. The toolmark image of the reference sample is divided into correlation cells. Each cell is registered to the cell-sized area of the compared image that has maximum surface topography similarity. For each resulting cell pair, one parameter quantifies the similarity of the cell surface topography and three parameters quantify the pattern congruency of the registration position and orientation. An identification (declared match) requires a significant number of CMCs, that is, cell pairs that meet both similarity and pattern congruency requirements. The use of cell correlations reduces the effects of “invalid regions” in the compared image pairs and increases the correlation accuracy. The identification accuracy of the CMC method can be further improved by considering a feature named “convergence,” that is, the tendency of the x–y registration positions of the correlated cell pairs to converge at the correct registration angle when comparing same-source samples at different relative orientations. In this paper, the difference of the convergence feature between known matching (KM) and known non-matching (KNM) image pairs is characterized, based on which an improved algorithm is developed for breech face image correlations using the CMC method. Its advantage is demonstrated by comparison with three existing CMC algorithms using four datasets. The datasets address three different brands of consecutively manufactured pistol slides, with significant differences in the distribution overlap of cell pair topography similarity for KM and KNM image pairs. For the same CMC threshold values, the convergence algorithm demonstrates noticeably improved results by reducing the number of false-positive or false-negative CMCs in a comparison.

Simultaneous estimation of multiple phases in digital holographic interferometry using state space analysis

A new approach is proposed for the multiple phase estimation from a multicomponent exponential phase signal recorded in multi-beam digital holographic interferometry. It is capable of providing multidimensional measurements in a simultaneous manner from a single recording of the exponential phase signal encoding multiple phases. Each phase within a small window around each pixel is appproximated with a first order polynomial function of spatial coordinates. The problem of accurate estimation of polynomial coefficients, and in turn the unwrapped phases, is formulated as a state space analysis wherein the coefficients and signal amplitudes are set as the elements of a state vector. The state estimation is performed using the extended Kalman filter. An amplitude discrimination criterion is utilized in order to unambiguously estimate the coefficients associated with the individual signal components. The performance of proposed method is stable over a wide range of the ratio of signal amplitudes. The pixelwise phase estimation approach of the proposed method allows it to handle the fringe patterns that may contain invalid regions.

What can the programming language Rust do for astrophysics?

AbstractThe astrophysics community uses different tools for computational tasks such as complex systems simulations, radiative transfer calculations or big data. Programming languages like Fortran, C or C++ are commonly present in these tools and, generally, the language choice was made based on the need for performance. However, this comes at a cost: safety. For instance, a common source of error is the access to invalid memory regions, which produces random execution behaviors and affects the scientific interpretation of the results.In 2015, Mozilla Research released the first stable version of a new programming language named Rust. Many features make this new language attractive for the scientific community, it is open source and it guarantees memory safety while offering zero-cost abstraction.We explore the advantages and drawbacks of Rust for astrophysics by re-implementing the fundamental parts of Mercury-T, a Fortran code that simulates the dynamical and tidal evolution of multi-planet systems.

A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization

Two main challenges in differential evolution (DE) are reducing the number of function evaluations required to obtain optimal solutions and balancing the exploration and exploitation. In this paper, a local abstract convex underestimate strategy based on abstract convexity theory is proposed to address these two problems. First, the supporting hyperplanes are constructed for the neighboring individuals of the trial individual. Consequently, the underestimate value of the trial individual can be obtained by the supporting hyperplanes of its neighboring individuals. Through the guidance of the underestimate value in the select operation, the number of function evaluations can be reduced obviously. Second, some invalid regions of the domain where the global optimum cannot be found are safely excluded according to the underestimate information to improve reliability and exploration efficiency. Finally, the descent directions of supporting hyperplanes are employed for local enhancement to enhance exploitation capability. Accordingly, a novel DE algorithm using local abstract convex underestimate strategy (DELU) is proposed. Numerical experiments on 23 bound-constrained benchmark functions show that the proposed DELU is significantly better than, or at least comparable to several state-of-the art DE variants, non-DE algorithms, and surrogate-assisted evolutionary algorithms.

Improving Tooth Outline Detection by Active Appearance Model with Intensity-Diversification in Intraoral Radiographs

Automatic tooth detection of intraoral radiographs shows progressively importance in massive forensic verification. Since intraoral radiographs acquired from small oral cavity reveal great variation of intensity and distortion of structure morphology, automatic tooth detection poses a huge challenge. Enhancement methods may not effectively augment informative grayscale gradient. We proposed an intensity-diversification method to increase the detection rate of the tooth through different intensity spaces. The diversification process attempted to explore the image information that was expressible by intensity transform function. In this study, gamma transform was employed to generate different intensity-diversified images from the test radiograph. Deformable statistical Active Appearance Models (AAM) was used to detect a possible mandibular molar tooth region on the images. The AAM regions detected from intensity-diversified images were compared and ranked using three methods: histogram-based, edge-based and crown-root approximation-based methods. Since edge-based and crown-root approximation-based methods revealed higher accuracies, the top five matches in the ranking lists from these two methods were consequently voted by the Borda count to get the most suspected tooth region. Totally, 419 images from 367 patients were used in this study, 100 images for training and 319 images for testing. In our results, the correct detection rate was 71%, comparing to only 45% detection rate of the images without intensity-diversification. AAM outlines were detected in all 319 images, but not all of them belonged to valid tooth regions. In original images, 144 images had valid tooth outlines detected by AAM; 175 images were detected with invalid tooth regions. The true positive rate is 45% and the false positive rate 55%. With intensity-diversification and proposed matching methods, 227 AAM outlines detected were valid tooth regions, and 92 outlines were invalid tooth regions. The true positive rate is 71% and false positive 29%. This results supported that intensity-diversification process could improve automatic detection rate of mandibular molar in intraoral radiographs.

Conservative interpolation of edge and face data on n dimensional structured grids using differential forms

Interpolation methods for edge and face centered data are described, which preserve line and area integrals under regridding. These interpolation methods complement the multilinear nodal and conservative interpolation methods, which are widely used in climate data processing and other areas. The presented interpolation schemes ensure that curl-free and divergence-free fields remain so after regridding. These edge and face conservative interpolation methods are suitable for general curvilinear structured grids, including those with singular points (poles). Support for masked (invalid) regions is implicitly provided by attaching (partial) line/surface integral field values to cell edges/faces.

A Robust Shadow Matching Algorithm forGNSSPositioning

Commercial GNSS devices tend to perform poorly in urban canyon environments. The dense and tall buildings block the signals from many of the satellites. In this work, we present a particle filter algorithm for a Shadow Matching framework to face this problem. Given a 3D city map and given the satellites' signal properties, the algorithm calculates in real-time invalid regions inside the Region Of Interest (ROI). This approach reduces the ROI to a fraction of its original size. We present a general framework for a Shadow Matching positioning algorithm based on a modified particle filter. Using simulation experiments we have shown that the suggested method can improve the accuracy of existing GNSS devices in urban regions. Moreover, the proposed algorithm can be efficiently extended to 3D positioning in high sampling rate, inherently applicable for UAVs and Drones. Copyright © 2015 Institute of Navigation.

Fast Hermite element method for smoothing and differentiating noisy displacement field in digital image correlation

In our previous work, an improved Hermite finite element smoothing method (IHFESM) combined with the well-known Tikhonov regularization was proposed to smooth the noisy displacement field directly calculated by digital image correlation (DIC). Even though IHFESM could reconstruct reliable strain field for arbitrary region of interest (ROI), it still suffers from three defects in practice: (i) for the large scale problems, it involves high computational burden to find the optimum regularization parameter within the generalized cross-validation (GCV) function, since the inverse and the trace of two large matrices are frequently evaluated, (ii) the search range is too wide to find the desired optimum regularization parameter quickly, and (iii) for the arbitrary ROI, the complex two dimensional meshing routines are required to locate and mesh the invalid regions into triangular elements. In current work, to overcome above deficiencies, we propose the fast Hermite element method (FHEM). The proposed FHEM avoids the redundant computation in IHFESM by decomposing the resultant two small matrices to speed up the original matrix inverse and trace computation. To narrow down the search range of regularization parameter, the magnitude of the closeness term and roughness penalty term in error function are balanced by a new parameter. The calculation of global regularization matrix is also sped up by using simple formulation without any sophisticated meshing routines. Experiments show that the FHEM is at least 50 times as fast as IHFESM with similar accuracy, and it is recommended to be adopted to smooth and differentiate the noisy displacement field for DIC in practice.

Recognition of Human Iris Using Accurate Iris Map

Personal identification based on Biometrics technology is a trend in future. Iris Recognition is regarded as a high accuracy verification technology when compared to traditional approaches. In real-time Iris Recognition, Iris Localization is very important step for Iris Recognition. So, the Iris Masks plays an important role in iris recognition. This Iris Mask indicates which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. If the Iris Mask is inaccurate, it will decrease its performance in Iris Recognition system. So, we use learning- based algorithms to estimate accurate Iris Masks from Iris Images, which propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Index Terms: Gaussian mixture models, iris mask, iris recognition, iris occlusion estimation, simulated annealing.

Investigation of a New 3D Intelligent Ray-Tracing Model

While characterizing the wireless network, it is critical to predict the exact signal propagation paths in an efficient way for three dimensional (3D) indoor environment. In this paper, a new 3D intelligent ray-tracing (IRT) model based on the modified binary angle division (MBAD) technique is presented. The MBAD algorithm can launch the minimum amount of rays by identifying the invalid regions, which can skip the processing of the unnecessary signals. To further accelerate the MBAD algorithm feasible for the 3D environment, virtual surface technique and efficient polygon test are used. Besides, the amount of time for intersection test is reduced by using the surface skipping technique. Moreover, different radio signal propagation paths, such as reflection, refraction, and diffraction from 3D objects along with infinitesimal rays with very small wavefront are considered. The results obtained from this study show the superiority of the proposed model in terms of higher prediction accuracy (29.21 %) and better computational time (58.78 %) than other well-known techniques. Finally, this work also analytically examines the measured and the simulated results for the proposed IRT model and evaluates the effectiveness. The experimental results are compared with the simulated results obtained from IRT model and good agreement (accuracy of about 97 to $$>$$ > 99 %) demonstrated.

An improved edge detection algorithm for depth map inpainting

Three-dimensional (3D) measurement technology has been widely used in many scientific and engineering areas. The emergence of Kinect sensor makes 3D measurement much easier. However the depth map captured by Kinect sensor has some invalid regions, especially at object boundaries. These missing regions should be filled firstly. This paper proposes a depth-assisted edge detection algorithm and improves existing depth map inpainting algorithm using extracted edges. In the proposed algorithm, both color image and raw depth data are used to extract initial edges. Then the edges are optimized and are utilized to assist depth map inpainting. Comparative experiments demonstrate that the proposed edge detection algorithm can extract object boundaries and inhibit non-boundary edges caused by textures on object surfaces. The proposed depth inpainting algorithm can predict missing depth values successfully and has better performance than existing algorithm around object boundaries.

Guided depth enhancement via a fast marching method

Range imaging sensors such as Kinect and time-of-flight cameras can produce aligned depth and color images in real time. However, the depth maps captured by such sensors contain numerous invalid regions and suffer from heavy noise. These defects more or less influence the use of depth information in practical applications. In order to enhance the depth maps, this paper proposes a new inpainting approach based on the fast marching method (FMM). We extend the inpainting model and the propagation strategy of FMM to incorporate color information for depth inpainting. An edge-preserving guided filter is further applied for noise reduction. To validate our algorithm, we perform experiments on both Kinect data and Middlebury dataset which, respectively, provide qualitative and quantitative results. Meanwhile, we also compare it to the original FMM and other two state-of-the-art depth enhancement methods. Experimental results show that our method performs better than the local methods in terms of both visual and metric qualities, and it achieves visually comparable results to the time-consuming global method.

An Automatic Iris Occlusion Estimation Method Based on High-Dimensional Density Estimation

Iris masks play an important role in iris recognition. They indicate which part of the iris texture map is useful and which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. The accuracy of the iris mask is extremely important. The performance of the iris recognition system will decrease dramatically when the iris mask is inaccurate, even when the best recognition algorithm is used. Traditionally, people used the rule-based algorithms to estimate iris masks from iris images. However, the accuracy of the iris masks generated this way is questionable. In this work, we propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate. Experimental results show that the masks generated by the proposed algorithm increase the iris recognition rate on both ICE2 and UBIRIS dataset, verifying the effectiveness and importance of our proposed method for iris occlusion estimation.

Object-image-based method to construct an unweighted quality map for phase extraction and phase unwrapping

A method to construct an unweighted quality map for phase extraction and phase unwrapping is proposed, based on an object image pattern. The object image pattern must be recorded under the same conditions as that of the corresponding interference patterns, except that the lights coming from the reference arm of the interferometer are hidden. An unweighted quality map that can represent the valid and invalid regions in the interference patterns is completed successfully, based on two factors: the fact that the object region in the object image pattern is homologous with the valid region (i.e., the interference region) in the interference patterns, and on distinguishing between the object and background regions in the object image pattern using neighbor window threshold filtering and fitting the boundary of the object image. The application of the proposed method to the real measurement shows its feasibility and correctness. This paper might provide an alternative method for constructing an unweighted quality map for phase extraction and phase unwrapping.

Reversible authentication and cross-recovery of images using ( t, n)-threshold and modified-RCM watermarking

This paper presents an image authentication scheme for digital images. The proposed scheme protects a group of n images mutually. It designs a block matching procedure to generate the recovery data for each image, and applies a ( t, n − 1), 2 ≤ t < n, threshold mechanism to encode each recovery data in n − 1 shares. The recovery shares are cross-embedded in the n images using a modified reversible contrast mapping watermarking scheme. A signature-based authentication code is finally generated and stamped to provide evidence for integrity of each image. The scheme not only can detect the tampering activities, but also can locate and recover the invalid regions of the tampered image if t or more watermarked images in the same group were intact. A nice characteristic of the proposed scheme is that the original images can be reconstructed lossless if no watermarked image was tampered, making the technique feasible in the application of protecting very sensitive images such as military or medical images. Experimental results show that the proposed scheme successfully detects various kinds of image alterations such as filtering, cropping, and replacement, and the corrupted images are properly recovered using the cross-recovery scheme.

Windowed Fourier-filtered and quality-guided phase-unwrapping algorithm

We propose a windowed Fourier-filtered and quality-guided phase-unwrapping algorithm that is an extension and improvement of our previous phase-unwrapping algorithm based on windowed Fourier transform [Opt. Laser Technol.37, 458 (2005), Key Eng. Mater.326-328, 67 (2006). First, the filtered amplitude is used as a real-valued quality map, rather than a binary mask, which makes the phase-unwrapping algorithm more tolerant to low-quality regions in a wrapped-phase map, and the process is more automatic. Second, the window size selection is considered, which enables the algorithm to be adapted to tackle different phase-unwrapping problems. A large window size is useful for removing noise, building long barriers along phase discontinuities, and identifying invalid regions, while a small window size is useful for preserving local features, such as small regions and high-quality narrow channels. Eight typical examples in Ghiglia and Pritt's excellent book Two-Dimensional Phase Unwrapping: Theory, Algorithm and Software (Wiley, 1998) are used to evaluate the proposed algorithm. The proposed algorithm is able to unwrap all these examples successfully. The windowed Fourier ridges algorithm, another algorithm based on windowed Fourier transform, is also tested and found to be useful in building barriers along phase discontinuities.

Unsteady unified hypersonic/supersonic method for aeroelastic applications including wave/shock interaction

A unified unsteady hypersonic/supersonic method using perturbed Euler characteristics (PEC) has been developed for the treatment of stability derivatives and panel flutter of wedgelike profiles. The present PEC method is valid for all supersonic Mach numbers, ranging from the low supersonic shock-attached limit to the Newtonian limit, in the complete frequency range, and for arbitrary body thickness so long as the bow shock remains attached to the body apex. The unsteady lifts, moments, and stability derivatives for oscillating wedge profiles, an oscillating leading-edge flap, and the related panel flutter problem are studied. Our computed results are found to differ with those of piston theory and linear potential theory in their invalid regions as expected. The essential feature of the PEC method lies in that it can fully account for the unsteady Mach wave/shock wave interaction and, hence, the rotationality and thickness effects. In view of its exactness, in terms of the characteristic formulation, and its ease of application, the present (PEC) method should be a promising method for panel flutter applications to hypersonic vehicles such as NASP.