High-contrast Features Research Articles

Evaluation of Image Segmentation Methods for In Situ Quality Assessment in Additive Manufacturing

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

Mapping off-road tracks and animal paths in protected areas using high-resolution GeoEye-1 panchromatic satellite imagery

ABSTRACT Off-road driving activities are common in many protected areas. This study aimed at applying a curvelet-based approach for the automatic extraction, mapping and separation of off-road tracks and animal paths in Masai Mara National Reserve, Kenya, using high-resolution GeoEye-1 panchromatic satellite imagery (50 cm). A novel hybrid remote sensing-GIS method comprising three main blocks is proposed: (1) extracting the high-contrast curvilinear feature from curvelet magnitudes derived from the finer scale of curvelet coefficient; (2) extracting the low-contrast curvilinear feature from coarser scale curvelets and refine the shape by deformable active contour (Snake), and (3) categorizing the extracted curvilinear feature into vehicle tracks and animal paths by a fuzzy logic inference system. Results from quantification of extraction and categorization performance of the trails were 77.5% for completeness, 89.2% for correctness, and 4.5% for redundancy, with an overall accuracy of 79.5%. Using grid matching, we find a high correlation between off-road vehicle tracks and animal paths in the area (r = 0.75, p < 0.05) indicating co-occurrence of these two types of trails. The proposed approach provides a basis for large-scale mapping and monitoring of off-road tracks and animal paths in the African savanna from space.

Magnetic splitting of electromagnetically induced transparency and absorption spectra in a standing-wave coupled lambda-type system

We study the splitting of traveling wave and standing wave coupled Electromagnetically Induced Transparency (EIT) and Electromagnetically Induced enhanced Absorption (EIA) lines in the presence of a uniform magnetic field in an Electromagnetically Induced Grating (EIG). We realize a Λ-type system in a room-temperature Doppler-broadened buffer-gas-free, unshielded vapor cell of pure 133Cs atoms. Different orientations of the magnetic field are studied while the polarizations of the coupling and probe laser beams are arranged in the lin⊥lin configuration. The effects of the different field directions on the EIT and the EIG-induced EIA are explained in light of the existing theory. The magnetically induced high-contrast features in the EIG scheme are also discussed in comparison to the low-contrast EIT features observed for the same strengths of magnetic field.

Thermally boosted upconversion luminescence and high-performance thermometry in ScF3:Yb3+/Tm3+ nanorods with negative thermal expansion

Luminescence nanothermometers featuring high spatial resolution and thermal resolution have aroused extensive interest in biological and nanomedical fields. However, the challenge of thermal quenching in upconversion (UC) nanocrystals hampers achieving elevated relative temperature sensitivity (Sr). In this study, the quasi cubic nanorods of ScF3:Yb3+/Tm3+ with negative thermal expansion were prepared, and thermally boosted luminescence was found to derive from the phonon-assisted energy transfer, resulting in the emission band of 3F2,3 → 3H6 of Tm3+ ions enhanced monotonously. While the emission bands of (1G4)1D2→3F4 of Tm3+ ions first increased slightly and then decreased seriously because of the competition between phonon-assisted and cross-relaxation (CR) processes. Relying on this high-contrast thermal dependent feature, the maximum relative sensitivity of 1.60% K−1 at 523 K was achieved in the proposed luminescence intensity ratio (LIR) thermometry. These findings would open a new avenue for designing thermally boosted upconversion nanocrystals and reliable optical thermometers by using Tm3+ activated luminescent materials.

An adaptive space and time method in partially explicit splitting scheme for multiscale flow problems

We propose a space and time adaptive framework for a partially explicit splitting scheme of flow problems in multiscale media. We consider the parabolic equation with high-contrast multiscale coefficients. It is known that sufficient number of multiscale basis are needed to capture non-local features and information across all scales. Moreover, the time step size in an explicit discretization of the problem is affected by the magnitude of the coefficient. To improve computational efficiency, in this work, we introduce an adaptive algorithm to refine temporal size and enrich multiscale spaces. We first utilize a stable temporal splitting scheme with an appropriate construction of multiscale spaces to ensure that the time step size is independent of the contrast. The multiscale subspaces are then constructed to handle the fast-flow and slow-flow parts separately, and implicit and explicit discretization is adopted for the degrees of freedom with respect to the two multiscale subspaces, respectively. A multirate time stepping is introduced for the two parts considering that the flow rates vary significantly in different regions due to the high-contrast features of the multiscale coefficients. We derive both temporal and spatial error estimators to identify local regions for two components of the solutions. Start with coarser time step sizes and a few important multiscale basis functions in each subspace, we can adaptively refine time stepping and add additional basis with constraint energy minimizing properties based on the derived error indicators. The stability and convergence of the method are studied, and several numerical tests are presented to demonstrate the performance of the proposed method.

Views from 'crabworld': the spatial distribution of light in a tropical mudflat.

Natural scene analysis has been extensively used to understand how the invariant structure of the visual environment may have shaped biological image processing strategies. This paper deals with four crucial, but hitherto largely neglected aspects of natural scenes: (1) the viewpoint of specific animals; (2) the fact that image statistics are not independent of the position within the visual field; (3) the influence of the direction of illumination on luminance, spectral and polarization contrast in a scene; and (4) the biologically relevant information content of natural scenes. To address these issues, I recorded the spatial distribution of light in a tropical mudflat with a spectrographic imager equipped with a polarizing filter in an attempt to describe quantitatively the visual environment of fiddler crabs. The environment viewed by the crabs has a distinct structure. Depending on the position of the sun, the luminance, the spectral composition, and the polarization characteristics of horizontal light distribution are not uniform. This is true for both skylight and for reflections from the mudflat surface. The high-contrast feature of the line of horizon dominates the vertical distribution of light and is a discontinuity in terms of luminance, spectral distribution and of image statistics. On a clear day, skylight intensity increases towards the horizon due to multiple scattering, and its spectral composition increasingly resembles that of sunlight. Sky-substratum contrast is highest at short wavelengths. I discuss the consequences of this extreme example of the topography of vision for extracting biologically relevant information from natural scenes.

Resolution aware nonconvex quasinorm iterative digital breast tomosynthesis imaging

Digital breast tomosynthesis is a promising modality providing 3D cross sectional images of breast. However, reconstructing the image from few projections is still a challenging task. We introduce an anisotropic total variation regularized reconstruction method which exploits varying resolution along transverse and sagittal planes by adapting the weights of TV gradients. Moreover, we also investigate if non-convex ℓp quasinorm with p ∈ (0,1) can help to improve the performance of anisotropic TV in detecting micro calcifications and spheroidal masses. The contrast to noise ratio (CNR) for in-plane quality, full with at half maximum (FWHM) of artefact spread function (ASF) for resolution in the z-axis, signal difference to noise ratio (SdNR), and quality factor (QF) for a comprehensive evaluation were used in quantitative analyses and comparisons. For low contrast analysis, QF value of the proposed method for p = 0.9 is 0.18 whereas QF value of isotropic TV with ℓ1 and ℓ2 norms are 0.15 and 0.14, respectively. For high contrast features such as micro-calcifications, although the best CNR value changes with different p values, the majority of the best CNR values are calculated for values 0.8 and 0.9. Overall, the results show that the proposed method, anisotropic TV with non-convex ℓp quasinorm, outperforms isotropic TV with ℓ1 and ℓ2 norms by recovering small micro calcifications and spheroidal masses with higher CNR and increased depth resolution. The proposed method shows the best performance for p values of 0.8 ≤ p < 1.

Zebrafish Larvae Position Tracker (Z-LaP Tracker): a high-throughput deep-learning behavioral approach for the identification of calcineurin pathway-modulating drugs using zebrafish larvae

Brain function studies greatly depend on quantification and analysis of behavior. While behavior can be imaged efficiently, the quantification of specific aspects of behavior is labor-intensive and may introduce individual biases. Recent advances in deep learning and artificial intelligence-based tools have made it possible to precisely track individual features of freely moving animals in diverse environments without any markers. In the current study, we developed Zebrafish Larvae Position Tracker (Z-LaP Tracker), a modification of the markerless position estimation software DeepLabCut, to quantify zebrafish larval behavior in a high-throughput 384-well setting. We utilized the high-contrast features of our model animal, zebrafish larvae, including the eyes and the yolk for our behavioral analysis. Using this experimental setup, we quantified relevant behaviors with similar accuracy to the analysis performed by humans. The changes in behavior were organized in behavioral profiles, which were examined by K-means and hierarchical cluster analysis. Calcineurin inhibitors exhibited a distinct behavioral profile characterized by increased activity, acoustic hyperexcitability, reduced visually guided behaviors, and reduced habituation to acoustic stimuli. The developed methodologies were used to identify ‘CsA-type’ drugs that might be promising candidates for the prevention and treatment of neurological disorders.

Efficient diffraction control using a tunable active-Raman gain medium

We present a scheme to create an all-optical tunable and lossless waveguide using a controllable coherent Raman process in an atomic rubidium vapor in an $\mathcal{N}$-type configuration. We employ a Gaussian Raman field and a Laguerre-Gaussian control field to imprint a high-contrast tunable waveguidelike feature inside the atomic medium. We numerically demonstrate that such a waveguide is able to guide arbitrary modes of a weak probe beam to several Rayleigh lengths without diffraction and absorption. Our results on an all-optical waveguide-based scheme may have potential applications in lossless image processing, high-contrast biomedical imaging, and image metrology.

P‐8.11: A Halo Optimization Idea Applied to Automotive Local Diming Display

As the advantages of Local Diming display technology are gradually recognized by mainstream car company customers, for it has High contrast and low power consumption features, but the halo problem that arises at this stage is also plaguing developers and end customers. This article will discuss with readers the solution to the halo from three aspects: halo evaluation and test standards, design optimization schemes and image processing methods.

Pixel-wise structured light calibration method with a color calibration target.

We propose to use a calibration target with a narrow spectral color range for the background (e.g., from blue) and broader spectral color range for the feature points (e.g., blue + red circles), and fringe patterns matching the background color for accurate phase extraction. Since the captured fringe patterns are not affected by the high contrast of the calibration target, phase information can be accurately extracted without edging artifacts. Those feature points can be clearly "seen" by the camera if the ambient light matches the feature color or without the background color. We extract each calibration pose for three-dimensional coordinate determination for each pixel, and then establish pixel-wise relationship between each coordinate and phase. Comparing with our previously published method, this method significantly fundamentally simplifies and improves the algorithm by eliminating the computational framework estimate smooth phase near high-contrast feature edges. Experimental results demonstrated the success of our proposed calibration method.

Benzothiadiazole-based dibenzobenzimidazole derivatives with aggregation-induced deep-red fluorescence and different mechanically responsive fluorescence features

Four novel benzothiadiazole-containing dibenzobenzimidazole derivatives were elaborately designed and successfully synthesized. Meanwhile, their molecular structures were characterized by nuclear magnetic resonance spectroscopy, mass spectrometry and elemental analysis. Interestingly, these newly synthesized dyes demonstrated remarkable aggregation-induced emission characteristics, with high brightness deep-red aggregative-state fluorescence. These highly emissive fluorophores displayed different photoluminescence in the solid state, which involved two fluorescence colors of orange and deep-red. Additionally, these fluorogenic molecules also showed different mechanically responsive emissive properties. Among them, orange emissive triad compound containing dibenzobenzimidazole, benzothiadiazole and triphenylamine units exhibited deep-red fluorescence upon treated by mechanical grinding; thus this dye possessed high-contrast mechanofluorochromism feature. In contrast, the other three deep-red fluorescent compounds showed no obvious emissive variation after grinding, indicating their negligible mechanofluorochromism phenomena. The powder X-ray diffraction experimental results provided evidence for source of the notable mechanofluorochromic behavior, and crystalline-to-amorphous morphological conversion is responsible for the observed mechanically responsive fluorescence nature.

A Spectral Principal Component Analysis-Based Framework for Composite Hard/Soft Tissue Fluorescence Image Investigation.

Traditional thin sectioning microscopy of large bone and dental tissue samples using demineralization may disrupt structure morphologies and even damage soft tissues, thus compromising the histopathological investigation. Here, we developed a synergistic and original framework on thick sections based on wide-field multi-fluorescence imaging and spectral Principal Component Analysis (sPCA) as an alternative, fast, versatile, and reliable solution, suitable for highly mineralized tissue structure sustain and visualization. Periodontal 2-mm thick sections were stained with a solution containing five fluorescent dyes chosen for their ability to discriminate close tissues, and acquisitions were performed with a multi-zoom macroscope for blue, green, red, and NIR (near-infrared) emissions. Eigen-images derived from both standard scaler (Std) and Contrast Limited Adaptive Histogram Equalization (Clahe) pre-preprocessing significantly enhanced tissue contrasts, highly suitable for histopathological investigation with an in-depth detail for sub-tissue structure discrimination. Using this method, it is possible to preserve and delineate accurately the different anatomical/morphological features of the periodontium, a complex tooth-supporting multi-tissue. Indeed, we achieve characterization of gingiva, alveolar bone, cementum, and periodontal ligament tissues. The ease and adaptability of this approach make it an effective method for providing high-contrast features that are not usually available in standard staining histology. Beyond periodontal investigations, this first proof of concept of an sPCA solution for optical microscopy of complex structures, especially including mineralized tissues opens new perspectives to deal with other chronic diseases involving complex tissue and organ defects. Overall, such an imaging framework appears to be a novel and convenient strategy for optical microscopy investigation.

Multirate Partially Explicit Scheme for Multiscale Flow Problems

For time-dependent problems with high-contrast multiscale coefficients, the time step size for explicit methods is affected by the magnitude of the coefficient parameter. With a suitable construction of multiscale space, one can achieve a stable temporal splitting scheme where the time step size is independent of the contrast. Consider the parabolic equation with heterogeneous diffusion parameter, the flow rates vary significantly in different regions due to the high-contrast features of the diffusivity. In this work, we aim to introduce a multirate partially explicit splitting scheme to achieve efficient simulation with the desired accuracy. We first design multiscale subspaces to handle flow with different speed. For the fast flow, we obtain a low-dimensional subspace with respect to the high-diffusive component and adopt an implicit time discretization scheme. The other multiscale subspace will take care of the slow flow, and the corresponding degrees of freedom are treated explicitly. Then a multirate time stepping is introduced for the two parts. The stability of the multirate methods is analyzed for the partially explicit scheme. Moreover, we derive local error estimators corresponding to the two components of the solutions and provide an upper bound of the errors. An adaptive local temporal refinement framework is then proposed to achieve higher computational efficiency. Several numerical tests are presented to demonstrate the performance of the proposed method.

Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification

Full-waveform inversion (FWI) is an accurate imaging approach for modeling the velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, the strong nonlinearity of FWI resulting from fitting oscillatory waveforms can trap the optimization in local minima. We have adopted a neural-network-based full-waveform inversion (NNFWI) method that integrates deep neural networks with FWI by representing the velocity model with a generative neural network. Neural networks can naturally introduce spatial correlations as regularization to the generated velocity model, which suppresses noise in the gradients and mitigates local minima. The velocity model generated by neural networks is input to the same partial differential equation (PDE) solvers used in conventional FWI. The gradients of the neural networks and PDEs are calculated using automatic differentiation, which back propagates gradients through the acoustic PDEs and neural network layers to update the weights of the generative neural network. Experiments on 1D velocity models, the Marmousi model, and the 2004 BP model determine that NNFWI can mitigate local minima, especially for imaging high-contrast features such as salt bodies, and it significantly improves the inversion in the presence of noise. Adding dropout layers to the neural network model also allows analyzing the uncertainty of the inversion results through Monte Carlo dropout. NNFWI opens a new pathway to combine deep learning and FWI for exploiting the characteristics of deep neural networks and the high accuracy of PDE solvers. Because NNFWI does not require extra training data and optimization loops, it provides an attractive and straightforward alternative to conventional FWI.

Low-Cost Nanostructured Thin Films as Covert Laser Readable Security Tags for Large-Scale Productions Tracking

We report here the feasibility study of anti-counterfeiting low-cost nanostructured flexible security tags for the tracking of large-scale fabrication products, such as pharmaceuticals or original equipment manufacturers. The fabrication process makes use of the mature nanotechnology called Template Synthesis to shape thin track-etched polymer film into covert laser readable tags, combining random self-organized structures with organized patterns. Techniques are developed to drastically limit the number of fabrication steps and keep fabrication costs low, while opening to numerous adjustment parameters. A dedicated, simple optical setup is presented, to capture speckle images of such tags lightened up by light emitting diodes or laser beams. Speckle images are analyzed in terms of encoding parameters, found here quite numerous to ensure a large coding range of large-scale production batches. We particularly highlight ultra-dark areas in speckle images, where nanowire structures completely inhibit speckle patterns. This unique, high-contrast optical feature addresses these low-cost nanostructured thin films to provide a very promising solution for large-scale security tags.

Free trajectory cone beam computed tomography reconstruction method for synchronous scanning of geometric calibration phantom and imaging object

In order to suppress the geometrical artifacts caused by random jitter in ray source scanning, and to achieve flexible ray source scanning trajectory and meet the requirements of task-driven scanning imaging, a method of free trajectory cone-beam computed tomography (CBCT) reconstruction is proposed in this paper. This method proposed a geometric calibration method of two-dimensional plane. Based on this method, the geometric calibration phantom and the imaging object could be simultaneously imaged. Then, the geometric parameters could be obtained by online calibration method, and then combined with the geometric parameters, the alternating direction multiplier method (ADMM) was used for image iterative reconstruction. Experimental results showed that this method obtained high quality reconstruction image with high contrast and clear feature edge. The root mean square errors (RMSE) of the simulation results were rather small, and the structural similarity (SSIM) values were all above 0.99. The experimental results showed that it had lower image information entropy (IE) and higher contrast noise ratio (CNR). This method provides some practical value for CBCT to realize trajectory freedom and obtain high quality reconstructed image.

Improving a Rapid Alignment Method of Tomography Projections by a Parallel Approach

The high resolution of synchrotron cryo-nano tomography can be easily undermined by setup instabilities and sample stage deficiencies such as runout or backlash. At the cost of limiting the sample visibility, especially in the case of bio-specimens, high contrast nano-beads are often added to the solution to provide a set of landmarks for a manual alignment. However, the spatial distribution of these reference points within the sample is difficult to control, resulting in many datasets without a sufficient amount of such critical features for tracking. Fast automatic methods based on tomography consistency are thus desirable, especially for biological samples, where regular, high contrast features can be scarce. Current off-the-shelf implementations of such classes of algorithms are slow if used on a real-world high-resolution dataset. In this paper, we present a fast implementation of a consistency-based alignment algorithm especially tailored to a multi-GPU system. Our implementation is released as open-source.

Use of Hyper-Spectral Visible and Near-Infrared Satellite Data for Timely Estimates of the Earth’s Surface Reflectance in Cloudy Conditions: Part 2- Image Restoration With HICO Satellite Data in Overcast Conditions

Satellite-based visible and near-infrared imaging of the Earth’s surface is generally not performed in moderate to highly cloudy conditions; images that look visibly cloud covered to the human eye are typically discarded. Here, we expand upon previous work that employed machine learning (ML) to estimate underlying land surface reflectances at red, green, and blue (RGB) wavelengths in cloud contaminated spectra using a low spatial resolution satellite spectrometer. Specifically, we apply the ML methodology to a case study at much higher spatial resolution with the Hyperspectral Imager for the Coastal Ocean (HICO) that flew on the International Space Station (ISS). HICO spatial sampling is of the order of 90 m. The purpose of our case study is to test whether high spatial resolution features can be captured using hyper-spectral imaging in lightly cloudy and overcast conditions. We selected one clear and one cloudy image over a portion of the panhandle coastline of Florida to demonstrate that land features are partially recoverable in overcast conditions. Many high contrast features are well recovered in the presence of optically thin clouds. However, some of the low contrast features, such as narrow roads, are smeared out in the heavily clouded part of the reconstructed image. This case study demonstrates that our approach may be useful for many science and operational applications that are being developed for current and upcoming satellite missions including precision agriculture and natural vegetation analysis, water quality assessment, as well as disturbance, change, hazard, and disaster detection.

Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb’s model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.