Shadow Edges Research Articles

Image shadow removal based on pseudo-illuminated region and log domain transformation

ABSTRACT The presence of shadows in images can significantly degrade image quality. To address the challenges, this study utilizes pseudo-illumination regions and logarithmic domain transformations. Firstly, the input shadow image and its corresponding shadow region are segmented and clustered into multiple image blocks. Then, a comprehensive consideration of distance, color, and texture features is employed to construct pseudo illumination regions. Subsequently, the designed logarithmic domain transformation function is utilized for shadow removal. Finally, color correction and optimization of shadow edges complete the overall process. The feasibility of this approach has been preliminarily verified through experimental analysis.

Horizon replicas in black hole shadows

Recently, new exploratory channels have opened up for the physics of highly compact objects, such as gravitational waves and black hole shadows. Moreover, more precise analysis and observations are now possible in the physics of accretion around compact objects. These advancements provide in particular an unprecedented insight into the physics near the horizons of a black hole. In this work we focus on the shadow boundary of a Kerr black hole, introducing observables related to special null orbits, called horizons replicas, solutions of the shadow edge equations which are related to particular photon orbits, defined by constraints on their impact parameter, carrying information about the angular momentum of the central spinning object. These orbits are related to particular regions on the shadow boundary and might be used to determine the spin of the black hole. The results provide the conditions by which horizon replicas are imprinted in the black hole shadow profile, in dependence on the black hole dimensionless spin and observational angle, providing eventually new templates for the future observations.

Stylizing Ribbons: Computing Surface Contours With Temporally Coherent Orientations.

Line work is a core element for the stylization of computer animations used by recent shows. However, existing stylization techniques are limited to edge treatments based on brush strokes or textures applied solely on top of curves. In this work, we propose new stylization effects by offering artists direct control over the inside and outside of surface contours. To this end, we introduce a method that creates ribbons, geometry strips of possibly varying width, that extrude from each side of the surface contour with temporally coherent orientations. Our contributions include the generation of spatially and temporally consistent normal orientations along visible contours and a trimming routine that converts arrangements of offset curves into ribbons free of intersections. We demonstrate the expressiveness and versatility of stylized ribbons by applying various effects on both character and shadow edges from animation sequences.

Polynomial Representation and Degree Sequence of Graphs Resulting from Some Graph Operations

Let $G = (V(G),E(G))$ be a graph with degree sequence $\langle d_1,d_2, \cdots, d_n \rangle$, where $d_1 \ge d_2 \ge \cdots \ge d_n$. The polynomial representation of $G$ is given by $f_G(x) = \displaystyle \sum_{i=1}^n x^{d_i} = \sum_{k=1}^{\Delta(G)}a_kx^{k}$, where $a_k$ is the number of vertices of $G$ having degree $k$ for each $i = 1,2,\cdots n = \Delta(G)$. In this paper, we give the polynomial representation of the complement and line graph of a graph, the shadow graph, complementary prism, edge corona, strong product, symmetric product, and disjunction of two graphs.

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-of-the-art methods in various evaluation metrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/SonarRPFNet</uri> .

Spiral-phase-objective for a compact spiral-phase-contrast microscopy.

Spiral-phase-contrast imaging, which utilizes a spiral phase optical element, has proven to be effective in enhancing various aspects of imaging, such as edge contrast and shadow imaging. Typically, the implementation of spiral-phase-contrast imaging requires the formation of a Fourier plane through a 4f optical configuration in addition to an existing optical microscope. In this study, we present what we believe to be a novel single spiral-phase-objective, integrating a spiral phase plate, which can be easily and simply applied to a standard microscope, such as a conventional objective. Using a new hybrid design approach that combines ray-tracing and field-tracing simulations, we theoretically realized a well-defined and high-quality vortex beam through the spiral-phase-objective. The spiral-phase-objective was designed to have conditions that are practically manufacturable while providing predictable performance. To evaluate its capabilities, we utilized the designed spiral-phase-objective to investigate isotropic spiral phase contrast and anisotropic shadow imaging through field-tracing simulations, and explored the variation of edge contrast caused by changes in the thickness of the imaging object.

Closeness Centrality of Vertices in Graphs Under Some Operations

In this paper, we revisit the concept of (normalized) closeness centrality of a vertex in a graph and investigate it in some graphs under some operations. Specifically, we derive formulas that compute the closeness centrality of vertices in the shadow graph, complementary prism, edge corona, and disjunction of graphs.

Efficient Spatial Resampling Using the PDF Similarity

In real-time rendering, spatiotemporal reservoir resampling (ReSTIR) is a powerful technique to increase the number of candidate samples for resampled importance sampling. However, reusing spatiotemporal samples is not always efficient when target PDFs for the reused samples are dissimilar to the integrand. Target PDFs are often spatially different for highly detailed scenes due to geometry edges, normal maps, spatially varying materials, and shadow edges. This paper introduces a new method of rejecting spatial reuse based on the similarity of PDF shapes for single-bounce path connections (e.g., direct illumination). While existing rejection methods for ReSTIR do not support arbitrary materials and shadow edges, our PDF similarity takes them into account because target PDFs include BSDFs and shadows. In this paper, we present a rough estimation of PDF shapes using von Mises--Fisher distributions and temporal resampling. We also present a stable combination of our rejection method and the existing rejection method, considering estimation errors due to temporal disocclusions and moving light sources. This combination efficiently reduces the error around shadow edges with temporal continuities. By using our method for a ReSTIR variant that reuses shadow ray visibility for the integrand, we can reduce the number of shadow rays while preserving shadow edges.

Shadow Pattern-Enhanced Building Height Extraction Using Very-High-Resolution Image

Building height is valuable for a variety of foci in urban studies. The traditional field investigations are not practical for the updates of massive building height in a large-scale urban area. Given the relationship between building structures and their shadow sizes, the building shadow becomes practical for estimating its corresponding building height when its geometrical shape is visible in newly emerging very-high-resolution (VHR) images. However, the shadow shape of different buildings might vary significantly, posing a great challenge to determining the edge of shadow useful for predicting building height. This study proposes a shadow pattern classification system ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ShadowClass</i> ) to summarize the varied shadow shapes into a number of pattern categories and employ a cutting-edge CNN model to classify the extracted shadows into a pattern for automatically determining the edge of a building shadow being useful for building height estimation. We integrated the proposed approach into two branches of the state-of-the-art approaches: shadow-based building height estimation with open cyberinfrastructure and shadow-based building height estimation with VHR image. The experimental results proved that the proposed method could be a practical solution for single and isolated buildings that have their complete shadow shape.

Multilevel feature context semantic fusion network for cloud and cloud shadow segmentation

Cloud detection is an important prerequisite for remote sensing image application. Any remote sensing image from which the information of ground object could be obtained will inevitably be preprocessed on cloud occlusion. In the traditional method, the segmentation of cloud and its shadow will be affected by the complex background. In the detection process, due to insufficient information extraction, misjudgment often occurs, and the cloud boundary processing is also very rough. In order to improve the accuracy of cloud and cloud shadow segmentation, we propose a multilevel feature context semantic fusion network. The network takes the residual network as the backbone networkand adopts the structure of encoding and decoding as a whole. In the model, we introduce the multibranch residual context semantic module, the multiscale convolution subchannel attention module, and the feature fusion upsampling module to strengthen the feature extraction, refine the cloud and cloud shadow edge information, and enhance the actual segmentation ability of the model. The experimental results show that the proposed method is more accurate than the previous network in the segmentation of cloud and cloud shadow and has good generalization ability on other datasets, which is of great significance for the study of cloud.

Mitigating Shadows in LIDAR Scan Matching Using Spherical Voxels

In this paper we propose an approach to mitigate shadowing errors in Lidar scan matching, by introducing a preprocessing step based on spherical gridding. Because the grid aligns with the Lidar beam, it is relatively easy to eliminate shadow edges which cause systematic errors in Lidar scan matching. As we show through simulation, our proposed algorithm provides better results than ground-plane removal, the most common existing strategy for shadow mitigation. Unlike ground plane removal, our method applies to arbitrary terrains (e.g. shadows on urban walls, shadows in hilly terrain) while retaining key Lidar points on the ground that are critical for estimating changes in height, pitch, and roll. Our preprocessing algorithm can be used with a range of scan-matching methods; however, for voxel-based scan matching methods, it provides additional benefits by reducing computation costs and more evenly distributing Lidar points among voxels.

Distinguishing shadows from surface boundaries using local achromatic cues.

In order to accurately parse the visual scene into distinct surfaces, it is essential to determine whether a local luminance edge is caused by a boundary between two surfaces or a shadow cast across a single surface. Previous studies have demonstrated that local chromatic cues may help to distinguish edges caused by shadows from those caused by surface boundaries, but the information potentially available in local achromatic cues like contrast, texture, and penumbral blur remains poorly understood. In this study, we develop and analyze a large database of hand-labeled achromatic shadow edges to better understand what image properties distinguish them from occlusion edges. We find that both the highest contrast as well as the lowest contrast edges are more likely to be occlusions than shadows, extending previous observations based on a more limited image set. We also find that contrast cues alone can reliably distinguish the two edge categories with nearly 70% accuracy at 40x40 resolution. Logistic regression on a Gabor Filter bank (GFB) modeling a population of V1 simple cells separates the categories with nearly 80% accuracy, and furthermore exhibits tuning to penumbral blur. A Filter-Rectify Filter (FRF) style neural network extending the GFB model performed at better than 80% accuracy, and exhibited blur tuning and greater sensitivity to texture differences. We compare human performance on our edge classification task to that of the FRF and GFB models, finding the best human observers attaining the same performance as the machine classifiers. Several analyses demonstrate both classifiers exhibit significant positive correlation with human behavior, although we find a slightly better agreement on an image-by-image basis between human performance and the FRF model than the GFB model, suggesting an important role for texture.

Predictive value and prevalence of refractive edge shadow in diagnosis of ovarian dermoids.

To evaluate the diagnostic performance of refractive edge shadow in evaluation of ovarian dermoids compared to other benign adnexal masses. Ultrasound images of 139 patients with 154 dermoids, endometriomas, and hemorrhagic cysts were retrospectively reviewed by 3 radiologists blinded to final diagnosis. Ultrasound and clinical features were compared to pathology or follow-up ultrasound results as reference standard. Inter-reader agreements with free-marginal kappa and diagnostic performance were evaluated. The former was compared using Fisher's exact test or Mann-Whitney test with p < 0.05 to determine statistical significance. The study sample consisted of 154 lesions: 50 dermoids, 50 endometriomas, and 54 hemorrhagic cysts. Refractive edge shadow, homogeneous echogenic appearance, tip of the iceberg sign, mural echogenic nodule, echogenic shadowing focus, and dot-dash sign all were statistically significant across all readers for the diagnosis of dermoid. Prevalence of each feature in dermoids compared to other entities were as follows: refractive edge shadow (70% vs 8%; p < 0.001), homogeneously echogenic appearance (34% vs 2%; p < 0.001), tip of the iceberg sign (16% vs 1%; p < 0.001), mural echogenic nodule (38% vs 2%; p < 0.001), echogenic shadowing focus (13% vs 1%; p < 0.001), and dot-dash sign (44% vs 1%; p < 0.001). Refractive edge shadow had the highest sensitivity, negative predictive value, and accuracy among all ultrasound features associated with dermoids (70%, 86%, and 85%, respectively). Refractive edge shadow is a promising ultrasound feature for diagnosis of dermoids, with the highest diagnostic accuracy and prevalence compared to other previously described features associated with dermoids.

Gravitational lensing by black holes with multiple photon spheres

We study gravitational lensing of light by hairy black holes, which, in a certain parameter regime, can possess two photon spheres of different size outside the event horizon. In particular, we focus on higher-order images of a point-like light source and a luminous celestial sphere produced by strong gravitational lensing near photon spheres. Two photon spheres usually triple the number of high-order images of a point-like light source. When a hairy black hole is illuminated by a celestial sphere, two photon spheres would give rise to two critical curves in the black hole image, and the smaller critical curve coincides with the shadow edge. In addition to a set of higher-order images of the celestial sphere outside the shadow edge, two more sets of higher-order images are observed inside and outside the larger critical curve, respectively.

Dynamical photon sphere and time evolving shadow around black holes with temporal accretion

A photon sphere is known as the geometrical structure shaping a black hole shadow. The mechanism is well understood for static or stationary black hole spacetimes such as the Schwarzschild and the Kerr spacetimes. In this paper, we investigate and explicitly specify a photon sphere that shapes a black hole shadow in a dynamical spacetime while taking the global structure of the spacetime into account. We consider dynamical and eternal black hole cases of the Vaidya spacetime, which represents a spherically symmetric black hole with accreting null dust. First, we numerically show that there are the dynamical photon sphere and photon orbits corresponding to the shadow edge in a moderate accretion case. Second, the photon spheres are derived analytically in special cases. Finally, we discuss the relation between our photon sphere and the several notions defined as a photon sphere generalization.

Nondivergent deflection of light around a photon sphere of a compact object

We demonstrate that the location of a stable photon sphere (PS) in a compact object is not always an edge such as the inner boundary of a black hole shadow, whereas the location of an unstable PS is known to be the shadow edge notably in the Schwarzschild black hole. If a static spherically symmetric (SSS) spacetime has the stable outermost PS, the spacetime cannot be asymptotically flat. A nondivergent deflection is caused for a photon traveling around a stable PS, though a logarithmic divergent behavior is known to appear in most of SSS compact objects with an unstable photon sphere. The reason for the nondivergence is that the closest approach of a photon is prohibited in the immediate vicinity of the stable PS when the photon is emitted from a source (or reaches a receiver) distant from a lens object. The finite gap size depends on the receiver and source distances from the lens as well as the lens parameters. The mild deflection angle of light can be approximated by an arcsine function. A class of SSS solutions in Weyl gravity exemplify the nondivergent deflection near the stable outer PS.

High-Precision Measurement of Height Differences from Shadows in Non-Stereo Imagery: New Methodology and Accuracy Assessment

The shadow-height method has been extensively used to extract the heights of buildings from the shadows they cast in non-stereo (single view) aerial and satellite imagery. However, the use of this method in Earth sciences has been limited, partially due to the relatively low accuracy reported, the fuzziness of shadow edges, the complexities of the scanning sensors, and a lack of software tools. In this paper, we present an enhanced shadow-height methodology offering significant accuracy improvement. These improvements are mainly the result of using a physical approach to model the illumination gradient through the edge of shadows and by leveraging meteorological data to precisely estimate atmospheric refraction. We validated 91 shadow-derived height estimations from images obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) at three sites with latitudes between 33 and 78°S: The Andes Mountains, Sentinel Range, and Abbot ice shelf. Reference measurements were obtained from Global Navigation Satellite System (GNSS) surveys and the Ice, Cloud, and land Elevation Satellite (ICESat). The observed errors fell below 6% for small height differences (∼20 m) and below 2% for larger height differences (≳300 m). Our validation data cover solar elevations ranging from 3.7 to 42.2°, and we observed smaller absolute errors at lower solar elevations. This novel information can be valuable for studying surface elevation changes in present and old imagery and extending glacier volume variation time-series.

Flares and their echoes can help distinguish photon rings from black holes with space-Earth very long baseline interferometry

Photon rings near the edge of a black hole shadow are supposed to be a unique tool to validate general relativity and provide reliable measurements of principal black hole parameters: spin and mass. Such measurements are possible though only for nearby supermassive black holes (SMBH) with space-Earth very long baseline interferometry (S-VLBI) in the submillimeter wavelength range. For subrings to be distinguished S-EVLBI observations with long baselines at the Lagrangian Sun-Earth L2 libration point are needed. However, the average fluxes of nearby SMBH: Sagittarius ${\mathrm{A}}^{*}$ (Sgr ${\mathrm{A}}^{*}$) and $\mathrm{M}{87}^{*}\ensuremath{-}{F}_{\ensuremath{\nu}}\ensuremath{\sim}1\text{ }\text{ }\mathrm{Jy}$, are still insufficient to detect the signal from the photon rings with even such long baselines. We argue that only manifestations of flares in the submillimeter waveband in their accretion disks can reveal observable signals from the photon rings with the S-EVLBI at L2. Such observations will become possible within the planned joint program of the Event Horizon Telescope (EHT) and Millimetron Space Observatory (MSO), and within the planned next generation EHT (ngEHT) project. Two different observational tests for photons rings are discussed. The first one involves observations of a time series of responds from subsequent subrings as can be seen in a 1D visibility function within the join EHT-MSO configuration, the second one---measurements of an increase of the angle between subsequent subrings in the 2D VLBI image which can be obtained within the ngEHT project.

Research on edge‐guided disambiguation module for shadow detection

In shadow detection area, deep learning-based methods have achieved outstanding performance. However, the potential ambiguity in shadow images is still the main reason for the poor performance of the algorithms. By analysing the relationships among shadow edges, shadow false-positive information, and shadow areas, an edge-guided disambiguation module for better shadow detection performance is proposed. The edge-guided disambiguation module consists two parts: 1) the shadow edge feature is used to guide the comprehensive shadow area feature generation of different resolution. 2) The shadow false-positive features is applied to reduce ambiguity in the comprehensive shadow area features. In this way, the missed detections is added in the first step and the false detections is reduced in the second step. More accurate detection results is obtained. The comprehensive experiments are proceeded on the three public shadow detection datasets: SBU, UCF, and ISTD. The experimental results demonstrated the effectiveness and robustness of the proposed method.

Wave shadow zones as a primary control of storm erosion and recovery on embayed beaches

Waves interact with headlands on embayed beaches through refraction, diffraction and attenuation and can create alongshore gradients in wave energy and beach response. This energy gradient shifts alongshore with changes in wave direction, especially during storms when waves may come from a direction different to average conditions. To better predict embayed beach storm responses, improved identification of exposed and headland shadowed zones is needed. Here we present a generalised geometric approach to quantify the alongshore distance shadowed by headlands, called the shadow edge (Ysh), differentiating exposed and headland shadowed zones. Our approach uses headland geometry collected from readily available imagery, combined with measured or modelled storm wave direction, reducing the reliance on complex nearshore wave models derived from relatively scarce bathymetric data. We use monthly topographic beach surveys (2015–2019) at nine embayed beaches in SE Australia to investigate the impacts of headland and embayment geometry relative to storm wave direction (i.e., exposure). Further, beach responses were compared to erosion and recovery rates across different embayment geometries with varying levels of geological control on beach morphodynamics. We found that storm frequency, headland shadowing and embayment geometry control the alongshore magnitude of beach erosion and recovery rates. Mean subaerial beach volume losses to six high-energy storms were on average 3.5 times higher in exposed zones (42.7 ± 40.7 m3/m) than headland shadowed zones (12.1 ± 23.6 m3/m). Our geometric approach provides a simple alternative to predict headland influence on beach morphodynamics. The approach can be applied at a regional scale and could be integrated into early warning systems to predict coastal erosion.