Shaded Images Research Articles

Lineaments Mapping Using Shaded Relief Images Derived from Landsat-9 Imagery at Diyala Government, NE Iraq

The Landsat-9 satellite images with a 30 m resolution at Diyala government was evaluated using the shaded relief technique to extract and map the surface lineaments. The processing of the azimuth and latitude method results in four shaded images (315\45, 200\50, 50\90, and 110\60). The optimal shaded relief (110\60) was used to extract the lineaments using the manual process. The results indicated that the primary structural trend of the lineaments was in the NW-SE direction, with a minor presence of NE-SW trends. It can be concluded that these structures are closely associated with the Makhoul-Hamrin trend in the Low Folded Zone (LFZ) influenced by the prevailing tectonic regime. In contrast, the lineaments structural trend of NE-SW would result from the neotectonics regime that linked to the listric fault system. On the other hand, the southeastern part of the study area shows no surface features due to the stress of the tectonic regime that affected the Tertiary deposits.

A tutorial on the physics of light and image shading.

This paper provides an overview of the many different ways that light interacts with surfaces in the natural environment to provide useful information for visual perception. It begins with a discussion of how the concept of light has evolved over the course of human history. It then considers a wide variety of optical phenomena including Lambert's laws of illumination, the effects of microscopic surface structure on patterns of reflection, the bidirectional reflectance distribution function, the refraction of transmitted light, chromatic dispersion, thin film interference, sub-surface scattering, the Fresnel effects, indirect illumination from multiple reflections, caustics, and the structure of the light field. The primary goal of this discussion is to provide the necessary background information to help students and young researchers more easily understand the scientific literature on the perception of 3D shape and material properties from patterns of image shading.

The shading isophotes: Model and methods for Lambertian planes and a point light

Structure-from-Motion (SfM) and Shape-from-Shading (SfS) are complementary classical approaches to 3D vision. Broadly speaking, SfM exploits geometric primitives from textured surfaces and SfS exploits pixel intensity from the shading image. We propose an approach that exploits virtual geometric primitives extracted from the shading image, namely the level-sets, which we name shading isophotes. Our approach thus combines the strength of geometric reasoning with the rich shading information. We focus on the case of untextured Lambertian planes of unknown albedo lit by an unknown Point Light Source (PLS) of unknown intensity. We derive a comprehensive geometric model showing that the unknown scene parameters are in general all recoverable from a single image of at least two planes. We propose computational methods to detect the isophotes, to reconstruct the scene parameters in closed-form and to refine the results densely using pixel intensity. Our methods thus estimate light source, plane pose and camera pose parameters for untextured planes, which cannot be achieved by the existing approaches. We evaluate our model and methods on synthetic and real images.

3sG: Three‐stage guidance for indoor human action recognition

AbstractInference using skeleton to steer RGB videos is applicable to fine‐grained activities in indoor human action recognition (IHAR). However, existing methods that explore only spatial alignment are prone to bias, resulting in limited performance. The authors propose a Three‐stage Guidance (3sG) framework, leveraging skeleton knowledge to promote RGB in three stages. First, a soft shading image is proposed for alleviating background noise in videos, allowing the network to directly focus more on the motion region. Second, the authors propose to extract RGB frames of interest to reduce the computational effort. Furthermore, to explore more fully the complementary information between skeletons and RGB, the skeleton is coupled to the frame representation in a different spatial–temporal sharing pattern. Third, the global skeleton and skeleton‐guided RGB features are fed into the shared classifiers, which approximate the logit distributions of the two to enhance the performance in RGB unimodal. Finally, a fusion strategy that utilizes two learnable parameters to adaptively integrate the skeleton with the RGB is proposed. 3sG outperforms the state‐of‐the‐art results on the Toyota Smarthome dataset while it is more efficient than similar methods on the NTU RGB+D dataset.

Cognitive Inspired & Computationally-Intelligent Early Melanoma Detection Using Feature Analysis Techniques

Melanoma is the most malignant kind of skin cancer, and it is responsible for the majority of deaths caused by skin cancer. However, this can be easily addressed by reverting to the standard method of damage removal if it is discovered in a timely manner. In this view, it is of the utmost importance to develop procedures for the early and reliable identification of melanomas. Since images for melanoma diagnosis are recorded in the clinic as an epiluminance image using a specific kind of equipment, the technique is machine-dependent. We make use of a method for managing images as well as a high-resolution shading image of a skin ulcer captured by a high-resolution camera or other device. Instead of depending on the epilumination images that are produced by the emergency equipment, the initial task that needs to be done in the present research is to capture high-resolution photographs of the skin injuries. The category limit will be determined with the use of machine learning, and then highlight extraction will take place. The research focuses on clinical photos obtained from fast cameras that were taken of individuals suffering from skin cancer. The problem of uneven illumination was kept at a strategic distance by the use of medial separation and pre-processing using the histogram. Utilisation of a brand new image segmentation method called "Otsu" for the extraction of sores. The extraction of ABCD (Asymmetry, Border, Color, and Dimension) involves the use of innovative methodologies as well as the Total Dermoscopic Value in order to characterise the weight coefficients. A solution to the problem of upgrading the categorization error that is based on machine learning. The parameters that are taken into consideration for evaluating the proposed model are senstivity, specificity, precision and accuracy and the results obtained are 1, 0.93, 0.93, and 0.93 respectively. It has been observed that the proposed model performs better as compared to the ones present in existing literature.

A Modified Technique of Hybrid Multiobjective Genetic Algorithm for Image Fusion

Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g., panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however nownot recognized whereasmultispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the Genetic Algorithm (GA). Comparing the results obtained by these methods, the output image by the GA is clearer. The feature of the resultant image is verified through parameters such as Root Mean Square Error (RMSE), peak signal to noise ratio, Mutual Information (MI), and Spatial Frequency (SF). In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a GA is used for image fusion. This is again compared with GA results. The same performance parameters are used. And it is observed that the Hybrid Genetic Algorithm (HGA) is superior tothe AG. Here the only RMSE parameter is considered under the fitness function of the GA so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the GA then all parameters using a HGA will give better performance. This method is called a Hybrid Multiobjective Genetic Algorithm (HMOGA) [14].

Perception of surface concavities and convexities from patterns of image shading

Perception of surface concavities and convexities from patterns of image shading

Anthropogenic Landforms Derived from LiDAR Data in the Woodlands near Kotlarnia (Koźle Basin, Poland).

Unlike farmland or urban areas, forests have long been regarded as environments that favour the preservation of valuable geological and historical sites. However, due to invasive forestry methods, the implementation of large investment projects and the development of mining, they are increasingly no longer safe spaces for the relics of human activities recorded in landforms. Data collection, including using LiDAR technology, presents an opportunity to preserve knowledge about these landforms. Through the analysis of shaded images of a 37-hectare woodland area near Kotlarnia, landforms of various ages documenting 21 different human activities were identified, including remnants of reforestation activities, the expansion and modification of hydraulic structures and road infrastructure, charcoal burning and tar distilling, exploitation of mineral resources and military activities. The results of the remote sensing work were verified in the field.

A temporal-spatio detection model and contrast calculation method on new active sky screen with high-power laser

To improve the detection performance of sky screen under different background environments, this paper invents an active sky screen by using high-power laser light source to form active detection screen, which makes use of the advantages of the sky screen detection receiving light path and photoelectric detection sensor made of new materials. Based on the design method of active sky screen, the temporal-spatio detection model of active sky screen is established by combining the distribution characteristics of laser light sources in any position and detection screen parameters; the surface of projectile is decomposed into a multiple small units area, and the calculation function of projectile’s reflected laser power is deduced by using the superposition principle of the reflected laser energy of multiple small units area and four parameter BRDF. According to the features of blue sky and cloudy background, the different of the projectile’s reflected laser energy and the shading image energy of projectile itself is used to evaluate contrast and derive the new contrast calculation function of active sky screen. Through experimental testing and quantitative calculation analysis, the proposed computational model and contrast function are validated.

Three-dimensional holographic communication system for the metaverse

We demonstrate a real-time three-dimensional communication system integrating the capture, hologram generation, transmission and display. The point cloud of a 3D scene is captured by a structured light camera and a light field camera. Phase-only computer-generated holograms are calculated by the layer-based angular spectrum method based on rendered depth images and shading images. The dynamic reconstruction of phase-only holograms is realized by a dynamic 4K liquid crystal spatial light modulator. The framerate of 20 fps and the latency of 0.1 s are realized, which can be satisfied by the 5G network. We suggest that this system is a potential solution to telepresence in the metaverse.

Parallel transmission 2D RARE imaging at 7T with transmit field inhomogeneity mitigation and local SAR control

PurposeWe develop and test a parallel transmit (pTx) pulse design framework to mitigate transmit field inhomogeneity with control of local specific absorption rate (SAR) in 2D rapid acquisition with relaxation enhancement (RARE) imaging at 7T. MethodsWe design large flip angle RF pulses with explicit local SAR constraints by numerical simulation of the Bloch equations. Parallel computation and analytical expressions for the Jacobian and the Hessian matrices are employed to reduce pulse design time. The refocusing-excitation “spokes” pulse pairs are designed to satisfy the Carr-Purcell-Meiboom-Gill (CPMG) condition using a combined magnitude least squares-least squares approach. ResultsIn a simulated dataset, the proposed approach reduced peak local SAR by up to 56% for the same level of refocusing uniformity error and reduced refocusing uniformity error by up to 59% (from 32% to 7%) for the same level of peak local SAR compared to the circularly polarized birdcage mode of the pTx array. Using explicit local SAR constraints also reduced peak local SAR by up to 46% compared to an RF peak power constrained design. The excitation and refocusing uniformity error were reduced from 20%–33% to 4%–6% in single slice phantom experiments. Phantom experiments demonstrated good agreement between the simulated excitation and refocusing uniformity profiles and experimental image shading. ConclusionPTx-designed excitation and refocusing CPMG pulse pairs can mitigate transmit field inhomogeneity in the 2D RARE sequence. Moreover, local SAR can be decreased significantly using pTx, potentially leading to better slice coverage, enabling larger flip angles or faster imaging.

Image Fusion by a Hybrid Multiobjective Genetic Algorithm Technique

Sensors used in image acquisition. This sensor technology is going on upgrading as per user need or as per need of an application. Multiple sensors collect the information of their respective wavelength band. But one sensor is not sufficient to acquire the complete information of one scene. To gain the overall data of one part, it becomes essential to cartel the images from multiple sources. This is achieved through merging. It is the method of merging the data from dissimilar input sources to create a more informative image compared with an image from a single input source. These are multisensor photos e.g. panchromatic and multispectral images. The first image offers spatial records whereas the lateral image offers spectral data. Through visible inspections, the panchromatic photo is clearer than a multispectral photo however the grey shade image is. Articles are greater clear however now not recognized whereas multispectral picture displays one of a kind shades however performing distortion. So comparing the characteristics of these two images, the resultant image is greater explanatory than these enter images. Fusion is done using different transform methods as well as the genetic algorithm. Comparing the results obtained by these methods, the output image by the genetic algorithm is clearer. The feature of the resultant image is verified through parameters such as root mean square error, peak signal to noise ratio, mutual information, and spatial frequency. In the subjective analysis, some transform techniques also giving exact fused images. The hybrid approach combines the transform technique and a genetic algorithm is used for image fusion. This is again compared with genetic algorithm results. The same performance parameters are used. And it is observed that the hybrid genetic algorithm is superior to the genetic algorithm. Here the only root means square error parameter is considered under the fitness function of the genetic algorithm so only this parameter is far better than the remaining parameters. If we consider all parameters in the fitness function of the genetic algorithm then all parameters using a hybrid genetic algorithm will give better performance. This method is called a hybrid multiobjective genetic algorithm.

Portrait imaging relighting system based on a simplified photometric stereo method.

This study proposes a portrait image relighting system based on a simplified photometric stereo method. The system, comprising a controllable digital single lens reflex camera and five polarized flashlights, can obtain a color shade-less image and synthesize a normal map from shaded images. When calibrating the photometric stereo, the normal map is taken as a linear combination of shaded images and clamped with respect to specific normal directions on a white-coated sphere. The relit images were generated through inverse rendering in a predefined virtual environment. To evaluate personal preference, 24 adult subjects were recruited to conduct subjective assessments comparing the deep portrait relighting method results. From experiments regarding different scenarios, we concluded that the proposed system based on a simplified photometric stereo performs acceptably for relighting portrait images.

3D Reconstruction of Non-Lambertian Surfaces by Perspective Shape-From-Shading With Fast Viscosity Solution

Shape-from-shading (SFS) is an important method to reconstruct three-dimensional (3D) shape of a surface in photometry and computer vision. Lambertian surface reflectance and orthographic camera projection are two fundamental assumptions which generally result in undesirable reconstructed results since inaccurate imaging model is adopted. In this paper, we propose a new fast 3D shape reconstruction approach via the SFS method relaxing the two assumptions. To this end, the Oren-Nayar reflectance and perspective projection models are used to establish an image irradiance equation which depicts the relationship between the 3D shape of non-Lambertian surfaces and its corresponding two-dimensional (2D) shading image. Considering the light attenuation of the near point source, the image irradiance equation is transformed into a static Hamilton-Jacobi partial differential equation (PDE) by solving a quadratic equation. The viscosity solution of the resultant Hamilton-Jacobi PDE is approximated by using optimal control theory and iterative fast marching method starting from a viscosity supersolution. The performance of the proposed approach is evaluated on both synthetic and real-world images and the experimental results demonstrate that the proposed approach is accurate and fast.

A color image analysis tool to help users choose a makeup foundation color

This paper presents an approach to predict the color of skin-with-foundation based on a no makeup selfie image and a foundation shade image. Our approach first calibrates the image with the help of the color checker target, and then trains a supervised-learning model to predict the skin color. In the calibration stage, We propose to use three different transformation matrices to map the device dependent RGB response to the reference CIE XYZ space. In so doing, color correction error can be minimized. We then compute the average value of the region of interest in the calibrated images, and feed them to the prediction model. We explored both the linear regression and support vector regression models. Cross-validation results show that both models can accurately make the prediction.

Customized Radiofrequency Phased-Array Coil Combining Transmit-Only, Receive-Only, and Transmit/Receive Coils for Magnetic Resonance Imaging of Visual Cortex at 7 Tesla

Magnetic resonance imaging (MRI) using an ultra-high magnetic field (7 Tesla) enables detailed and non-invasive studies of the function and anatomy of the human visual cortex, which is the brain region responsible for visual signal processing. However, 7T human MRI often suffers from image shading in the occipital region due to the radiofrequency (RF) wave propagation effect. Dedicated visual cortex coils, on the other hand, often lack the capability to visualize the whole brain which is necessary for image registration. We propose a novel RF coil structure in which a 2-channel transmit and receive (TRx) coil is grafted onto the frontal part of a multi-channel transmit-only/receive-only (TORO, 4Tx/14Rx) visual cortex coil. This coil was tested for high-resolution functional MRI with an in-plane resolution of 0.5 mm. The results showed that the proposed coil achieved a higher ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times 2.5$ </tex-math></inline-formula> ) temporal signal-to-noise ratio (tSNR) in functional imaging of the visual cortex area than that of a commercial 7T whole-head coil. The added 2-channel TRx elements allowed whole-brain edge images to be acquired, enabling successful brain segmentation and atlas registration without the need for a second scan using a whole-head coil. The proposed coil structure can be useful for high-resolution visual functional MRI at very high magnetic fields due to its sensitivity, open geometry, and compatibility with the standard image processing workflow.

Correcting remote-sensed shaded image with urban surface radiative transfer model

Shaded areas reduce the quality of digital images, bring many difficulties to image interpretation, especially in the high-resolution remote-sensed images. In order to improve the urban application capability, shadow correction should be performed. In this article, a new methodology for shadow correction was proposed applying the Urban Solar Radiative Transfer (USRT)11USRT: Urban Solar Radiative Transfer. model, which was a shadow correction scheme based on physical model. The sky view factor (SVF)22SVF: Sky view factor. was selected as one factor to quantify the morphological and structural characteristics of urban underlying surface, and then each radiation component was expressed quantitatively at pixel-level scale. Finally, the shadow correction was performed by calculating the actual reflectance of the urban materials in shadow areas. To validate the proposed scheme, three different spatial-resolution remote-sensed images were applied to perform shadow correction, including samples of Landsat-8, Sentinel-2, and Worldview-3 satellite image data. The results showed the following: (1) The shadow correction method can better improve the reflectance of the shadow area to the level of the illuminated area. There was a positive effect on the spectral reconstruction of different materials in the shadow areas after correction, and the spectral curves of corrected results coincided with the illuminated areas. (2) With the increase of spatial resolution, there was an increasing trend in the accuracy of shadow correction. In fact, the difference in the reflectance between the shadow corrected region and the illumination region decreased with the spatial resolution increasing. For example, after USRT shading correction, the rRMSE values of vegetation in the Landsat-8, Sentinel-2, and Worldview-3 images was 20.4%, 13.2%, and 9.8%, respectively. Generally, this proposed scheme can restore the spectral characteristics in the shadow areas and improve image quality, and there is a good application prospect in the field of high-resolution remote-sensed image processing.

Demarcation of Coastal Dune Morphology and Dune Patterns using Geospatial Models: A Case Study from Manapad Coastal Stretch, Tamil Nadu, South India

Abstract Coastal sand dunes are the dynamic depositional landform which are formed near the coast, due to aeolian activity. Coastal sand dune acts as a natural barrier against coastal erosion hence its periodic monitoring is essential. Modification of this landform due to anthropogenic activities was observed in the coastal stretch towards the south of Tamil Nadu, even though these dune ecosystems were meant to be conserved and preserved in the coastal zone regulation notification. Present study, focuses on finding key techniques required to demarcate the morphological parameters such as ridge line, crest portion, toe, dune orientation, and its pattern from the open source ALOS World 3D (AW3D) DEM for periodic monitoring of coastal dune ecosystems. Topographic models such as hill shading, slope, aspect, curvature, and hybrid techniques were performed. The outcomes obtained from topographic models were validated with spatial profiling and field survey. From the study it is observed that, hybrid techniques particularly slope overlaid on hill shade, slope, gaussian stretched hill shade image, and spatial profiling are the key techniques to bring out the morphological variations and the parabolic dune patterns, which were hitherto not reported in earlier literatures. Gaussian stretched hill shade image enhanced the micro landforms which were difficult to extract from normal hill shading. The slope image of the study is found to be more informative in exhibiting the minor dunes and patterns compared to shaded relief image. The ridge portion of the dune, crest and limb portion and coastal plains were differentiated based on the slope value ranges. The hybrid image formed by overlaying slope and curvature data on hill-shaded image is more informative in providing most of the parabolic patterns formed by micro and meso scale dunes. Spatial profile could be used as an additional tool to validate the information’s derived from topographic models, wherever there is limitation in field accessibility.

An illumination direction estimation algorithm of face images

Illumination is one of the important factors affecting the accuracy of face recognition. This paper proposes an illumination direction estimation algorithm of face images and it can be applied to the non-frontal face image. In order to eliminate the influence of face’s albedo, the algorithm use SfSNet to get the shading image. The algorithm divides the face region into eight regions by using five landmarks in face and estimate the illumination direction by analyzing the average brightness of each regions. Experiment on YaleB dataset shows that the algorithm can estimate the illumination direction correctly in most cases.

Line Drawing Extraction from Cartoons Using a Conditional Generative Adversarial Network

Recently, three-dimensional (3D) content used in various fields has attracted attention owing to the development of virtual reality and augmented reality technologies. To produce 3D content, we need to model the objects as vertices. However, high-quality modeling is time-consuming and costly. Drawing-based modeling is a technique that shortens the time required for modeling. It refers to creating a 3D model based on a user’s line drawing, which is a 3D feature represented by two-dimensional (2D) lines. The extracted line drawing provides information about a 3D model in the 2D space. It is sometimes necessary to generate a line drawing from a 2D cartoon image to represent the 3D information of a 2D cartoon image. The extraction of consistent line drawings from 2D cartoons is difficult because the styles and techniques differ depending on the designer who produces the 2D cartoons. Therefore, it is necessary to extract line drawings that show the geometric characteristics well in 2D cartoon shapes of various styles. This paper proposes a method for automatically extracting line drawings. The 2D cartoon shading image and line drawings are learned using a conditional generative adversarial network model, which outputs the line drawings of the cartoon artwork. The experimental results show that the proposed method can obtain line drawings representing the 3D geometric characteristics with a 2D line when a 2D cartoon painting is used as the input.