Texture Mapping Research Articles

Research and application of 3D visualization and Internet of Things technology in urban land use efficiency management

Land is the basis for human beings to engage in economic and social activities, but the continuous acceleration of the process of economic development and urban expansion has brought about a series of ecological and urban problems. Therefore, the simulation of urban spatial expansion is very important for the rational planning of urban land and the promotion of sustainable urban development. The three-dimensional visualization of urban land allows users to have a perceptual understanding of urban construction, and enables decision makers, designers and users to have a more vivid and rational understanding of the current state of urban construction and planning and design blueprints. Based on this, this article combines three-dimensional imaging and Internet of Things technology to carry out the research on urban land utilization. First of all, this article discusses image modeling techniques such as texture mapping and panoramas, and applies these techniques to the model reconstruction of complex urban land. The model effectively combines graphics-based and image-based model reconstruction technology to construct a highly realistic three-dimensional urban landscape. Secondly, this article combines the Internet of Things and wavelet analysis to design an Internet of Things architecture for urban land use efficiency management. Finally, on the premise of taking into account the important results and the status quo of urban development, the countermeasures and suggestions for increasing the urban land use rate are put forward.

Automobile Driver Fatigue Detection Method Based on Facial Image Recognition under Single Sample Condition

Under the existing single sample condition, the fatigue detection method of an automobile driver has some problems, such as an improper camera calibration method, image denoising beyond the controllable range, low fatigue detection accuracy, and unsatisfactory effect. A fatigue detection method for drivers based on face recognition under a single sample condition is proposed. Firstly, the camera is calibrated by Zhang Zhengyou’s calibration method. The optimal camera parameters were calculated by linear simulation analysis, and the image was nonlinear refined by the maximum likelihood method. Then, the corrected image effect is enhanced, and the scale parameter gap in the MSRCR image enhancement method is adjusted to the minimum. The detection efficiency is improved by a symmetric algorithm. Finally, the texture mapping technology is used to enhance the authenticity of the enhanced image, and the face image recognition is carried out. The constraint conditions of fatigue detection are established, and the fatigue detection of car drivers under the condition of a single sample is completed. Experimental results show that the proposed method has a good overall detection effect: the fatigue detection accuracy is 20% higher than that of the traditional method, and the average detection time is over 30%. Compared with the traditional fatigue detection methods, this method has obvious advantages, can effectively extract more useful information from the image, and has strong applicability.

Design of 3D Scene Visual Communication Modeling Based on Virtual Reality Graphics Rendering Framework

Virtual reality is a kind of computer simulation system which can create virtual environment. People can interact with the three-dimensional environment generated by computer. In this paper, the real-time computer graphics technology, three-dimensional modeling technology and binocular stereo vision technology in virtual reality technology are studied, and the binocular stereo vision optical system is designed. This paper studies the visual communication modeling design of 3D scene under the framework of virtual reality graphics rendering. In this paper, we design and make a three-dimensional model, and develop a virtual indoor scene application. In this paper, the 3D model is used to build the scene, and several commonly used rendering technologies such as texture mapping, material, lighting calculation, transparent effect and shadow calculation in unity engine are used to render the scene. The experimental results show that this method can improve the design efficiency and the accuracy of design matching.

Adaptive coding unit size convolutional neural network for fast 3D-HEVC depth map intracoding

The advanced three-dimensional extension of high-efficiency video coding (3D-HEVC) is the latest coding standard for 3D video. The coding of the depth map for 3D-HEVC is very time-consuming. With the development of deep learning, it has become feasible to employ convolutional neural networks (CNNs) to predict the coding unit (CU) division of the depth map. However, there are three types of CU sizes: 64, 32, and 16, which makes it difficult to unify the model. The features of the depth map are very different from the texture map. In view of the aforementioned problems, we propose an adaptive CU size CNNs for fast 3D-HEVC depth map intracoding. We first employ spatial pyramid pooling to fully extract the features of the three types of CUs. Then, we apply the nonlocal self-attention mechanism to make it suitable for depth maps. Compared with the 3D-HEVC reference algorithm, the proposed network reduces the coding time by an average of 35.7%, while the quality degradation of the synthesized virtual view is negligible.

Digital mapping of soil texture in ecoforest polygons in Quebec, Canada.

Texture strongly influences the soil’s fundamental functions in forest ecosystems. In response to the growing demand for information on soil properties for environmental modeling, more and more studies have been conducted over the past decade to assess the spatial variability of soil properties on a regional to global scale. These investigations rely on the acquisition and compilation of numerous soil field records and on the development of statistical methods and technology. Here, we used random forest machine learning algorithms to model and map particle size composition in ecoforest polygons for the entire area of managed forests in the province of Quebec, Canada. We compiled archived laboratory analyses of 29,570 mineral soil samples (17,901 sites) and a set of 33 covariates, including 22 variables related to climate, five related to soil characteristics, three to spatial position or spatial context, two to relief and topography, and one to vegetation. After five repeats of 5-fold cross-validation, results show that models that include two functionally independent values regarding particle size composition explain 60%, 34%, and 78% of the variance in sand, silt and clay fractions, respectively, with mean absolute errors ranging from 4.0% for the clay fraction to 9.5% for the sand fraction. The most important model variables are those observed in the field and those interpreted from aerial photography regarding soil characteristics, followed by those regarding elevation and climate. Our results compare favorably with those of previous soil texture mapping studies for the same territory, in which particle size composition was modeled mainly from rasterized climatic and topographic covariates. The map we provide should meet the needs of provincial forest managers, as it is compatible with the ecoforest map that constitutes the basis of information for forest management in Quebec, Canada.

Integrated Method for Road Extraction: Deep Convolutional Neural Network Based on Shape Features and Images

As a significant application in RS images, road detection is still a challenging task due to the presence of complex surroundings and multiple false objects. To achieve a satisfying result, a road detection method based on residual learning and saliency sampling is developed in this paper. First, a multistrapdown module is designed with double residual learning blocks that have low computational complexity and time consumption. Second, to improve the classification accuracy and learning ability of the method, a saliency sampling set is established by fusing brightness, orientation and texture maps. The sampling set is imported and merged into the model through the pooling layer. Finally, the cross entropy loss function is carried out in a Softmax classifier. Extensive experiments show that our proposed integrated method is much better than the state-of-the art methods in detection accuracy.

Development of starch texture rheological maps through empirical modeling of starch swelling behavior

Starches are commonly used in sauces, dressings, dairy products, bakery items, confections, and snacks due to their wide range of functional properties. However, using starches to deliver palatable textures is often challenging because multiple texture and rheological attributes have to be balanced simultaneously. Textural properties of even simple starch-thickened foods can vary in surprising ways with changes in starch swelling volume and concentration. Thus, we have modeled the swelling behavior of starch thickeners to develop rheological maps of starch texture as a predictive tool for formulation of starch-thickened foods. Predictive equations for starch suspension viscosity, elastic modulus, phase angle, critical strain and shear thinning index were developed as a function of starch swelling volume and concentration, and these equations showed good fit with experimental data (R2 ≥ 0.89). The developed starch texture maps overlay cutoff lines of various texture behaviors over plots of either viscosity or elastic modulus as a function of starch swelling volume and concentration, indicating zones of various rheologically-influenced texture properties such as shear-thinning and significant elastic behavior. The starch texture maps provide a powerful predictive and visual tool that enables targeted, efficient development of texture solutions for starch-thickened foods.

Image-to-Video Generation via 3D Facial Dynamics

We present a versatile model, FaceAnime, for various video generation tasks from still images. Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks. However, the generated face images usually suffer from quality loss, image distortion, identity change, and expression mismatching due to the weak representation capacity of the facial landmarks. In this paper, we propose to “imagine” a face video from a single face image according to the reconstructed 3D face dynamics, aiming to generate a realistic and identity-preserving face video, with precisely predicted pose and facial expression. The 3D dynamics reveal changes of the facial expression and motion, and can serve as a strong prior knowledge for guiding highly realistic face video generation. In particular, we explore face video prediction and exploit a well-designed 3D dynamic prediction network to predict a 3D dynamic sequence for a single face image. The 3D dynamics are then further rendered by the sparse texture mapping algorithm to recover structural details and sparse textures for generating face frames. Our model is versatile for various AR/VR and entertainment applications, such as face video retargeting and face video prediction. Superior experimental results have well demonstrated its effectiveness in generating high-fidelity, identity-preserving, and visually pleasant face video clips from a single source face image.

USING SPATIAL DATA TO SIMULATE RAIN RUNOFF

The paper presents the main stages of spatial data preparation for computer models of runoff formation by the example of the Charysh River basin. DEM was employed to construct a system of sub-basins and a hydrological graph as well as to calculate morphometric characteristics of its elements. Sources of data on vegetation and mechanical composition of soils are given Using the Harmonized World Soil Database and the soil map of Altai Krai, a map of soil texture (grain-size composition) of the Charysh River basin was created. Two ways of precipitation accounting (the weather station data; the Persiann-CDR data set) were compared. Calculations of runoff from the the Charysh catchment were made for two models.

Evaluation of CH4 Emission in Two Paddy Field Areas, Khonkaen and Ayutthaya, in Thailand

It is well known that submerged soils emit high levels of methane (CH4) due to oxygen deprivation and free iron oxide causing a quick reduction. However, there are other soil properties that control the reduction processes in soil, especially the amount of soil organic carbon (SOC). This study aimed to investigate the major factors controlling CH4 production potential (CH4PP) in Thai paddy fields. Two provinces, Ayutthaya, a clay soil region, and Khonkaen, a sandy soil region, were selected to represent a wide range of soil textures. Soil characteristic analysis pre- and post-incubation, and weekly gas detection in an incubation experiment over two months, was conducted. Stepwise multiple regression analysis was employed to analyze major soil factors controlling CH4PP. For the regional prediction of CH4PP, a map dataset of Ayutthaya and Khonkaen by the Land Development Department, Thailand, and a soil texture map (with intersected point data using the soil property map in ArcGIS) by OpenLandMap, were used. CH4PP was correlated with 1:10 pH, Fe2+, and water-soluble organic carbon (WSOC) measured after incubation. Although CH4PP showed no significant correlation with any soil properties measured before incubation, CH4PP was correlated with SOC, 1:10 electrical conductivity (EC), exchangeable ammonium (ExNH4), and sand content. It was thought that SOC and ExNH4 were related to organic matter decomposition, 1:10 EC was related to SO42− reduction and sand content was related to free oxides. Predicted regional CH4PP was similar in Ayutthaya and Khonkaen, although SOC, ExNH4 and 1:10 EC was higher, and sand content was lower in Ayutthaya than in Khonkaen. In both regions, the distribution of CH4PP corresponded to SOC, and CH4PP was lower with lower sand content and higher 1:10 EC. In clayey Ayutthaya, higher CH4PP was observed in the area with higher ExNH4. This indicates that soil properties other than soil texture and SOC influence CH4PP in the paddy fields in Thailand.

Robust Texture Mapping Using RGB-D Cameras.

The creation of a textured 3D mesh from a set of RGD-D images often results in textured meshes that yield unappealing visual artifacts. The main cause is the misalignments between the RGB-D images due to inaccurate camera pose estimations. While there are many works that focus on improving those estimates, the fact is that this is a cumbersome problem, in particular due to the accumulation of pose estimation errors. In this work, we conjecture that camera poses estimation methodologies will always display non-neglectable errors. Hence, the need for more robust texture mapping methodologies, capable of producing quality textures even in considerable camera misalignments scenarios. To this end, we argue that use of the depth data from RGB-D images can be an invaluable help to confer such robustness to the texture mapping process. Results show that the complete texture mapping procedure proposed in this paper is able to significantly improve the quality of the produced textured 3D meshes.

Spatiotemporal Texture Reconstruction for Dynamic Objects Using a Single RGB‐D Camera

AbstractThis paper presents an effective method for generating a spatiotemporal (time‐varying) texture map for a dynamic object using a single RGB‐D camera. The input of our framework is a 3D template model and an RGB‐D image sequence. Since there are invisible areas of the object at a frame in a single‐camera setup, textures of such areas need to be borrowed from other frames. We formulate the problem as an MRF optimization and define cost functions to reconstruct a plausible spatiotemporal texture for a dynamic object. Experimental results demonstrate that our spatiotemporal textures can reproduce the active appearances of captured objects better than approaches using a single texture map.

Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value

• Urban areas 3D digitisation using multispectral aerial imagery • Experimental low-cost multispectral camera for drone development • Supervised machine learning methods for 3D mesh segmentation using multispectral data • Performance evaluation of the proposed methods on a large scale case study • Web-based visualisation and annotation tool for segmentation fine-tuning Disaster risk management of movable and immovable cultural heritage is a highly significant research topic. In this work, we present a pipeline for 3D digitisation, segmentation and annotation of large scale urban areas in order to produce data that can be exploited in disaster management simulators (e.g fire spreading, crowd movement, firefighting training, evacuation planning, etc.). We have selected the old town of Xanthi (Greece) as a challenging case study. We developed a custom multispectral camera to be carried by a commercial drone. Using the structure from motion / multiview stereo (SFM/MVS) approach, we produced a 3D model of the urban area covering 0.5 k m 2 that is followed by a multilayer texture map which carries information from visible and near-infrared regions of the electromagnetic spectrum. We developed a set of machine learning approaches based on logistic regression, support vector machines and artificial neural networks that allow 3D model segmentation by exploiting not only morphological and structural features but also the multispectral behaviour of different material surfaces. We objectively evaluate the performance of the proposed segmentation approaches on six significant material-based classes (cobbled-roads granite kilns, building walls, ceramic roof-tiles, low-vegetation, high-vegetation and metal surfaces) that are used in simulating fire propagation and crowd movement. The experiments revealed that the segmentation accuracy can be enhanced by taking into consideration surface material multispectral properties as well as morphological features. A Web-based multi-user annotation tool complements our proposed pipeline by enabling further 3D model segmentation, fine tuning and semantics annotation (e.g. usage-based building classification and evacuation priorities, escape paths and gathering points).

폴리곤 메시의 정점 이동과 표면 텍스처 매핑에 노이즈 텍스처를 적용하는 쉐이더 기법

폭발, 마법 등 불특정한 시각효과를 구현하는데 파티클과 노이즈가 효과적으로 사용된다. 파티클은 자유롭게 제작할 수 있지만, 많이 사용될수록 CPU/GPU 사용률이 높아진다. 본 논문에서는 이러한 단점을 극복하기 위해 변경이 어렵지만, 정해진 자원을 소모하는 폴리곤 메시를 활용해 CPU/GPU 사용률을 줄인다. 그리고 쉐이더를 활용해 폴리곤 메시의 정점과 표면 텍스처 매핑에 불특정한 패턴을 가진 노이즈 텍스처를 적용하여 시각효과를 구현한다. 실험결과, 프로파일러에서 쉐이더를 적용한 구체 폴리곤 메시가 2~4ms CPU, 1~2m_s GPU 사용률을 보였고 쉐이더를 활용해 불특정한 시각효과를 구현할 수 있음을 보였다.

Analysis of in vitro demineralised human enamel using multi-scale correlative optical and scanning electron microscopy, and high-resolution synchrotron wide-angle X-ray scattering

Enamel caries is a highly prevalent worldwide disease that involves the demineralisation of the outer tooth structure. In this study, we report the analysis of artificially demineralised human enamel sections (‘slices’) etched using lactic acid (2% v/v) in comparison with healthy enamel using correlative techniques of optical and electron microscopy, as well as scanning diffraction. Demineralisation of the enamel was characterised at the micron to sub-micron scale. The structure of the healthy enamel was investigated using Focused Ion Beam - Scanning Electron Microscopy (FIB-SEM) and compared with an etched sample to reveal their structural differences. Additional chemical analysis using energy-dispersive X-ray spectroscopy (EDS) was performed and a decrease in the Ca/P atomic % ratio was found in etched samples in comparison with healthy enamel, suggesting greater loss of calcium compared with phosphorus. Synchrotron wide-angle X-ray scattering (WAXS) was performed on the samples to reveal the differences in the diffraction patterns before and after etching in terms of lattice structure and preferred orientation (texture). Texture maps were extracted from diffraction analysis at 500 nm spatial resolution. These maps were correlated with the dimension of the enamel structure. The multi-scale correlative approach provided insights into the demineralisation-induced enamel structure alteration at a resolution approaching 500 nm.

OpenEar Image Data Enables Case Variation in High Fidelity Virtual Reality Ear Surgery.

Virtual reality (VR) simulation is an established option for temporal bone surgical training. Most VR simulators are based on computed tomography imaging, whereas the Visible Ear Simulator (VES) is based on high-fidelity cryosections of a single temporal bone specimen. Recently published OpenEar datasets combine cone-beam computed tomography (CBCT) and micro-slicing to achieve similar model quality. This study explores integration of OpenEar datasets into VES to enable case variation in simulation with implications for patient-specific modeling based on CBCT. The OpenEar dataset consists of segmented, coregistered, multimodal imaging sets of human temporal bones. We derived drillable bone segments from the dataset as well as triangulated surface models of critical structures such as facial nerve or dura. Realistic visualization was achieved using coloring from micro-slicing, custom tinting, and texture maps. Resulting models were validated by clinical experts. Six of the eight OpenEar datasets could be integrated in VES complete with instructional guides for various temporal bone surgical procedures. Resulting models were of high quality because of postprocessing steps taken to increase realism including colorization and imaging artifact removal. Bone artifacts were common in CBCT, resulting in dehiscences that most often could not be found in the ground truth micro-slicing data. New anatomy models are included in VES version 3.5 freeware and provide case variation for training which could help trainees to learn more quickly and transferably under variable practice conditions. The use of CBCT for VR simulation models without postprocessing results in bone artifacts, which should be considered when using clinical imaging for patient-specific simulation, surgical rehearsal, and planning.

464 3D head visualization for mapping and tracking dermatological conditions

Tracking or quantifying changes in hair and skin conditions over time is a task usually left up to the clinician: the affected areas (or images thereof) are either compared to the photos taken during past visits or a standard reference scale is used to compare the condition to a limited set of images or drawings to determine the severity level of the current state. While automated skin detection algorithms are gaining traction for binary diagnostic tasks or to segment areas of interest at a particular time-point, tracking changes automatically remains challenging due to the lack of easily accessible and affordable systems that can be used to quantitatively compare the changes in a standardized way. Focusing on tracking conditions that occur on the head, we present a new method to recover a clinically relevant model of a person’s head, defined as the complete 3D head surface in the absence of hair volume, starting only with a video taken from a single hand-held camera. Using techniques from computer vision, more specifically structure-from-motion and multi-view stereo, we determine first the shape of each person's head and then the alignment of the fitted 3D head for all video frames to recover texture mapping information, irrespective of the person's pose. This alignment is then used to map and visualize hair or skin information, for example disease quantifications, onto the head model for tracking changes in the condition over time. We demonstrate that our approach recovers a consistent geometry for varying head shapes, from videos taken by different people, with different smartphones, and in a variety of uncontrolled environments such as outdoors, living rooms, and hallways, and hence, can be applied to the clinical setting as well. Furthermore, we show how, once the head geometry for the person has been recovered, it can also be used in augmented reality compatible smartphones to guide image capture and map dermatological quantifications for integration in the clinic.

GIS, GPS And Dumpy Level Survey for Estimation Of Runoff By SCS-CN Method For VIIT Campus, Visakhapatnam

Globally majority of countries in the worldare worrying about declining of groundwater levels day by day. The one of the best solutions to improve the Groundwater levels is the artificial recharge of Groundwater. The estimation of runoff is very essential for the recharge of Groundwater. Exactly here a study has been madeto estimate the runoff by Soil Conservation Service Curve Number (SCS-CN) method for Vignan’s Institute of Information Technology (VIIT) campus, Visakhapatnam. In this study, the Land Use/Land cover (LU/LC)map of the study area has been extracted using GIS software from the Google Earth Remote Sensing imagery. The ground truth survey has been done using Global Position System (GPS) to update the missing data in the imagery. The Digital Elevation Model (DEM) of the study area has been generated using elevation data which was collected through dumpy level survey along with GPS. The soil texture map has been extracted from Soil Map which was bought from the National Bureau Soil Sciences (NBSS). The rainfall data of the study area has been collected from the nearest Gajuwaka Rain gauge station. The runoff of the study area has been estimated using the SCS-CN method by using the LU/LC, the Soil Texture map and rainfall data. In the present study the year wise total runoff varies from 30.352 mm to 209.445 mm of the study area. The average Annual rainfall of the study area is 1048.84 mm for the years 2009 to 2018. The average annual runoff volumeof the study area for 10 years is 16293.48 Cu.M.The DEM map has been used to derive the mini watersheds to locate the Artificial Recharge locations to recharge the runoff to the Groundwater.

SHADE AND TEXTURE MAPPING ON TEETH FOR PALAEOANTHROPOLOGICAL RECONSTRUCTIONS

Abstract. Over the past few years closer cooperation can be observed in various aspects of digital techniques in such disciplines as dentistry and anthropology. And in most cases that consists in imaging and image processing which results in obtaining 3D reconstructions. And indeed, they can significantly improve research and practice. Thus, depending on imaging technique and application, they can support CAD/CAM technology or precisely reconstruct morphology of invisible structures. However the currently presented study refers to technical aspects of shade and texture mapping, which is more aimed to obtain more realistic 3D reconstructions of palaeoanthropological material. Colour or shade matching has become an integral part of dental practice. It can be carried in a traditional manner though matching the tooth with conventional shade-guides, or, which is in line with the subjects of our study, by means of spectrophotometry. And the main procedures of shade detection have been performed by SpectroShade (MHT). Necessary attention has been paid to conditions influencing shade detection process with respect to the studied material teeth taken from the Bronze Age findings. Reconstructive techniques have traditionally been a scientific and practical part of palaeoanthropological research which is directed at appearance reconstruction. Though the leading part of this branch has been always aimed at analysis of skull morphology. In our time of rapidly developing digital techniques reconstructions have become to a large extent a matter of improvements of imaging and image processing techniques. Even though this doesnt directly refer to soft tissue reconstruction, it undoubtedly applies to dental reconstructions. And the current study presents improvements in reconstruction through combining imaging with shade and texture mapping on 3D models of teeth.

Instant Panoramic Texture Mapping with Semantic Object Matching for Large-Scale Urban Scene Reproduction.

This paper proposes a novel panoramic texture mapping-based rendering system for real-time, photorealistic reproduction of large-scale urban scenes at a street level. Various image-based rendering (IBR) methods have recently been employed to synthesize high-quality novel views, although they require an excessive number of adjacent input images or detailed geometry just to render local views. While the development of global data, such as Google Street View, has accelerated interactive IBR techniques for urban scenes, such methods have hardly been aimed at high-quality street-level rendering. To provide users with free walk-through experiences in global urban streets, our system effectively covers large-scale scenes by using sparsely sampled panoramic street-view images and simplified scene models, which are easily obtainable from open databases. Our key concept is to extract semantic information from the given street-view images and to deploy it in proper intermediate steps of the suggested pipeline, which results in enhanced rendering accuracy and performance time. Furthermore, our method supports real-time semantic 3D inpainting to handle occluded and untextured areas, which appear often when the user's viewpoint dynamically changes. Experimental results validate the effectiveness of this method in comparison with the state-of-the-art approaches. We also present real-time demos in various urban streets.