A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Urban detection, delimitation and morphology: comparative analysis of selective "megacities"

Postprint (published version

Recovering occlusion-free textured 3D maps of urban facades by a synergistic use of terrestrial images, 3D point clouds and area-based information

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Variable Resolution & Dimensional Mapping For 3d Model Optimization

Three-dimensional computer models, especially geospatial architectural data sets, can be visualized in the same way humans experience the world, providing a realistic, interactive experience. Scene familiarization, architectural analysis, scientific visualization, and many other applications would benefit from finely detailed, high resolution, 3D models. Automated methods to construct these 3D models traditionally has produced data sets that are often low fidelity or inaccurate; otherwise, they are initially highly detailed, but are very labor and time intensive to construct. Such data sets are often not practical for common real-time usage and are not easily updated. This thesis proposes Variable Resolution & Dimensional Mapping (VRDM), a methodology that has been developed to address some of the limitations of existing approaches to model construction from images. Key components of VRDM are texture palettes, which enable variable and ultra-high resolution images to be easily composited; texture features, which allow image features to integrated as image or geometry, and have the ability to modify the geometric model structure to add detail. These components support a primary VRDM objective of facilitating model refinement with additional data. This can be done until the desired fidelity is achieved as practical limits of infinite detail are approached. Texture Levels, the third component, enable real-time interaction with a very detailed model, along with the flexibility of having alternate pixel data for a given area of the model and this is achieved through extra dimensions. Together these techniques have been used to construct models that can contain GBs of imagery data

AUTOMATIC IMAGE TO MODEL ALIGNMENT FOR PHOTO-REALISTIC URBAN MODEL RECONSTRUCTION

We introduce a hybrid approach in which images of an urban scene are automatically alignedwith a base geometry of the scene to determine model-relative external camera parameters. Thealgorithm takes as input a model of the scene and images with approximate external cameraparameters and aligns the images to the model by extracting the facades from the images andaligning the facades with the model by minimizing over a multivariate objective function. Theresulting image-pose pairs can be used to render photo-realistic views of the model via texturemapping.Several natural extensions to the base hybrid reconstruction technique are also introduced. Theseextensions, which include vanishing point based calibration refinement and video stream basedreconstruction, increase the accuracy of the base algorithm, reduce the amount of data that mustbe provided by the user as input to the algorithm, and provide a mechanism for automaticallycalibrating a large set of images for post processing steps such as automatic model enhancementand fly-through model visualization.Traditionally, photo-realistic urban reconstruction has been approached from purely image-basedor model-based approaches. Recently, research has been conducted on hybrid approaches, whichcombine the use of images and models. Such approaches typically require user assistance forcamera calibration. Our approach is an improvement over these methods because it does notrequire user assistance for camera calibration

Structural Integrity of an Electron Beam Melted Titanium Alloy

Advanced manufacturing encompasses the wide range of processes that consist of “3D printing” of metallic materials. One such method is Electron Beam Melting (EBM), a modern build technology that offers significant potential for lean manufacture and a capability to produce fully dense near-net shaped components. However, the manufacture of intricate geometries will result in variable thermal cycles and thus a transient microstructure throughout, leading to a highly textured structure. As such, successful implementation of these technologies requires a comprehensive assessment of the relationships of the key process variables, geometries, resultant microstructures and mechanical properties. The nature of this process suggests that it is often difficult to produce representative test specimens necessary to achieve a full mechanical property characterisation. Therefore, the use of small scale test techniques may be exploited, specifically the small punch (SP) test. The SP test offers a capability for sampling miniaturised test specimens from various discrete locations in a thin-walled component, allowing a full characterisation across a complex geometry. This paper provides support in working towards development and validation strategies in order for advanced manufactured components to be safely implemented into future gas turbine applications. This has been achieved by applying the SP test to a series of Ti-6Al-4V variants that have been manufactured through a variety of processing routes including EBM and investigating the structural integrity of each material and how this controls the mechanical response

Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network

The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.European Union (EU) under Erasmus+ project entitled “Fostering Internationalization in Agricultural Engineering in Iran and Russia” [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JPinfo:eu-repo/semantics/publishedVersio

Segmentation of remote sensing images using similarity measure based fusion-MRF model

Classifying segments and detecting changes in terrestrial areas are important and time-consuming efforts for remote sensing image analysis tasks, including comparison and retrieval in repositories containing multitemporal remote image samples for the same area in very different quality and details. We propose a multilayer fusion model for adaptive segmentation and change detection of optical remote sensing image series, where trajectory analysis or direct comparison is not applicable. Our method applies unsupervised or partly supervised clustering on a fused-image series by using cross-layer similarity measure, followed by multilayer Markov random field segmentation. The resulted label map is applied for the automatic training of single layers. After the segmentation of each single layer separately, changes are detected between single label maps. The significant benefit of the proposed method has been numerically validated on remotely sensed image series with ground-truth data

Use of image analysis to evaluate the effect of high hydrostatic pressure and pasteurization as preservation treatments on the microstructure of red sweet pepper

[EN] The aim of this work was to evaluate the effect of HHP treatment and PAST on the microstructure of red Lamuyotype sweet peppers using image analysis and to determine the parameters that allow characterizing the changes observed on the structure using different magnifications (100×, 200×, and 350×). The results show that all the preservation treatments evaluated caused structural modifications on the microstructure of red sweet pepper, but HHP at 500 MPa and PAST had less impact. Fractal dimension texture, contrast, inverse difference moment, and entropy are texture features that are appropriate for characterizing the effect of HHP and PAST on red pepper texture. In this context, it is important to consider the magnification at which red pepper texture is evaluated because cell damage caused by treatments is best observed at low magnification. Consequently, image analysis could be used in future studies to relate microstructure to the functionality of products subject to HHP. Industrial relevance: Red sweet peppers (Capsicum annuum) are an excellent source of essential nutrients and bioactive compounds. High hydrostatic pressure (HHP) applied during food processing can improve the retention of food quality attributes and nutritional and organoleptic properties better than pasteurization (PAST). Image analysis is a non-invasive technique that allows to provide objective evaluations from digitalized images. There are no studies that quantify the effect of HHP and PAST using image texture parameters and that evaluate the effect of the magnification used on these texture features. These features are critical factors that determine food acceptance or rejection by the consumers. Thus, texture measurement has gained much attention from food science and industry. Therefore, it would be interesting to study the effect of these treatments on the microstructure of red sweet pepper tissue using image analysis. Thereby, it would be possible to relate the image information to structural modifications and to the extractability of bioactive compounds or acceptance of preservation processes by consumers.The authors wish to acknowledge the Spanish Ministry of Science and Innovation for the financial support (project AGL2011-30064-C02) and to the Universitat Politecnica de Valencia (UPV) for the FPI grant given to Maria Hernandez Carrion. The authors also thank University of La Sabana for let Maria Hernandez Carrion to do a stay of research and for access to their materials and methods. Moreover, the authors wish to thank Elsevier for assistance with the English manuscriptHernández-Carrión, M.; Hernando Hernando, MI.; Sotelo-Díaz, I.; Quintanilla-Carvajal, M.; Quiles Chuliá, MD. (2015). Use of image analysis to evaluate the effect of high hydrostatic pressure and pasteurization as preservation treatments on the microstructure of red sweet pepper. Innovative Food Science & Emerging Technologies. 27:69-78. https://doi.org/10.1016/j.ifset.2014.10.011S69782