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Academic literature on the topic 'Multispectral 3D model'

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Published: 14 March 2023

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Journal articles on the topic "Multispectral 3D model"

Sun, Guoxiang, Xiaochan Wang, Ye Sun, Yongqian Ding, and Wei Lu. "Measurement Method Based on Multispectral Three-Dimensional Imaging for the Chlorophyll Contents of Greenhouse Tomato Plants." Sensors 19, no. 15 (July 30, 2019): 3345. http://dx.doi.org/10.3390/s19153345.

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Nondestructive plant growth measurement is essential for researching plant growth and health. A nondestructive measurement system to retrieve plant information includes the measurement of morphological and physiological information, but most systems use two independent measurement systems for the two types of characteristics. In this study, a highly integrated, multispectral, three-dimensional (3D) nondestructive measurement system for greenhouse tomato plants was designed. The system used a Kinect sensor, an SOC710 hyperspectral imager, an electric rotary table, and other components. A heterogeneous sensing image registration technique based on the Fourier transform was proposed, which was used to register the SOC710 multispectral reflectance in the Kinect depth image coordinate system. Furthermore, a 3D multiview RGB-D image-reconstruction method based on the pose estimation and self-calibration of the Kinect sensor was developed to reconstruct a multispectral 3D point cloud model of the tomato plant. An experiment was conducted to measure plant canopy chlorophyll and the relative chlorophyll content was measured by the soil and plant analyzer development (SPAD) measurement model based on a 3D multispectral point cloud model and a single-view point cloud model and its performance was compared and analyzed. The results revealed that the measurement model established by using the characteristic variables from the multiview point cloud model was superior to the one established using the variables from the single-view point cloud model. Therefore, the multispectral 3D reconstruction approach is able to reconstruct the plant multispectral 3D point cloud model, which optimizes the traditional two-dimensional image-based SPAD measurement method and can obtain a precise and efficient high-throughput measurement of plant chlorophyll.

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Sun, Guoxiang, Yongqian Ding, Xiaochan Wang, Wei Lu, Ye Sun, and Hongfeng Yu. "Nondestructive Determination of Nitrogen, Phosphorus and Potassium Contents in Greenhouse Tomato Plants Based on Multispectral Three-Dimensional Imaging." Sensors 19, no. 23 (December 1, 2019): 5295. http://dx.doi.org/10.3390/s19235295.

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Measurement of plant nitrogen (N), phosphorus (P), and potassium (K) levels are important for determining precise fertilization management approaches for crops cultivated in greenhouses. To accurately, rapidly, stably, and nondestructively measure the NPK levels in tomato plants, a nondestructive determination method based on multispectral three-dimensional (3D) imaging was proposed. Multiview RGB-D images and multispectral images were synchronously collected, and the plant multispectral reflectance was registered to the depth coordinates according to Fourier transform principles. Based on the Kinect sensor pose estimation and self-calibration, the unified transformation of the multiview point cloud coordinate system was realized. Finally, the iterative closest point (ICP) algorithm was used for the precise registration of multiview point clouds and the reconstruction of plant multispectral 3D point cloud models. Using the normalized grayscale similarity coefficient, the degree of spectral overlap, and the Hausdorff distance set, the accuracy of the reconstructed multispectral 3D point clouds was quantitatively evaluated, the average value was 0.9116, 0.9343 and 0.41 cm, respectively. The results indicated that the multispectral reflectance could be registered to the Kinect depth coordinates accurately based on the Fourier transform principles, the reconstruction accuracy of the multispectral 3D point cloud model met the model reconstruction needs of tomato plants. Using back-propagation artificial neural network (BPANN), support vector machine regression (SVMR), and gaussian process regression (GPR) methods, determination models for the NPK contents in tomato plants based on the reflectance characteristics of plant multispectral 3D point cloud models were separately constructed. The relative error (RE) of the N content by BPANN, SVMR and GPR prediction models were 2.27%, 7.46% and 4.03%, respectively. The RE of the P content by BPANN, SVMR and GPR prediction models were 3.32%, 8.92% and 8.41%, respectively. The RE of the K content by BPANN, SVMR and GPR prediction models were 3.27%, 5.73% and 3.32%, respectively. These models provided highly efficient and accurate measurements of the NPK contents in tomato plants. The NPK contents determination performance of these models were more stable than those of single-view models.

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Wisotzky, Eric L., Jean-Claude Rosenthal, Anna Hilsmann, Peter Eisert, and Florian C. Uecker. "A multispectral 3D-Endoscope for Cholesteatoma Removal." Current Directions in Biomedical Engineering 6, no. 3 (September 1, 2020): 257–60. http://dx.doi.org/10.1515/cdbme-2020-3065.

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AbstractWe present a stereo-multispectral endoscopic prototype using a filter-wheel to guide the removal of cholesteatoma tissue in the middle ear. An image-based method is used that combines multispectral tissue classification for the detection of tissue to be removed and 3Dreconstruction to determine its metric dimensions. The multispectral illumination used for tissue classification ranges from λ = 400 nm to λ = 500 nm with step-size of 20 nm, which results in six different narrow-band illumination modes. For classical RGB imaging and metric calculations, a broadband illumination mode is applied before and after the narrow-band illumination. The spectral information is augmented into the broadband mode using an overlay technique. The combination of multispectral imaging with stereoscopic 3D-reconstruction results in new valuable visualization of intraoperative data. This allows to generate a 3D-model of the patients anatomy highlighting the identified malicious tissue and compare the anatomical dimensions with pre-operative CT data.

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Zainuddin, K., Z. Majid, M. F. M. Ariff, K. M. Idris, M. A. Abbas, and N. Darwin. "3D MODELING FOR ROCK ART DOCUMENTATION USING LIGHTWEIGHT MULTISPECTRAL CAMERA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W9 (January 31, 2019): 787–93. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w9-787-2019.

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Abstract. This paper discusses the use of the lightweight multispectral camera to acquire three-dimensional data for rock art documentation application. The camera consists of five discrete bands, used for taking the motifs of the rock art paintings on a big structure of a cave based on the close-range photogrammetry technique. The captured images then processed using commercial structure-from-motion photogrammetry software, which automatically extracts the tie point. The extracted tie points were then used as input to generate a dense point cloud based on the multi-view stereo (MVS) and produced the multispectral 3D model, and orthophotos in a different wavelength. For comparison, the paintings and the wall surface also observed by using terrestrial laser scanner which capable of recording thousands of points in a short period of time with high accuracy. The cloud-to-cloud comparison between multispectral and TLS 3D point cloud show a sub-cm discrepancy, considering the used of the natural features as control target during 3D construction. Nevertheless, the processing also provides photorealistic orthophoto, indicates the advantages of the multispectral camera in generating dense 3D point cloud as TLS, photorealistic 3D model as RGB optic camera, and also with the multiwavelength output.

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LARSON, ADAM M., ANTHONY LEE, PO-FENG LEE, KAYLA J. BAYLESS, and ALVIN T. YEH. "ULTRASHORT PULSE MULTISPECTRAL NON-LINEAR OPTICAL MICROSCOPY." Journal of Innovative Optical Health Sciences 02, no. 01 (January 2009): 27–35. http://dx.doi.org/10.1142/s1793545809000292.

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Ultrashort pulse, multispectral non-linear optical microscopy (NLOM) is developed and used to image, simultaneously, a mixed population of cells expressing different fluorescent protein mutants in a 3D tissue model of angiogenesis. Broadband, sub-10-fs pulses are used to excite multiple fluorescent proteins and generate second harmonic in collagen. A 16-channel multispectral detector is used to delineate the multiple non-linear optical signals, pixel by pixel, in NLOM. The ability to image multiple fluorescent protein mutants and collagen, enables serial measurements of cell-cell and cell-matrix interactions in our 3D tissue model and characterization of fundamental processes in angiogenic morphogenesis.

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Es Sebar, Leila, Luca Lombardo, Marco Parvis, Emma Angelini, Alessandro Re, and Sabrina Grassini. "A metrological approach for multispectral photogrammetry." ACTA IMEKO 10, no. 4 (December 30, 2021): 111. http://dx.doi.org/10.21014/acta_imeko.v10i4.1194.

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This paper presents the design and development of a three-dimensional reference object for the metrological quality assessment of photogrammetry-based techniques, for application in the cultural heritage field. The reference object was 3D printed, with nominal manufacturing uncertainty of the order of 0.01 mm. The object was realized as a dodecahedron, and in each face, a different pictorial preparation was inserted. The preparations include several pigments, binders, and varnishes, to be representative of the materials and techniques used historically by artists.Since the reference object’s shape, size and uncertainty are known, it is possible to use this object as a reference to evaluate the quality of a 3D model from the metric point of view. In particular, verification of dimensional precision and accuracy are performed using the standard deviation on measurements acquired on the reference object and the final 3D model. In addition, the object can be used as a reference for UV-induced Visible Luminescence (UVL) acquisition, being the materials employed UV-fluorescent. Results obtained with visible-reflected and UVL images are presented and discussed.

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Lerma, J. L., M. Cabrelles, T. S. Akasheh, and N. A. Haddad. "Documentation of Weathered Architectural Heritage with Visible, near Infrared, Thermal and Laser Scanning Data." International Journal of Heritage in the Digital Era 1, no. 2 (June 2012): 251–75. http://dx.doi.org/10.1260/2047-4970.1.2.251.

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Documentation of cultural heritage requires simple, quick and easy to use multi-sensor approaches to determine the state of conservation of monuments and sites. The documentation of a highly weathered architectural heritage such as the Obelisk Tomb is a good example to test the performance integrating multispectral imagery and laser scanning data. The Obelisk Tomb is the first important façade that a visitor sees while entering to the archaeological site of Petra in Jordan. The rich architectural formations carry Egyptian, Hellenistic and Nabataean influences. The damage that was inflicted on this unique monument led us to study it applying a number of modern digital techniques including 3D scanning, multispectral photography with visible and near infrared images, and thermography. All the multiband content is initially registered onto different multispectral bands. The multispectral information is enhanced and eventually draped onto the 3D laser scanning model in order to improve documentation and analysis of the state of conservation. Our results integrating the multispectral data, thermography and terrestrial laser scanning clearly enhance the power of diagnosis over the Obelisk Tomb with state-of-the-art optical equipment and image processing software. Furthermore, the capacity to examine, analyse and detect the existing damages is enhanced by the false colour processing of the input photographic data. Weathering effects are highlighted onto the 3D model and shed some light on the causes of the damages.

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Luo, Heng, Biao He, Renzhong Guo, Weixi Wang, Xi Kuai, Bilu Xia, Yuan Wan, Ding Ma, and Linfu Xie. "Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images." Remote Sensing 13, no. 17 (August 27, 2021): 3414. http://dx.doi.org/10.3390/rs13173414.

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Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications.

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Wisotzky, Eric L., Jean-Claude Rosenthal, Ulla Wege, Anna Hilsmann, Peter Eisert, and Florian C. Uecker. "Surgical Guidance for Removal of Cholesteatoma Using a Multispectral 3D-Endoscope." Sensors 20, no. 18 (September 17, 2020): 5334. http://dx.doi.org/10.3390/s20185334.

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We develop a stereo-multispectral endoscopic prototype in which a filter-wheel is used for surgical guidance to remove cholesteatoma tissue in the middle ear. Cholesteatoma is a destructive proliferating tissue. The only treatment for this disease is surgery. Removal is a very demanding task, even for experienced surgeons. It is very difficult to distinguish between bone and cholesteatoma. In addition, it can even reoccur if not all tissue particles of the cholesteatoma are removed, which leads to undesirable follow-up operations. Therefore, we propose an image-based method that combines multispectral tissue classification and 3D reconstruction to identify all parts of the removed tissue and determine their metric dimensions intraoperatively. The designed multispectral filter-wheel 3D-endoscope prototype can switch between narrow-band spectral and broad-band white illumination, which is technically evaluated in terms of optical system properties. Further, it is tested and evaluated on three patients. The wavelengths 400 nm and 420 nm are identified as most suitable for the differentiation task. The stereoscopic image acquisition allows accurate 3D surface reconstruction of the enhanced image information. The first results are promising, as the cholesteatoma can be easily highlighted, correctly identified, and visualized as a true-to-scale 3D model showing the patient-specific anatomy.

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Suo, McGovern, and Gilmer. "Coastal Dune Vegetation Mapping Using a Multispectral Sensor Mounted on an UAS." Remote Sensing 11, no. 15 (August 2, 2019): 1814. http://dx.doi.org/10.3390/rs11151814.

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Vegetation mapping, identifying the type and distribution of plant species, is important for analysing vegetation dynamics, quantifying spatial patterns of vegetation evolution, analysing the effects of environmental changes and predicting spatial patterns of species diversity. Such analysis can contribute to the development of targeted land management actions that maintain biodiversity and ecological functions. This paper presents a methodology for 3D vegetation mapping of a coastal dune complex using a multispectral camera mounted on an unmanned aerial system with particular reference to the Buckroney dune complex in Co. Wicklow, Ireland. Unmanned aerial systems (UAS), also known as unmanned aerial vehicles (UAV) or drones, have enabled high-resolution and high-accuracy ground-based data to be gathered quickly and easily on-site. The Sequoia multispectral sensor used in this study has green, red, red edge and near-infrared wavebands, and a regular camer with red, green and blue wavebands (RGB camera), to capture both visible and near-infrared (NIR) imagery of the land surface. The workflow of 3D vegetation mapping of the study site included establishing coordinated ground control points, planning the flight mission and camera parameters, acquiring the imagery, processing the image data and performing features classification. The data processing outcomes included an orthomosaic model, a 3D surface model and multispectral imagery of the study site, in the Irish Transverse Mercator (ITM) coordinate system. The planimetric resolution of the RGB sensor-based outcomes was 0.024 m while multispectral sensor-based outcomes had a planimetric resolution of 0.096 m. High-resolution vegetation mapping was successfully generated from these data processing outcomes. There were 235 sample areas (1 m × 1 m) used for the accuracy assessment of the classification of the vegetation mapping. Feature classification was conducted using nine different classification strategies to examine the efficiency of multispectral sensor data for vegetation and contiguous land cover mapping. The nine classification strategies included combinations of spectral bands and vegetation indices. Results show classification accuracies, based on the nine different classification strategies, ranging from 52% to 75%.

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Dissertations / Theses on the topic "Multispectral 3D model"

Martin, Emmanuel. "Dart : modèle 3D multispectral et inversion d’images optique de satellite : application aux couverts forestiers." Toulouse 3, 2006. http://www.theses.fr/2006TOU30269.

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En télédétection optique, les modèles de transfert radiatif (T-R) ont pour but de simuler la mesure radiométrique des capteurs spatiaux qui observent le système "Terre - Atmosphère". La modélisation des mesures de couverts végétaux est en général limitée au niveau de la représentation du paysage et du T-R associé. Cette thèse a permis de transformer le modèle 3-D de T-R DART (brevet : PCT/FR 02/01181) en un modèle multispectral simulant les images de télédétection optique (thermique inclus) de tout paysage urbain et naturel, avec relief et atmosphère, pour tout capteur spatial/aéroporté. Ce nouveau modèle a été validé par comparaison avec d'autres modèles (expérience RAMI-3, Centre Commun de Recherche, Italie) et avec des mesures in situ et aéroportées (République Tchèque). In fine, une méthode d'inversion a été développée. Elle a permis d'utiliser DART pour évaluer l'impact de la modélisation sur l'extraction du LAI d'une forêt de conifères avec des images hyperspectrales THR
In optical remote sensing, radiative transfer (R-T) models aim to simulate radiometric measurement of spatial sensors that spot “Landscape-Atmosphere” system. The modeling of vegetation canopies measurements is generally limited to landscape representation and R-T modeling. This thesis allowed transforming a 3-D R-T model DART (patent : PCT/FR 02/01181) to multispectral model simulating optical remote sensing images (thermal infrared included) of any natural and urban landscape with relief and atmosphere for any spatial/airborne sensor. This new model was validated by models intercomparison (RAMI-3 experience, Joint Research Centre, Italy) and with in situ and airborne measurements (Czech Republic). In fine, an inversion method was developed. It allowed using DART for evaluating the modeling impact on LAI estimation of coniferous forest using hyperspectral VHR images

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Paviotti, Anna. "Acquisition and processing of multispectral data for texturing 3D models." Doctoral thesis, Università degli studi di Padova, 2009. http://hdl.handle.net/11577/3426851.

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This thesis deals with three problems concerning the use of a multispectral imaging spectrograph in applications of cultural heritage. The multispectral camera is part of an instrument developed within the project "Shape&Color" (CARIPARO, 2003-2005), coupling the spectrograph with a 3D laser scanner. Although the issues we have addressed arose from the characteristics of this specic instrument, they can be regarded as general problems concerning multispectral imaging, and are therefore of broader interest. The first part relates on the characterization of the spectrograph performance in measuring spectral reflectance under different illumination conditions. Four different illumination setups have been used to acquire a set of colored calibrated tiles. The system performance has been evaluated through a metrologically-inspired procedure, using as descriptors the average error (AE) and the average error standard deviation (AESTD), calculated by means of error propagation formula. The best results have been obtained with a metallic iodide lamp and an incandescence lamp used in a sequence, juxtaposing the spectral reflectance measured with the metallic iodide lamp in the 400-600 nm interval and that obtained with the halogen lamp in the 600-900 nm interval. The second presented issue concerns the problem of separating spectral illumination and spectral reflectance from the acquired color signal (the global radiation signal reflected by a target object). Since the latter can be considered as the product of illumination and spectral reflectance, this is an ill-posed problem. Methods in the literature estimate the two functions apart from a scale factor. The proposed solution attempts at the recovery of this scale factor using a statistical-based approach. The core of the algorithm consists of the estimation of the illumination intensity through a modification of the RANSAC algorithm, using relations derived from the physical constraints of the illumination and the spectral reflectance. The spectral reflectance is subsequently computed from the measured color signal and the estimated illumination function. The algorithm has been tested on four case studies, representing artworks of different pictorial techniques, color characteristics and dimensions. The results are good in terms of mean relative error, while the infinity norm of the relative error sometimes assumes high values. The last problem we have dealt with is that of using the multispectral images acquired with the Shape&Color scanner to texturize uncalibrated 3D data. What makes the problem worth addressing is that the spectral camera is not pinhole, but can be classified as a cylindrical panoramic camera. In this thesis, the general problem of estimating the extrinsic parameters of the camera from a known set of 3D-2D correspondences has been considered. The chosen approach is the classical reprojection error minimization procedure. As the projection operator is nonlinear, the objective function has a very complicated structure. Due to this and to the high dimensionality of the problem, the minimization results are strongly sensitive to the choice of the initial parameter values. This work proposes a way of finding a reliable initial point for the minimization function, so as to lower the risk of being trapped into local minima.

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Book chapters on the topic "Multispectral 3D model"

Valero, Mario M., Amanda Makowiecki, Alan Brewer, Craig B. Clements, Neil P. Lareau, Adam K. Kochanski, and Edward Strobach. "The California Fire Dynamics Experiment (CalFiDE): Developing Validation Data Sets for Coupled Fire-Atmosphere Simulations." In Advances in Forest Fire Research 2022, 388–93. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_62.

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The California Fire Dynamics Experiment (CalFiDE) is a 6-week study of wildfire behavior and its response to spatially and temporally evolving wind fields in California, USA. It is the result of a partnership between the Chemical Sciences Laboratory (CSL) at the National Oceanic and Atmospheric Administration (NOAA), the Wildfire Interdisciplinary Research Center (WIRC) at San Jose State University (SJSU) and University of Nevada, Reno. A Twin Otter aircraft will be instrumented and flown over landscape-scale wildfires in California between August 14 and September 30, 2022. Onboard instrumentation includes (i) a scanning Doppler lidar system capable of measuring vertical profiles of 3D wind speed and turbulence, (ii) a multispectral infrared imaging system designed to remotely sense fire behavior, (iii) NightFox fire radiative power sensors (iv) AIMMS probe to measure flight-level winds, temperature, and water vapor content, and (v) Chemistry instruments to sample flight level NOX , NOY, O3, CO and GHG. Airborne measurements will be complemented with ground-based mobile scanning radars and lidars, which will be positioned around the fire to characterize the spatial structure and internal dynamics of the smoke plume. This combination of sensors will provide a unique opportunity to characterize landscape-scale wildfire behavior, fire weather and fire atmospheric chemistry in a synchronized manner. We expect that the datasets resulting from this experiment will have a broad applicability in fundamental fire dynamics studies, fire model validation exercises and the calibration of spaceborne remote sensing fire observations.

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Conference papers on the topic "Multispectral 3D model"

Shao, Xiaopeng, and Jianqi Zhang. "Simulation of 3D infrared scenes using random fields model." In Multispectral Image Processing and Pattern Recognition, edited by Yair Censor and Mingyue Ding. SPIE, 2001. http://dx.doi.org/10.1117/12.441610.

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Yu, Hengyong, Xuanqin Mou, Yong Wang, and Yuanlong Cai. "3D reconstruction model of vessels based on object-oriented quantization." In Multispectral Image Processing and Pattern Recognition, edited by Jayaram K. Udupa and Aaron Fenster. SPIE, 2001. http://dx.doi.org/10.1117/12.440272.

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Paquit, Vincent, Jeffery R. Price, Fabrice Mériaudeau, and Kenneth W. Tobin. "3D multispectral light propagation model for subcutaneous veins imaging." In Medical Imaging, edited by Jiang Hsieh and Ehsan Samei. SPIE, 2008. http://dx.doi.org/10.1117/12.772825.

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Sun, Min, Dan Zhang, and Zheng Hu. "3D tunnel model reconstruction from monocular image." In Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, edited by Mingyue Ding, Bir Bhanu, Friedrich M. Wahl, and Jonathan Roberts. SPIE, 2009. http://dx.doi.org/10.1117/12.833131.

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Jing, Hui, Meifa Huang, and Yanru Zhong. "3D model retrieve based on K -means clustering." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.750672.

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Chen, Peng, and Linkui Meng. "Research of 3D model construction and visualization of complicated objects in 3D cyber-city." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.746883.

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Zhang, Jun, Jian Liu, and Xiaomao Liu. "Using penalty function to find the relative oriented model in 3D surface reconstruction." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323613.

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Zheng, Shunyi, Guozhong Su, Zuxun Zhang, and Chengcai Zheng. "Automatic generation of 3D surface model from scatter points based on projective constraint." In MIPPR 2005 SAR and Multispectral Image Processing, edited by Liangpei Zhang, Jianqing Zhang, and Mingsheng Liao. SPIE, 2005. http://dx.doi.org/10.1117/12.654936.

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Zhou, Jie, Tianrui Zhou, and Haipeng Pan. "Feature extraction technology of 3D geometric model based on STEP-NC." In International Symposium on Multispectral Image Processing and Pattern Recognition, edited by S. J. Maybank, Mingyue Ding, F. Wahl, and Yaoting Zhu. SPIE, 2007. http://dx.doi.org/10.1117/12.749454.

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Ioannakis, George, Fotis Arnaoutoglou, Anestis Koutsoudis, and Christodoulos Chamzas. "Exploiting Supervised Learning for 3D Model Semantic Segmentation Using Multispectral Data." In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2019. http://dx.doi.org/10.1109/spin.2019.8711658.

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