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基于预定位图像框架策略的全景图配准拼接算法研究
The Research of Panorama Matching Stitching Algorithm Based on a Predetermined Image Framework Strategy
 [PDF]

徐飞黎
Journal of Image and Signal Processing (JISP) , 2015, DOI: 10.12677/JISP.2015.44013
Abstract:
由于壁画拼接在图像拼接应用场景的特殊性,采用传统的单应性投射矩阵配准算法无法满足。通过SFM流程对壁画三维重建,预定位全景图的框架图片,调整非框架图片的投射顺序,采用滑动窗口直接线性变换算法进行投射。通过全景图的主观评价,误差数值的客观评价以及与商业拼接软件作实验对比验证了本课题算法的可行性。
Because of the particularity of the mural mosaic work in the panorama stitching, the existing algorithms can’t satisfy the needs. We set up three-dimensional reconstruction by a structure-from- motion (SFM) system to obtain the framework, and then project the no-frame images to the frame by Moving DLT in order to refine the transformations. The experiment shows a better result between objectivity and subjectivity compared with commercial softwares.
National-scale analysis for the identification of High Conservation Value Forests (HCVFs)
Maesano M,Giongo Alves MV,Ottaviano M,Marchetti M
Forest@ , 2011, DOI: 10.3832/efor0649-008
Abstract: In Italy, forests cover about one third of the national territory. In recent years, sustainability has been applied to forest management through the introduction of the Sustainable Forest Management (SFM) concept. Since the Rio Conference, several initiatives at international and governmental level aimed to realize the SFM concept by the establishment of a set of principles with general validity. One of the most successful initiatives is the Forest Stewardship Council (FSC), which has developed a system of voluntary certification specific for the forestry sector, as well as 10 principles and 56 criteria for good forest management. The concept of High Conservation Value Forest concept (HCVFs) was defined in 1999 by FSC under Principle 9, and its application requires the identification of six categories of High Conservation Values (HCV). The aim of this study was to define the parameters for the HCVFs Italian forests, A first national mapping for the first level of High Conservation Value was developed focusing on protected areas, threatened and endangered species and the ecosystemic temporal use. Protected areas may constitute the basis of the SFM. This work is the result of data processing and distribution analysis through the intersection of vectorial data of national forests areas in ArcMap, on the basis of available information. Protected forest areas represent 34% of the national forest area. The different categories of protected areas contribute differently to protection, in particular the larger amount of preserved forests (22.96%) falls within Sites of Community Importance (SCI). The analysis of highly protected forest types revealed major differences likely linked to site ecological conditions, which are extremely variable over the country. The HCVF concept is applied in the forest certification field and can be used in sustainable forest management, planning and land use, and policy commitments.
An Intelligent Model For Detection Of Post-Operative Infections
Mohamed El-Rashidy,TahaTaha,Nabil Ayad,HodaSroor
International Journal of Artificial Intelligence & Applications , 2011,
Abstract: An effective intelligent diagnosis model is aiming to provide a comprehensive ANALYSIS to form optimal partitioning REPRESENTATION of patient data, and extracts the most significant features for each partition which raise the accuracy of diagnosis process. Optimal Clustering for support feature machine (OCSFM) is proposed to improve the feature selection in medical data classification comprises clustering, feature selection, and classification concepts which is based on fuzzy C-means, max-min, and support feature machine (SFM) models. Experiments have been conducted on database of surgical patients to detect postoperative infections. The performance of the method is evaluated using classification sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient. The results show that the highest classification performance is obtained for the OCSFM model, and this is very promising compared to Na veBayes, Linear Support Vector Machine (Linear SVM), Polykernal SVM, artificial neural network (ANN), and SFM models.
Modeling and Detection of Camouflaging Worm using IP Traceback
S Preetha
International Journal of Computer Science and Communication Networks , 2012,
Abstract: Active worms pose major security threats to the Internet. This is due to the ability of active worms to propagate in an automated fashion as they continuously compromise computers on the Internet. Active worms evolve during their propagation, and thus, pose great challenges to defend against them. A new class of active worms, referred to as Camouflaging Worm (C-Worm in short). The C-Worm is different from traditional worms because of its ability to intelligently manipulate its scan traffic volume over time. Thereby, the C-Worm camouflages its propagation from existing worm detection systems based on analyzing the propagation traffic generated by worms. The characteristics of the C-Worm and conduct a comprehensive comparison between its traffic and non-worm traffic (background traffic). The two types of traffic are barely distinguishable in the time domain. However, their distinction is clear in the frequency domain, due to the recurring manipulative nature of the C-Worm. Motivated by observations, designed a novel spectrum-based scheme to detect the C-Worm. The Power Spectral Density (PSD) distribution of the scan traffic volume and its corresponding Spectral Flatness Measure (SFM) to distinguish the C-Worm traffic from background traffic. Using a comprehensive set of detection metrics and real-world traces as background traffic, the extensive performance evaluations on proposed spectrum-based detection scheme. The performance data clearly demonstrates that our scheme can effectively detect the C-Worm propagation. Furthermore, show the generality of spectrum-based scheme in effectively detecting not only y the C-Worm, but traditional worms as well
A Multi-Resolution Photogrammetric Framework for Digital Geometric Recording of Large Archeological Sites: Ajloun Castle-Jordan  [PDF]
A’kif Al-Fugara, Rida Al-Adamat, Yahya Al-Shawabkeh, Omar Al-Kour, Abdel Rahman Al-Shabeeb
International Journal of Geosciences (IJG) , 2016, DOI: 10.4236/ijg.2016.73033
Abstract: The generation of reality-based 3D models of archeological sites is the future of representing existing ancient structures. Such approach requires substantial economic and logistical costs which limit this application. In this context, this paper presents the use of photogrammetric workflow, based on Structure from Motion techniques (SfM) to derive 3D metric information from Structure-and-Motion images. The interdisciplinary 3D modeling framework consists of fusion of multi resolution images from both overlapped low-altitude aerial and multi-view terrestrial imagery. The acquisition of aerial photographs survey was based on archived oblique aerial stereo pair photos acquired from the Aerial Photographic Archive for Archaeology in the Middle East (APAAME) project, while terrestrial of close range photos covering the castle walls has been acquired using portable camera. Camera calibration and orientation were carried out by VisualSFM, CMPMVS (Multi-View Reconstruction) and SURE (Photogrammetric Surface Reconstruction from Imagery) software. The resulted cloud points were processed using cloud Compare, MeshLab, Agisoft, and Skethup Softwares. A complete 3D digital geometric recording of the site was accomplished based on dense 3D point clouds with realistic metric accuracy and photorealistic performance to meet all the surveying and archaeological needs. The final Ajloun castle’s digital model geometry was added as a 3D building layer on Google Earth.
Sustainable Forest management: example of implementation of PEFC certification schemes
Lovreglio R,Gammarano G,Leone V
Forest@ , 2006,
Abstract: After a short description of forest certification standards, the Authors outline PEFC certification schemes implemented in a study-case (municipal forest of Montano Antilia, in the National Park of Cilento and Vallo di Diano, South of Italy), discussing the limited success of certification and some of the obstacles to its adoption in the specific case. One of the barriers for the adoption of forest certification is the lack of basic information, even in presence of the Forests Management Plan.
Asymmetrical Charge Distribution Over the Strained Silicon Membrane
Shobha Kanta Lamichhane
Scientific World , 2013, DOI: 10.3126/sw.v11i11.8547
Abstract: Scientific World, Vol. 11, No. 11, July 2013, page 17-24 DOI: http://dx.doi.org/10.3126/sw.v11i11.8547
An Automated Technique for Generating Georectified Mosaics from Ultra-High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Based on Structure from Motion (SfM) Point Clouds
Darren Turner,Arko Lucieer,Christopher Watson
Remote Sensing , 2012, DOI: 10.3390/rs4051392
Abstract: Unmanned Aerial Vehicles (UAVs) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. Remote sensing with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. The small footprint of UAV imagery, however, makes it necessary to develop automated techniques to geometrically rectify and mosaic the imagery such that larger areas can be monitored. In this paper, we present a technique for geometric correction and mosaicking of UAV photography using feature matching and Structure from Motion (SfM) photogrammetric techniques. Images are processed to create three dimensional point clouds, initially in an arbitrary model space. The point clouds are transformed into a real-world coordinate system using either a direct georeferencing technique that uses estimated camera positions or via a Ground Control Point (GCP) technique that uses automatically identified GCPs within the point cloud. The point cloud is then used to generate a Digital Terrain Model (DTM) required for rectification of the images. Subsequent georeferenced images are then joined together to form a mosaic of the study area. The absolute spatial accuracy of the direct technique was found to be 65–120 cm whilst the GCP technique achieves an accuracy of approximately 10–15 cm.
Parallel- and serial-contact electrochemical metallization of monolayer nanopatterns: A versatile synthetic tool en route to bottom-up assembly of electric nanocircuits
Jonathan Berson,Assaf Zeira,Rivka Maoz,Jacob Sagiv
Beilstein Journal of Nanotechnology , 2012, DOI: 10.3762/bjnano.3.14
Abstract: Contact electrochemical transfer of silver from a metal-film stamp (parallel process) or a metal-coated scanning probe (serial process) is demonstrated to allow site-selective metallization of monolayer template patterns of any desired shape and size created by constructive nanolithography. The precise nanoscale control of metal delivery to predefined surface sites, achieved as a result of the selective affinity of the monolayer template for electrochemically generated metal ions, provides a versatile synthetic tool en route to the bottom-up assembly of electric nanocircuits. These findings offer direct experimental support to the view that, in electrochemical metal deposition, charge is carried across the electrode–solution interface by ion migration to the electrode rather than by electron transfer to hydrated ions in solution.
Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud
Adam J. Mathews,Jennifer L. R. Jensen
Remote Sensing , 2013, DOI: 10.3390/rs5052164
Abstract: This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R 2 value of 0.567, accounting for 57 percent of the variation of LAI SQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).
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