Top PDF MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

Registration process can be recognized to find correspondence between datasets by establishing relation. Brown (1992) classified existing registration methods into area-based and feature-based methods according to their nature. Area-based approach uses image intensity values extracted from image patches. It deals with images without attempting to detect salient objects. Correspondence can be determined with a sliding window of a specific size or over the entire image by correlation-like methods such as; fourier methods, and mutual information methods, and so forth. While, feature-based methods uses salient objects such as points, lines, and polygons to establish relation between two different datasets. The feature- based methods generally consist of feature extraction, feature matching, and transformation. In model-to-image registration, most of registration methods are based on feature-based methods because models have no texture information while salient objects can be extracted from the models and image. Points features such as line intersections, corners and centroids of regions can be easily extracted from models and images. Thus, Wunsch and Hirzinger (1996) used the iterative closest point algorithm (ICP) to register a model to the 3D data. In similar way, Avbelj et al. (2010) used point features to align 3D wire-frame building model with infrared video sequences using a subsequent closeness-based matching algorithm. However, Frueh et al. (2004) pointed out that point features extracted from image cause false correspondence due to a large number of outliers. As building models or man-made objects are mainly described by linear structures, many researchers have used lines or line segments as features for the registration process. Hsu et al. (2000) used line features to
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GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

GEOMETRIC AND REFLECTANCE SIGNATURE CHARACTERIZATION OF COMPLEX CANOPIES USING HYPERSPECTRAL STEREOSCOPIC IMAGES FROM UAV AND TERRESTRIAL PLATFORMS

Novel hyperspectral imaging technology based on a variable air gap Fabry-Perot interferometer (FPI) was used in this investigation. The FPI technology makes it possible to manufacture lightweight, frame format hyperspectral imager operating in the time-sequential principle. The first prototypes of the FPI-based cameras were operating in the visible to near- infrared spectral range (500-900 nm; VNIR) (Saari et al., 2011; Mäkynen et al., 2011; Honkavaara et al., 2013). The FPI technology is also commercially available in the VNIR range (http://www.rikola.fi). Similar to conventional cameras, these sensors can be operated from terrestrial and airborne platforms using photogrammetric principles, capturing image blocks with stereoscopic overlaps. Efficient and accurate data post- processing is required to transform these hundreds and thousands of images into products that allow the objects’ geometric and spectral characteristics to be interpreted on a quantitative geometric and radiometric basis. The modern computer vision and photogrammetric techniques based on structure-from-motion image orientation techniques (Wu et al., 2013) and dense digital matching generating accurate 3D point clouds and digital surface models (DSM) (Leberl et al., 2010) offer efficient tools to process the data sets.
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FEATURE LINE BASED BUILDING DETECTION AND RECONSTRUCTION FROM OBLIQUE AIRBORNE IMAGERY

FEATURE LINE BASED BUILDING DETECTION AND RECONSTRUCTION FROM OBLIQUE AIRBORNE IMAGERY

In this paper, a feature line based method for building detection and reconstruction from oblique airborne imagery is presented. With the development of Multi-View Stereo technology, increasing photogrammetric softwares are provided to generate textured meshes from oblique airborne imagery. However, errors in image matching and mesh segmentation lead to the low geometrical accuracy of building models, especially at building boundaries. To simplify massive meshes and construct accurate 3D building models, we integrate multi-view images and meshes by using feature lines, in which contour lines are used for building detection and straight skeleton for building reconstruction. Firstly, through the contour clustering method, buildings can be quickly and robustly detected from meshes. Then, a feature preserving mesh segmentation method is applied to accurately extract 3D straight skeleton from meshes. Finally, straight feature lines derived from multi-view images are used to rectify inaccurate parts of 3D straight skeleton of buildings. Therefore, low quality model can be refined by the accuracy improvement of mesh feature lines and rectification with feature lines of multi-view images. The test dataset in Zürich is provided by ISPRS/EuroSDR initiative Benchmark on High Density Image Matching for DSM Computation. The experiments reveal that the proposed method can obtain convincing and high quality 3D building models from oblique airborne imagery.
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AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES

AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES

Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs) images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment.
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AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD

AN AERIAL-IMAGE DENSE MATCHING APPROACH BASED ON OPTICAL FLOW FIELD

Dense matching is a key ingredient in automated acquisition of geometric models and 3D scenes from image sequence or videos, a process known as image-based 3D reconstruction. As for its application, dense matching can be used as for large-scale mapping, true orthophoto generation, environmental surveying archaeology and culture heritage, and especially 3D city modelling (Xiao, 2016). In last decades, numbers of well- performed algorithms have been developed they mainly divided into four types. Voxel based approaches, deformable polygonal based approaches, depth map based approaches and patch-based approaches. Voxel based approaches require knowing the bounding box that contains the scene that their accuracy is limited by the resolution of the voxel grid (Furukawa, 2010). Methods based on deformable polygonal meshes demand a high quality start point, such as a visual hull model (Laurentini, 1994), they use this kind of start point to initialized the corresponding optimization process. Those two kinds of approaches are often limited to the datasets quality and initial processing quality, which are not flexible. Compared to the voxel-based and polygonal mesh-based approaches, patch-based approaches such as patch-based multi-view stereo (PMVS) (Furukawa, 2010), and depth-based approaches such as semi- global matching (SGM) (Hirschmüller, 2008) are more flexible. SGM and its acceleration algorithms are widely used in digital evaluation model (DEM) generation and 3D scene reconstructions (Halaa, 2012).Although SGM is more flexible than voxel-based and polygonal mesh-based approaches, it require fusing individual depth map into a single 3D model. And the noise of depth maps often influence the final results accuracy and reliability. Rothermel proposed an enhanced SGM by using dynamic disparity range to search the pixel correspondence (Rothermel, 2012).Patch-based approaches are more reasonable in one scene than rectangle window matching
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SEGMENTATION OF UAV-BASED IMAGES INCORPORATING 3D POINT CLOUD INFORMATION

SEGMENTATION OF UAV-BASED IMAGES INCORPORATING 3D POINT CLOUD INFORMATION

Numerous applications related to urban scene analysis demand automatic recognition of buildings and distinct sub-elements. For example, if LiDAR data is available, only 3D information could be leveraged for the segmentation. However, this poses several risks, for instance, the in-plane objects cannot be distinguished from their surroundings. On the other hand, if only image based segmentation is performed, the geometric features (e.g., normal orientation, planarity) are not readily available. This renders the task of detecting the distinct sub-elements of the building with similar radiometric characteristic infeasible. In this paper the individual sub-elements of buildings are recognized through sub-segmentation of the building using geometric and radiometric characteristics jointly. 3D points generated from Unmanned Aerial Vehicle (UAV) images are used for inferring the geometric characteristics of roofs and facades of the building. However, the image-based 3D points are noisy, error prone and often contain gaps. Hence the segmentation in 3D space is not appropriate. Therefore, we propose to perform segmentation in image space using geometric features from the 3D point cloud along with the radiometric features. The initial detection of buildings in 3D point cloud is followed by the segmentation in image space using the region growing approach by utilizing various radiometric and 3D point cloud features. The developed method was tested using two data sets obtained with UAV images with a ground resolution of around 1-2 cm. The developed method accurately segmented most of the building elements when compared to the plane-based segmentation using 3D point cloud alone.
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A Hybrid Steganography System based on LSB Matching and Replacement

A Hybrid Steganography System based on LSB Matching and Replacement

Abstract—This paper proposes a hybrid steganographic ap- proach using the least significant bit (LSB) technique for grayscale images. The proposed approach uses both LSB match- ing (LSB-M) and LSB replacement to hide the secret data in images. Using hybrid LSB techniques increase the level of security. Thus, attackers cannot easily, if not impossible, extract the secret data. The proposed approach stores two bits in a pixel. The embedding rate can reach up to 1.6 bit per pixel. The proposed approach is evaluated and subjected to various kinds of image processing attacks. The performance of the proposed algorithm is compared with two other relevant techniques; pixel- value differencing (PVD) and Complexity Based LSB-M (CBL). Experimental results indicate that the proposed algorithm out- performs PVD in terms of imperceptibility. Also, it significantly outperforms CBL in two main features; higher embedding rate (ER), and more robust to most common image processing attacks such as median filtering, histogram equalization, and rotation.
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3D ear identification based on sparse representation.

3D ear identification based on sparse representation.

2. Ear Contour Alignment and Ear Region Extraction In order to partially solve the pose change problem and to make the extracted ear regions reside in a common canonical coordinate system, we adopt the ICP algorithm to align the ear contour of the sample being processed to an ear contour template created offline manually. Such an idea is inspired by the work [21]; however, there are some differences. At first, in our case, since the ear pit has already been detected, the computational cost of ICP-based contour matching could be reduced by aligning the ear pits first. Secondly, in our case, ICP matching is performed in the 2D image space, which is much faster than the one working in the 3D space. We built an ear contour template by manually selecting the ear pit point, 30 helix points, and 10 antihelix points from one instance of samples from UND-J2 ear database [51]. The ear contour template finally generated is shown in Fig. 3. For each 3D side face image being processed, we at first extract the sector
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João Nuno Silva Tabar Domingos (26333)

João Nuno Silva Tabar Domingos (26333)

The SaaS concept and its advantages are well known [JNL10, Lou10]. An or- ganization develops an application and controls the maintenance and versioning process providing only a frontend to the user. The application can be accessed from anywhere as long as a network connection is available and there is no need for application specific installation processes, a simple browser is generally suffi- cient. The data used by the applications can be safely stored in the cloud. At this level, users access full applications as services and pay for a subscription. Most of these applications do not allow a high level of customization. The customiza- tions available are usually similar to adapting a generic business application like an enterprise resource planning (ERP) application to the specific business needs. However the application itself can be a high level software development platform in which case a much higher level of customization is possible (ex. Force.com). Cloud computing did not change the concept of SaaS but allowed for SaaS solu- tions to be developed and deployed more quickly and with lower costs by using PaaS and IaaS solutions.
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Local Feature Based on Moment Invariants for Blurred Image Matching

Local Feature Based on Moment Invariants for Blurred Image Matching

On the other hand, a local feature scheme is suited to many applications such as wide baseline matching [9], image retrieval [10], object recognition and image classification [11]. The main idea of a local feature scheme is to detect interest points in an image and then to describe the surrounding pixels' information around each key point as a local feature. The advantages of local feature schemes are that they are robust to image changes such as scales, rotation, illumination change, and occlusion, etc. One of the most popular local feature scheme is Lowe's SIFT [12], which is widely accepted as the highest-quality scheme currently available. Another popular method is Bay et al’s SURF [13], which maintains good robustness and at same time can be computed much faster. However, since the majority of existing local features focus on robustness of image changes, there are no local feature schemes to solve the robustness against a strong blur as far as the authors know.
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Comparative Study on Context-Based Document Clustering

Comparative Study on Context-Based Document Clustering

Abstract- Clustering is an automatic learning technique aimed at grouping a set of objects into subsets or clusters. Objects in the same cluster should be as similar as possible, whereas objects in one cluster should be as dissimilar as possible from objects in the other clusters. Document clustering has become an increasingly important task in analysing huge documents. The challenging aspect to analyse the enormous documents is to organise them in such a way that facilitates better search and knowledge extraction without introducing extra cost and complexity. Document clustering has played an important role in many fields like information retrieval and data mining. In this paper, first Document Clustering has been proposed using Hierarchical Agglomerative Clustering and K-Means Clustering Algorithm.Here, the approach is purely based on the frequency count of the terms present in the documents where context of the documents are totally ignored. Therefore, the method is modified by incorporating Relatedness to measure the degree of relevance of the terms with respect to the concepts present in the documents. Thus, this Clustering is not only Term based but also understanding based (ie, Context Dependent). Next, the clustering is done by Hierarchical Agglomerative Clustering and K-Means with the Relatedness concept. Davies-Bouldin’s (DB) Index, which is a well-known metric, has been used to compare the quality of clusters-as they are obtained when the concept of Relatedness is not incorporated in the above mentioned document-clustering algorithms and secondly, when relatedness is integrated into the algorithms.
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Analysis of the Portuguese west coast morphology and morphodynamics, based on aerial images and GIS tools

Analysis of the Portuguese west coast morphology and morphodynamics, based on aerial images and GIS tools

be dissipative (flat, multi-bared); between those ( Ω = 2-5) they classified beaches as intermediate (one to two bars) (v). A comprehensive range of micro-tidal beach types are illustrated in this study. Relatively to the meso and macro-tidal beach systems, (iv), macro-tidal and meso-tidal beach systems are merged in the same group, in an attempt to classify them, particularly those where tide range exceeds 3 m for average spring tides; and thus only referring to macro-tidal beaches. One major issue in the study of macro-tidal beaches is to find common paths for comparisons; these situations do not occur only in high or low sea and swell environments, with varying grain size and sorting, but also where tides range from 3 to 15 m, concerned the previous classification. Based on gradient, topography, and relative sea-swell energy, macro-tidal beaches were divided into three groups, (iv):
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Res. Biomed. Eng.  vol.32 número2

Res. Biomed. Eng. vol.32 número2

Artiicial neural network (ANN) is a technique that has attracted much attention as an approach to estimate quantities when there are complex relationships between input and output variables, particularly when no links are known among them a priori. Amongst several advantages of neural network models, it can be emphasized that they are easy to use and to update, possess large degree of freedom and give accurate prediction at high speed (Nafey, 2009; Niemi et al., 1995). The main one is the ability to generalize, i.e., to learn from examples. In this regard, after an ANN has been satisfactorily trained and tested, it is able to
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Matéria (Rio J.)  vol.19 número1

Matéria (Rio J.) vol.19 número1

The performance of a 3D bone scaffold is not only related to the biomaterial selection, but also to its architecture and consequent interaction with the surrounding tissue. In order to be considered properly designed, a scaffold should allow bone ingrowth and provide vascularization for the new tissue. This can be achieved by controlling the scaffold pore volume fraction, pore interconnections, and pore size. It’s known that a highly interconnected porosity volume fraction provides a large surface area, which facilitates cells to migrate, proliferate, and differentiate [15, 16]. Both β-TCP and BCP scaffolds with structured porosity and controlled chemistry have been successfully fabricated by additive manufacture methods using concentrated colloidal inks [17]. In this study, scaffolds with interconnected macroporous were successfully obtained by direct-write assembly, which was confirmed by the micro-CT images. SCHELLER et al. [1] acknowledge that it is difficult to assimilate high porosity and mechanical integrity into a single material design, as mechanical properties are frequently maximized by minimizing porosity. Nonetheless, the mean compressive strength values of β-TCP (11 MPa) and BCP (15MPa) scaffolds obtained in this study were compatible or higher than that of human trabecular bone. DETSCH et al. [18] disclosed that the bone substitute strength should be between 0.5 and 15 MPa, considering a cancellous bone replacement. Studies with trabecular bone have been registered a wide variation of strength which is usually in the range of 4 to 12 MPa [17, 19, 20].
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COMPARISON BETWEEN TWO GENERIC 3D BUILDING RECONSTRUCTION  APPROACHES – POINT CLOUD BASED VS. IMAGE PROCESSING BASED

COMPARISON BETWEEN TWO GENERIC 3D BUILDING RECONSTRUCTION APPROACHES – POINT CLOUD BASED VS. IMAGE PROCESSING BASED

In a next step the topology between the planes has to be recovered. For that reason the line segments are used again. All segments are features with a unique ID. While a plane is fitted to a significant cluster of segments, all respective IDs are linked this particular plane. A segment, which two planes have in common, represents a link between planes. A visualization of those linking segments can be found in figure 3. In terms of topology, a building can now be described by planes and edges. With the help of the planes’ geometrical information, namely normal vector and the edges’ topological information, nodes are reconstructed by intersecting three adjacent planes. If more than three planes belong to a node, the point coordinate is recovered by intersecting three planes several times and using the mean of the yielding points of intersections. If two adjacent planes don’t have another plane in common, it may be possible that a 4-node is needed to connect four adjacent planes, which occurs two times on the synthetic building. Therefor the search is extended, in such a way that two adjacent planes are associated to two other adjacent planes if they share common edge relations. The mean coordinate of all possible intersection points is the 4-node. A figure for the reconstructed building basically looks like figure 2 (bottom). More details on the differences of the results can be found in the next section.
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Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

Moment feature based fast feature extraction algorithm for moving object detection using aerial images.

Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmov- ing object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because mov- ing object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection perfor- mance. This research proposes a two-layer bucket approach based on a new feature ex- traction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the pro- posed algorithm. The experimental results reveal the successful performance of the pro- posed MFEA algorithm and the proposed methodology.
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HOMOLOGY MODELLING AND SEQUENCE ANALYSIS OF anxC3.1

HOMOLOGY MODELLING AND SEQUENCE ANALYSIS OF anxC3.1

The Basic Local Alignment Search Tool (BLAST) finds regions of local similarity between sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches. BLAST can be used to infer functional and evolutionary relationships between sequences as well as help identify members of gene families.

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Predicting chick body mass by artificial intelligence‑based models

Predicting chick body mass by artificial intelligence‑based models

LIN,  H.;  ZHANG,  H.F.;  JIAO,  H.C.;  ZHAO,  T.;  SUI,  S.J.;  GU,  X.H.;  ZHANG,  Z.Y.;  BUYSE,  J.;  DECUYPERE,  E.  Thermoregulation  responses  of  broiler  chickens  to  humidity  at  different  ambient  temperatures.  I.  One  week  of  age.  Poultry Science, v.84, p.1166‑1172, 2005. DOI: 10.1093/ps/84.8.1173. MATHWORKS.  Fuzzy logic toolbox:  user’s  guide.  Version  2011.  Available  at:  <http://www.mathworks.com/help/toolbox/ fuzzy/49243.html>. Accessed on: Oct. 2011.

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An integrated in silico approach to design specific inhibitors targeting human poly(a)-specific ribonuclease.

An integrated in silico approach to design specific inhibitors targeting human poly(a)-specific ribonuclease.

Poly(A)-specific ribonuclease (PARN) is an exoribonuclease/deadenylase that degrades 39-end poly(A) tails in almost all eukaryotic organisms. Much of the biochemical and structural information on PARN comes from the human enzyme. However, the existence of PARN all along the eukaryotic evolutionary ladder requires further and thorough investigation. Although the complete structure of the full-length human PARN, as well as several aspects of the catalytic mechanism still remain elusive, many previous studies indicate that PARN can be used as potent and promising anti-cancer target. In the present study, we attempt to complement the existing structural information on PARN with in-depth bioinformatics analyses, in order to get a hologram of the molecular evolution of PARNs active site. In an effort to draw an outline, which allows specific drug design targeting PARN, an unequivocally specific platform was designed for the development of selective modulators focusing on the unique structural and catalytic features of the enzyme. Extensive phylogenetic analysis based on all the publicly available genomes indicated a broad distribution for PARN across eukaryotic species and revealed structurally important amino acids which could be assigned as potentially strong contributors to the regulation of the catalytic mechanism of PARN. Based on the above, we propose a comprehensive in silico model for the PARN’s catalytic mechanism and moreover, we developed a 3D pharmacophore model, which was subsequently used for the introduction of DNP-poly(A) amphipathic substrate analog as a potential inhibitor of PARN. Indeed, biochemical analysis revealed that DNP- poly(A) inhibits PARN competitively. Our approach provides an efficient integrated platform for the rational design of pharmacophore models as well as novel modulators of PARN with therapeutic potential.
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Generating automatically test cases based on models

Generating automatically test cases based on models

The term automated software testing can have multiple meanings for members of the software development and testing community. To some the term may mean test driven development and /or unit testing; to others it may mean using a capture & record tool to automate testing. Or can even mean custom-developing test scripts using a scripting language such as Pearl, Python or Ruby. Generally, all tests that are currently run as part of a manual testing program - functional, performance, concurrency, stress, and more - can be automated. How is manual software testing different from AST? First of all, it enhances manual testing efforts by focusing on automating tests that manual testing can hardly accomplish. It doesn’t replace the need for manual testers analytical skills, test strategy know- how, and understanding of testing techniques. This manual tester expertise serves as the blueprint for AST. It also can’t be separated from the manual testing; instead both AST and manual testing are inter-winded and complement each other. AST refers to automation efforts across the entire STL, with a focus on automating the integration and system testing efforts. The overall objective of AST is to design, develop, and deliver an automated test and retest capability that increases testings efficiencies; if implemented successfully, it can result in a substantial reduction in the cost, time and recourses associated with traditional test and evaluation methods and processes for software-intensive systems.
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