scholarly journals Integration of prior knowledge into dense image matching for video surveillance

2014 ◽  
Vol XL-3 ◽  
pp. 227-230
Author(s):  
M. Menze ◽  
C. Heipke
Keyword(s):  
Prior Knowledge ◽  
Video Surveillance ◽  
Image Matching ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Point Clouds ◽  
Search Space ◽  
Surveillance Camera ◽  
Dense Image ◽  
Dense Point

Three-dimensional information from dense image matching is a valuable input for a broad range of vision applications. While reliable approaches exist for dedicated stereo setups they do not easily generalize to more challenging camera configurations. In the context of video surveillance the typically large spatial extent of the region of interest and repetitive structures in the scene render the application of dense image matching a challenging task. In this paper we present an approach that derives strong prior knowledge from a planar approximation of the scene. This information is integrated into a graph-cut based image matching framework that treats the assignment of optimal disparity values as a labelling task. Introducing the planar prior heavily reduces ambiguities together with the search space and increases computational efficiency. The results provide a proof of concept of the proposed approach. It allows the reconstruction of dense point clouds in more general surveillance camera setups with wider stereo baselines.

scholarly journals Super Edge 4-Points Congruent Sets-Based Point Cloud Global Registration

2021 ◽  
Vol 13 (16) ◽  
pp. 3210
Author(s):  
Shikun Li ◽  
Ruodan Lu ◽  
Jianya Liu ◽  
Liang Guo
Keyword(s):  
Three Dimensional ◽  
Initial Point ◽  
Point Clouds ◽  
Search Space ◽  
Frame Rate ◽  
Registration Method ◽  
Dense Point ◽  
Coarse Registration ◽  
Fine Registration ◽  
Original Point

With the acceleration in three-dimensional (3D) high-frame-rate sensing technologies, dense point clouds collected from multiple standpoints pose a great challenge for the accuracy and efficiency of registration. The combination of coarse registration and fine registration has been extensively promoted. Unlike the requirement of small movements between scan pairs in fine registration, coarse registration can match scans with arbitrary initial poses. The state-of-the-art coarse methods, Super 4-Points Congruent Sets algorithm based on the 4-Points Congruent Sets, improves the speed of registration to a linear order via smart indexing. However, the lack of reduction in the scale of original point clouds limits the application. Besides, the coplanarity of registration bases prevents further reduction of search space. This paper proposes a novel registration method called the Super Edge 4-Points Congruent Sets to address the above problems. The proposed algorithm follows a three-step procedure, including boundary segmentation, overlapping regions extraction, and bases selection. Firstly, an improved method based on vector angle is used to segment the original point clouds aiming to thin out the scale of the initial point clouds. Furthermore, overlapping regions extraction is executed to find out the overlapping regions on the contour. Finally, the proposed method selects registration bases conforming to the distance constraints from the candidate set without consideration about coplanarity. Experiments on various datasets with different characteristics have demonstrated that the average time complexity of the proposed algorithm is improved by 89.76%, and the accuracy is improved by 5 mm on average than the Super 4-Points Congruent Sets algorithm. More encouragingly, the experimental results show that the proposed algorithm can be applied to various restrictive cases, such as few overlapping regions and massive noise. Therefore, the algorithm proposed in this paper is a faster and more robust method than Super 4-Points Congruent Sets under the guarantee of the promised quality.

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals A CASE STUDY ON THROUGH-WATER DENSE IMAGE MATCHING

2018 ◽  
Vol XLII-2 ◽  
pp. 659-666 ◽  
Author(s):  
G. Mandlburger
Keyword(s):  
Image Matching ◽  
Water Surface ◽  
Point Clouds ◽  
Water Clarity ◽  
Aerial Images ◽  
Mean Deviation ◽  
Range Image ◽  
Surface Mapping ◽  
Dense Image

In the last years, the tremendous progress in image processing and camera technology has reactivated the interest in photogrammetrybased surface mapping. With the advent of Dense Image Matching (DIM), the derivation of height values on a per-pixel basis became feasible, allowing the derivation of Digital Elevation Models (DEM) with a spatial resolution in the range of the ground sampling distance of the aerial images, which is often below 10 cm today. While mapping topography and vegetation constitutes the primary field of application for image based surface reconstruction, multi-spectral images also allow to see through the water surface to the bottom underneath provided sufficient water clarity. In this contribution, the feasibility of through-water dense image matching for mapping shallow water bathymetry using off-the-shelf software is evaluated. In a case study, the SURE software is applied to three different coastal and inland water bodies. After refraction correction, the DIM point clouds and the DEMs derived thereof are compared to concurrently acquired laser bathymetry data. The results confirm the general suitability of through-water dense image matching, but sufficient bottom texture and favorable environmental conditions (clear water, calm water surface) are a preconditions for achieving accurate results. Water depths of up to 5 m could be mapped with a mean deviation between laser and trough-water DIM in the dm-range. Image based water depth estimates, however, become unreliable in case of turbid or wavy water and poor bottom texture.

scholarly journals AN AUTOMATIC FILTER ALGORITHM FOR DENSE IMAGE MATCHING POINT CLOUDS

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 703-709 ◽  
Author(s):  
Y. Q. Dong ◽  
L. Zhang ◽  
X. M. Cui ◽  
H. B. Ai
Keyword(s):  
Image Matching ◽  
Iterative Process ◽  
Point Clouds ◽  
High Density ◽  
Aerial Images ◽  
Dense Image ◽  
Seed Points ◽  
Filter Algorithms

Although many filter algorithms have been presented over past decades, these algorithms are usually designed for the Lidar point clouds and can’t separate the ground points from the DIM (dense image matching, DIM) point clouds derived from the oblique aerial images owing to the high density and variation of the DIM point clouds completely. To solve this problem, a new automatic filter algorithm is developed on the basis of adaptive TIN models. At first, the differences between Lidar and DIM point clouds which influence the filtering results are analysed in this paper. To avoid the influences of the plants which can’t be penetrated by the DIM point clouds in the searching seed pointes process, the algorithm makes use of the facades of buildings to get ground points located on the roads as seed points and construct the initial TIN. Then a new densification strategy is applied to deal with the problem that the densification thresholds do not change as described in other methods in each iterative process. Finally, we use the DIM point clouds located in Potsdam produced by Photo-Scan to evaluate the method proposed in this paper. The experiment results show that the method proposed in this paper can not only separate the ground points from the DIM point clouds completely but also obtain the better filter results compared with TerraSolid. 1.

scholarly journals TEXTURE-AWARE DENSE IMAGE MATCHING USING TERNARY CENSUS TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 59-66 ◽  
Author(s):  
Han Hu ◽  
Chongtai Chen ◽  
Bo Wu ◽  
Xiaoxia Yang ◽  
Qing Zhu ◽  
...  
Keyword(s):  
Image Matching ◽  
Urban Areas ◽  
Point Clouds ◽  
Parameter Tuning ◽  
Census Transform ◽  
Urban Scenes ◽  
Dense Image ◽  
Global Matching ◽  
Matching Cost ◽  
Sharp Edges

Textureless and geometric discontinuities are major problems in state-of-the-art dense image matching methods, as they can cause visually significant noise and the loss of sharp features. Binary census transform is one of the best matching cost methods but in textureless areas, where the intensity values are similar, it suffers from small random noises. Global optimization for disparity computation is inherently sensitive to parameter tuning in complex urban scenes, and must compromise between smoothness and discontinuities. The aim of this study is to provide a method to overcome these issues in dense image matching, by extending the industry proven Semi-Global Matching through 1) developing a ternary census transform, which takes three outputs in a single order comparison and encodes the results in two bits rather than one, and also 2) by using texture-information to self-tune the parameters, which both preserves sharp edges and enforces smoothness when necessary. Experimental results using various datasets from different platforms have shown that the visual qualities of the triangulated point clouds in urban areas can be largely improved by these proposed methods.

Complementing TLS Point Clouds by Dense Image Matching

2013 ◽  
Vol 2 (3) ◽  
pp. 453-470 ◽  
Author(s):  
Wassim Moussa ◽  
Konrad Wenzel ◽  
Mathias Rothermel ◽  
Mohammed Abdel-Wahab ◽  
Dieter Fritsch
Keyword(s):  
Image Matching ◽  
Point Clouds ◽  
Dense Image

scholarly journals QUALITY ASPECTS OF TRUE ORTHOPHOTO IN URBAN AREAS

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 191-198
Author(s):  
W. Ostrowski ◽  
V. D. Gulli ◽  
K. Bakula ◽  
Z. Kurczyński
Keyword(s):  
Image Matching ◽  
Urban Areas ◽  
Quantitative Methods ◽  
Quality Evaluation ◽  
Point Clouds ◽  
Dense Image ◽  
Quality Aspects ◽  
Surface Models ◽  
Recent Developments ◽  
2D Data

Abstract. Orthophotos are one of the most popular photogrammetric products and have been a leading source of up-to-date 2D data of urban areas for years. In the last few years, together with innovations in the area of Dense Image Matching, Digital Surface Models created with dense image matching start to be utilized as the height source during orthorectification. Recently this production workflow of true orthophotos were adopted to production standard in many countries. The aim of the presented research was to evaluate recent developments in the area of automatic true orthophoto generation for urban areas and to define factors which have the main influence on the quality of the final product. Obtained results showed that besides of the image overlap, the main factors which have direct influence on the resulted true orthophoto are the occurrence of shadows and vegetation (trees). One of the outcomes of the presented research was that the quantitative methods develop for quality evaluation of Digital Surface Models and Point Clouds are not directly transferable on the quality evaluation of true orthophotos.

scholarly journals A SMARTPHONE-BASED 3D PIPELINE FOR THE CREATIVE INDUSTRY – THE REPLICATE EU PROJECT

2017 ◽  
Vol XLII-2/W3 ◽  
pp. 535-541 ◽  
Author(s):  
E. Nocerino ◽  
F. Lago ◽  
D. Morabito ◽  
F. Remondino ◽  
L. Porzi ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Point Clouds ◽  
First Year ◽  
Automated Generation ◽  
Dense Point ◽  
Modelling Techniques ◽  
Eu Project ◽  
Selection Of ◽  
Made In

During the last two decades we have witnessed great improvements in ICT hardware and software technologies. Three-dimensional content is starting to become commonplace now in many applications. Although for many years 3D technologies have been used in the generation of assets by researchers and experts, nowadays these tools are starting to become commercially available to every citizen. This is especially the case for smartphones, that are powerful enough and sufficiently widespread to perform a huge variety of activities (e.g. paying, calling, communication, photography, navigation, localization, etc.), including just very recently the possibility of running 3D reconstruction pipelines. The REPLICATE project is tackling this particular issue, and it has an ambitious vision to enable ubiquitous 3D creativity via the development of tools for mobile 3D-assets generation on smartphones/tablets. This article presents the REPLICATE project’s concept and some of the ongoing activities, with particular attention being paid to advances made in the first year of work. Thus the article focuses on the system architecture definition, selection of optimal frames for 3D cloud reconstruction, automated generation of sparse and dense point clouds, mesh modelling techniques and post-processing actions. Experiments so far were concentrated on indoor objects and some simple heritage artefacts, however, in the long term we will be targeting a larger variety of scenarios and communities.

scholarly journals Measuring Three-Dimensional Temperature Distributions in Steel–Concrete Composite Slabs Subjected to Fire Using Distributed Fiber Optic Sensors

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5518
Author(s):  
Yi Bao ◽  
Matthew S. Hoehler ◽  
Christopher M. Smith ◽  
Matthew Bundy ◽  
Genda Chen
Keyword(s):  
Fiber Optic ◽  
Three Dimensional ◽  
Thermal Response ◽  
Point Clouds ◽  
Fiber Optic Sensors ◽  
Distributed Sensors ◽  
Temperature Distributions ◽  
Composite Slabs ◽  
Spatially Distributed ◽  
Dense Point

Detailed information about temperature distribution can be important to understand structural behavior in fire. This study develops a method to image three-dimensional temperature distributions in steel–concrete composite slabs using distributed fiber optic sensors. The feasibility of the method is explored using six 1.2 m × 0.9 m steel–concrete composite slabs instrumented with distributed sensors and thermocouples subjected to fire for over 3 h. Dense point clouds of temperature in the slabs were measured using the distributed sensors. The results show that the distributed sensors operated at material temperatures up to 960 °C with acceptable accuracy for many structural fire applications. The measured non-uniform temperature distributions indicate a spatially distributed thermal response in steel–concrete composite slabs, which can only be adequately captured using approaches that provide a high density of through-depth data points.

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