SUB-CAMERA CALIBRATION OF A PENTA-CAMERA

2016 ◽

Vol XL-3/W4 ◽

pp. 35-40

Author(s):

K. Jacobsen ◽

M. Gerke

Keyword(s):

Data Sets ◽

Object Point ◽

Exterior Orientation ◽

Self Calibration ◽

Bundle Block Adjustment ◽

Dense Matching ◽

Laboratory Calibration ◽

Strongly Connected ◽

Limited Base ◽

Very High

Penta cameras consisting of a nadir and four inclined cameras are becoming more and more popular, having the advantage of imaging also facades in built up areas from four directions. Such system cameras require a boresight calibration of the geometric relation of the cameras to each other, but also a calibration of the sub-cameras. Based on data sets of the ISPRS/EuroSDR benchmark for multi platform photogrammetry the inner orientation of the used IGI Penta DigiCAM has been analyzed. The required image coordinates of the blocks Dortmund and Zeche Zollern have been determined by Pix4Dmapper and have been independently adjusted and analyzed by program system BLUH. With 4.1 million image points in 314 images respectively 3.9 million image points in 248 images a dense matching was provided by Pix4Dmapper. With up to 19 respectively 29 images per object point the images are well connected, nevertheless the high number of images per object point are concentrated to the block centres while the inclined images outside the block centre are satisfying but not very strongly connected. This leads to very high values for the Student test (T-test) of the finally used additional parameters or in other words, additional parameters are highly significant. The estimated radial symmetric distortion of the nadir sub-camera corresponds to the laboratory calibration of IGI, but there are still radial symmetric distortions also for the inclined cameras with a size exceeding 5μm even if mentioned as negligible based on the laboratory calibration. Radial and tangential effects of the image corners are limited but still available. Remarkable angular affine systematic image errors can be seen especially in the block Zeche Zollern. Such deformations are unusual for digital matrix cameras, but it can be caused by the correlation between inner and exterior orientation if only parallel flight lines are used. With exception of the angular affinity the systematic image errors for corresponding cameras of both blocks have the same trend, but as usual for block adjustments with self calibration, they still show significant differences. Based on the very high number of image points the remaining image residuals can be safely determined by overlaying and averaging the image residuals corresponding to their image coordinates. The size of the systematic image errors, not covered by the used additional parameters, is in the range of a square mean of 0.1 pixels corresponding to 0.6μm. They are not the same for both blocks, but show some similarities for corresponding cameras. In general the bundle block adjustment with a satisfying set of additional parameters, checked by remaining systematic errors, is required for use of the whole geometric potential of the penta camera. Especially for object points on facades, often only in two images and taken with a limited base length, the correct handling of systematic image errors is important. At least in the analyzed data sets the self calibration of sub-cameras by bundle block adjustment suffers from the correlation of the inner to the exterior calibration due to missing crossing flight directions. As usual, the systematic image errors differ from block to block even without the influence of the correlation to the exterior orientation.

Download Full-text

Moving Forward With Accelerometer-Assessed Physical Activity: Two Strategies to Ensure Meaningful, Interpretable, and Comparable Measures

Pediatric Exercise Science ◽

10.1123/pes.2018-0201 ◽

2018 ◽

Vol 30 (4) ◽

pp. 450-456 ◽

Cited By ~ 19

Author(s):

Alex V. Rowlands

Keyword(s):

Physical Activity ◽

Black Box ◽

Data Sets ◽

Accelerometer Data ◽

Future Health ◽

The Past ◽

Acceleration Data ◽

Night And Day ◽

Very High ◽

Made In

Significant advances have been made in the measurement of physical activity in youth over the past decade. Monitors and protocols promote very high compliance, both night and day, and raw measures are available rather than “black box” counts. Consequently, many surveys and studies worldwide now assess children’s physical behaviors (physical activity, sedentary behavior, and sleep) objectively 24 hours a day, 7 days a week using accelerometers. The availability of raw acceleration data in many of these studies is both an opportunity and a challenge. The richness of the data lends itself to the continued development of innovative metrics, whereas the removal of proprietary outcomes offers considerable potential for comparability between data sets and harmonizing data. Using comparable physical activity outcomes could lead to improved precision and generalizability of recommendations for children’s present and future health. The author will discuss 2 strategies that he believes may help ensure comparability between studies and maximize the potential for data harmonization, thereby helping to capitalize on the growing body of accelerometer data describing children’s physical behaviors.

Download Full-text

VOLUME BASED DTM GENERATION FROM VERY HIGH RESOLUTION PHOTOGRAMMETRIC DSMS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-83-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 83-90 ◽

Cited By ~ 5

Author(s):

B. Piltz ◽

S. Bayer ◽

A. M. Poznanska

Keyword(s):

Ground Surface ◽

Processing Parameters ◽

Data Sets ◽

Maximum Width ◽

Directional Filtering ◽

Very High Spatial Resolution ◽

Efficient Scheme ◽

Data Points ◽

Minimum Height ◽

Very High

In this paper we propose a new algorithm for digital terrain (DTM) model reconstruction from very high spatial resolution digital surface models (DSMs). It represents a combination of multi-directional filtering with a new metric which we call normalized volume above ground to create an above-ground mask containing buildings and elevated vegetation. This mask can be used to interpolate a ground-only DTM. The presented algorithm works fully automatically, requiring only the processing parameters minimum height and maximum width in metric units. Since slope and breaklines are not decisive criteria, low and smooth and even very extensive flat objects are recognized and masked. The algorithm was developed with the goal to generate the normalized DSM for automatic 3D building reconstruction and works reliably also in environments with distinct hillsides or terrace-shaped terrain where conventional methods would fail. A quantitative comparison with the ISPRS data sets Potsdam and Vaihingen show that 98-99% of all building data points are identified and can be removed, while enough ground data points (~66%) are kept to be able to reconstruct the ground surface. Additionally, we discuss the concept of size dependent height thresholds and present an efficient scheme for pyramidal processing of data sets reducing time complexity to linear to the number of pixels, O(WH).

Download Full-text

True-orthophoto generation from UAV images: Implementation of a combined photogrammetric and computer vision approach

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-5-57-2014 ◽

2014 ◽

Vol II-5 ◽

pp. 57-63 ◽

Cited By ~ 9

Author(s):

L. Barazzetti ◽

R. Brumana ◽

D. Oreni ◽

M. Previtali ◽

F. Roncoroni

Keyword(s):

3D Model ◽

Bundle Block Adjustment ◽

Dense Matching ◽

Automated Data Processing ◽

Feature Based ◽

Correlation Techniques ◽

Uav Images ◽

Image Orientation ◽

Santa Maria

This paper presents a photogrammetric methodology for true-orthophoto generation with images acquired from UAV platforms. The method is an automated multistep workflow made up of three main parts: (i) image orientation through feature-based matching and collinearity equations / bundle block adjustment, (ii) dense matching with correlation techniques able to manage multiple images, and true-orthophoto mapping for 3D model texturing. It allows automated data processing of sparse blocks of convergent images in order to obtain a final true-orthophoto where problems such as self-occlusions, ghost effects, and multiple texture assignments are taken into consideration. The different algorithms are illustrated and discussed along with a real case study concerning the UAV flight over the Basilica di Santa Maria di Collemaggio in L'Aquila (Italy). The final result is a rigorous true-orthophoto used to inspect the roof of the Basilica, which was seriously damaged by the earthquake in 2009.

Download Full-text

A General Point-Based Method for Self-Calibration of Terrestrial Laser Scanners Considering Stochastic Information

Remote Sensing ◽

10.3390/rs12182923 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2923

Author(s):

Tengfei Zhou ◽

Xiaojun Cheng ◽

Peng Lin ◽

Zhenlun Wu ◽

Ensheng Liu

Keyword(s):

Point Cloud ◽

A Priori ◽

Real Data ◽

Point Clouds ◽

Estimation Algorithm ◽

General Point ◽

Exterior Orientation ◽

Self Calibration ◽

Laser Scanners ◽

Orientation Parameters

Due to the existence of environmental or human factors, and because of the instrument itself, there are many uncertainties in point clouds, which directly affect the data quality and the accuracy of subsequent processing, such as point cloud segmentation, 3D modeling, etc. In this paper, to address this problem, stochastic information of point cloud coordinates is taken into account, and on the basis of the scanner observation principle within the Gauss–Helmert model, a novel general point-based self-calibration method is developed for terrestrial laser scanners, incorporating both five additional parameters and six exterior orientation parameters. For cases where the instrument accuracy is different from the nominal ones, the variance component estimation algorithm is implemented for reweighting the outliers after the residual errors of observations obtained. Considering that the proposed method essentially is a nonlinear model, the Gauss–Newton iteration method is applied to derive the solutions of additional parameters and exterior orientation parameters. We conducted experiments using simulated and real data and compared them with those two existing methods. The experimental results showed that the proposed method could improve the point accuracy from 10−4 to 10−8 (a priori known) and 10−7 (a priori unknown), and reduced the correlation among the parameters (approximately 60% of volume). However, it is undeniable that some correlations increased instead, which is the limitation of the general method.

Download Full-text

Biomedical Data Mining Using RBF Neural Networks

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch021 ◽

2011 ◽

pp. 106-111

Author(s):

Fang Chu ◽

Lipo Wang

Keyword(s):

Neural Networks ◽

Microarray Data ◽

High Accuracy ◽

Accurate Diagnosis ◽

Data Sets ◽

Biomedical Data ◽

Rbf Neural Networks ◽

Proper Treatment ◽

Pattern Recognition Problem ◽

Very High

Accurate diagnosis of cancers is of great importance for doctors to choose a proper treatment. Furthermore, it also plays a key role in the searching for the pathology of cancers and drug discovery. Recently, this problem attracts great attention in the context of microarray technology. Here, we apply radial basis function (RBF) neural networks to this pattern recognition problem. Our experimental results in some well-known microarray data sets indicate that our method can obtain very high accuracy with a small number of genes.

Download Full-text

Monitoring the Extragalactic High Energy Sky

Galaxies ◽

10.3390/galaxies7010009 ◽

2019 ◽

Vol 7 (1) ◽

pp. 9 ◽

Cited By ~ 2

Author(s):

Jean-Philippe Lenain

Keyword(s):

Active Galactic Nuclei ◽

Galactic Nuclei ◽

High Energy ◽

Line Of Sight ◽

Data Sets ◽

High Energies ◽

Multi Wavelength ◽

Rich Data ◽

Very High

Blazars are jetted active galactic nuclei with a jet pointing close to the line of sight, hence enhancing their intrinsic luminosity and variability. Monitoring these sources is essential in order to catch them flaring and promptly organize follow-up multi-wavelength observations, which are key to providing rich data sets used to derive e.g., the emission mechanisms at work, and the size and location of the flaring zone. In this context, the Fermi-LAT has proven to be an invaluable instrument, whose data are used to trigger many follow-up observations at high and very high energies. A few examples are illustrated here, as well as a description of different data products and pipelines, with a focus given on FLaapLUC, a tool in use within the H.E.S.S. collaboration.

Download Full-text

An Object-Based Bidirectional Method for Integrated Building Extraction and Change Detection between Multimodal Point Clouds

Remote Sensing ◽

10.3390/rs12101680 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1680

Author(s):

Chenguang Dai ◽

Zhenchao Zhang ◽

Dong Lin

Keyword(s):

Change Detection ◽

Laser Scanning ◽

Point Clouds ◽

Recall Rate ◽

Data Sets ◽

Building Extraction ◽

Dense Matching ◽

Object Based ◽

Alignment Errors ◽

Precision Rate

Building extraction and change detection are two important tasks in the remote sensing domain. Change detection between airborne laser scanning data and photogrammetric data is vulnerable to dense matching errors, mis-alignment errors and data gaps. This paper proposes an unsupervised object-based method for integrated building extraction and change detection. Firstly, terrain, roofs and vegetation are extracted from the precise laser point cloud, based on “bottom-up” segmentation and clustering. Secondly, change detection is performed in an object-based bidirectional manner: Heightened buildings and demolished buildings are detected by taking the laser scanning data as reference, while newly-built buildings are detected by taking the dense matching data as reference. Experiments on two urban data sets demonstrate its effectiveness and robustness. The object-based change detection achieves a recall rate of 92.31% and a precision rate of 88.89% for the Rotterdam dataset; it achieves a recall rate of 85.71% and a precision rate of 100% for the Enschede dataset. It can not only extract unchanged building footprints, but also assign heightened or demolished labels to the changed buildings.

Download Full-text