scholarly journals AUTOMATIC ISOLATION OF BLURRED IMAGES FROM UAV IMAGE SEQUENCES

2013 ◽  
Vol XL-1/W2 ◽  
pp. 361-366 ◽  
Author(s):  
T. Sieberth ◽  
R. Wackrow ◽  
J. H. Chandler
Keyword(s):  
Image Sequences ◽  
Uav Image ◽  
Blurred Images

scholarly journals UAV IMAGE BLUR – ITS INFLUENCE AND WAYS TO CORRECT IT

2015 ◽  
Vol XL-1/W4 ◽  
pp. 33-39 ◽  
Author(s):  
T. Sieberth ◽  
R. Wackrow ◽  
J. H. Chandler
Keyword(s):  
Image Processing ◽  
Target Identification ◽  
Image Sequences ◽  
Identification Measurements ◽  
Filtering Technique ◽  
Coordinate Measurements ◽  
Active Research ◽  
Uav Image ◽  
Blurred Images ◽  
Degradation Effect

Unmanned aerial vehicles (UAVs) have become an interesting and active research topic in photogrammetry. Current research is based on image sequences acquired by UAVs which have a high ground resolution and good spectral resolution due to low flight altitudes combined with a high-resolution camera. One of the main problems preventing full automation of data processing of UAV imagery is the unknown degradation effect of blur caused by camera movement during image acquisition. <br><br> The purpose of this paper is to analyse the influence of blur on photogrammetric image processing, the correction of blur and finally, the use of corrected images for coordinate measurements. It was found that blur influences image processing significantly and even prevents automatic photogrammetric analysis, hence the desire to exclude blurred images from the sequence using a novel filtering technique. If necessary, essential blurred images can be restored using information of overlapping images of the sequence or a blur kernel with the developed edge shifting technique. The corrected images can be then used for target identification, measurements and automated photogrammetric processing.

Conflict analytics through the vehicle safety space in mixed traffic flows using UAV image sequences

2020 ◽  
Vol 119 ◽  
pp. 102744
Author(s):  
Albert Y. Chen ◽  
Yen-Lin Chiu ◽  
Meng-Hsiu Hsieh ◽  
Po-Wei Lin ◽  
Ohay Angah
Keyword(s):  
Image Sequences ◽  
Vehicle Safety ◽  
Mixed Traffic ◽  
Traffic Flows ◽  
Uav Image

FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV

2020 ◽  
Author(s):  
Anette Eltner ◽  
Jens Grundmann
Keyword(s):  
Flow Direction ◽  
Control Point ◽  
Region Of Interest ◽  
Software Tool ◽  
Image Sequences ◽  
Surface Flow ◽  
Area Of Interest ◽  
Image Velocimetry ◽  
Uav Image ◽  
Flow Velocities

&lt;p&gt;We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.&lt;/p&gt;&lt;p&gt;The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01&amp;#160;m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31&amp;#160;% (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.&lt;/p&gt;

scholarly journals Automatic detection of blurred images in UAV image sets

2016 ◽  
Vol 122 ◽  
pp. 1-16 ◽  
Author(s):  
Till Sieberth ◽  
Rene Wackrow ◽  
Jim H. Chandler
Keyword(s):  
Automatic Detection ◽  
Uav Image ◽  
Blurred Images

Recognition of Birds in Blurred and Illumination Images by Local Features

2017 ◽  
Vol 7 (7) ◽  
pp. 243
Author(s):  
Suresha .M ◽  
. Sandeep
Keyword(s):  
Computer Vision ◽  
Time Complexity ◽  
Feature Detection ◽  
Feature Matching ◽  
Fast Algorithms ◽  
Local Features ◽  
Experimental Demonstration ◽  
Harris Corner ◽  
Blurred Image ◽  
Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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