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.

Human Behavior Recognition Based on Motion Decomposition

2014 ◽  
Vol 945-949 ◽  
pp. 1780-1783
Author(s):  
Shao Ping Zhu ◽  
Yu Hua Chen
Keyword(s):  
Image Processing ◽  
Human Behavior ◽  
Video Sequence ◽  
Image Sequences ◽  
Research Field ◽  
Interest Points ◽  
Behavior Recognition ◽  
Novel Method ◽  
Active Research ◽  
Human Behavior Recognition

Human behavior recognition is an active research field in computer vision and image processing. A novel method is proposed for human behavior recognition in video image sequences. First of all, a video sequence is represented by extracting space-time interest points. Then Human behavior is represented by activities through Motion Decomposition. The activity comprises labeled bags that are composed of unlabeled instances comprising to action. Final labeled activities are used to train a strong classifier which is used to predict the labels of unseen behavior bags. Experimental results show the effectiveness of the proposed method in comparison with other related works in the literature and can also tolerate noise and interference conditions.

Study of Noise Removal Techniques for Digital Images

Biometrics ◽  
2017 ◽  
pp. 1105-1144
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

Study of Noise Removal Techniques for Digital Images

2016 ◽  
pp. 48-87
Author(s):  
Punyaban Patel ◽  
Bibekananda Jena ◽  
Bibhudatta Sahoo ◽  
Pritam Patel ◽  
Banshidhar Majhi
Keyword(s):  
Image Processing ◽  
Gaussian Noise ◽  
Statistical Models ◽  
Noise Removal ◽  
Image Degradation ◽  
Linear Filters ◽  
Restoration Techniques ◽  
Filtering Technique ◽  
High Level ◽  
Camera Sensors

Images very often get contaminated by different types of noise like impulse noise, Gaussian noise, spackle noise etc. due to malfunctioning of camera sensors during acquisition or transmission using the channel. The noise in the channel affects processing of images in various ways. Hence, the image has to be restored by applying filtration process before the high level image processing. In general the restoration techniques for images are based up on the mathematical and the statistical models of image degradation. Denoising and deblurring are used to recover the image from degraded observations. The researchers have proposed verity of linear and non-linear filters for removal of noise from images. The filtering technique has been used to remove noisy pixels, without changing the uncorrupted pixel values. This chapter presents the metrics used for measurement of noise, and the various schemes for removing of noise from the images.

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

An Open-Source UAV Image Processing Web Service for Crop Health Monitoring

2019 ◽  
Author(s):  
Bipul Neupane ◽  
Teerayut Horanont ◽  
Hung Nguyen Duy ◽  
Sudshewin Suebvong ◽  
Tanadol Mahattanawutakorn
Keyword(s):  
Image Processing ◽  
Web Service ◽  
Open Source ◽  
Health Monitoring ◽  
Uav Image ◽  
Crop Health

Evaluation of Computer-assisted Image Enhancement in Minimal Invasive Endoscopic Surgery

2004 ◽  
Vol 43 (04) ◽  
pp. 362-366 ◽  
Author(s):  
F. Vogt ◽  
W. Hohenberger ◽  
D. Paulus ◽  
H. Niemann ◽  
C. H. Schick ◽  
...  
Keyword(s):  
Image Processing ◽  
Minimal Invasive ◽  
Image Sequences ◽  
Computer Assisted ◽  
Processing Methods ◽  
Temporal Filtering ◽  
Endoscopic Images ◽  
Filter Size ◽  
Image Pairs

Summary Objectives: This paper focusses on the evaluation of the usage of computer-aided image processing methods for minimal invasive surgery. During video endoscopy of visceral cavities the images are displayed directly on the monitor without further processing. In the course of the operation the former good quality of the images decreases due to typical disturbances like bleeding, smoke or flying particles. These disturbances can be reduced by using image processing methods like color normalization, temporal filtering or equalization. Methods: In this double-blinded analysis, 14 surgeons with different levels of experience evaluated 120 image pairs and 5 image sequences, directly comparing original and processed images or movies. Results: Color normalization and equalization proved to significantly enhance video endoscopic images. With regard to temporal filtering, an improvement could be seen in the image sequences with filter size 5 being a greater enhancement than filter size 3. Comparing the state of experience and its influence on the results, it occurred that the experienced surgeons preferred the original color while altogether agreeing that the color-normalized images were better. Conclusions: The results obtained in the present evaluation show that the image processing methods which were used can significantly improve the quality of video endoscopic images. As a result of this, necessary lavages of the operated area are reduced and a better overview and orientation for the surgeon can be reached.

3-D CARDIAC MODELING: A COMPUTER VISION APPROACH IN SPATIO-TEMPORAL IMAGE SEQUENCES

1996 ◽  
Vol 04 (02) ◽  
pp. 181-197 ◽  
Author(s):  
J.L. COATRIEUX ◽  
C. TOUMOULIN ◽  
R. COLLOREC
Keyword(s):  
Computer Vision ◽  
Three Dimensional ◽  
Object Identification ◽  
Image Sequences ◽  
Low Contrast ◽  
Dimensional Reconstruction ◽  
Contrast Structures ◽  
Computer Vision Applications ◽  
Spatio Temporal ◽  
Active Research

A very active research was conducted on motion analysis. Most of the concepts, methods and assumptions are well established and lead to additional improvements in computer vision applications. Even in medicine where we have to deal with noisy data, low contrast structures and deformable objects, they bring new cues at all the processing stages. This paper emphasizes the specificities of this area and also the potential difficulties. A compilation of results is given aimed at the quantification of heart kinetics in Digital Subtraction Angiography (DSA). They illustrate the benefits of cooperative schemes such as motion based segmentation, moving object identification, three-dimensional reconstruction and interpretation.

Sign in / Sign up

Export Citation Format

Share Document