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Influence of blur on feature matching and a geometric approach for photogrammetric deblurring

T. Sieberth, R. Wackrow, J. H. Chandler
2014 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
UAV image flights are also cost efficient and have become attractive for many applications including change detection in small scale areas.  ...  <br><br> The aim of this research is to develop a blur correction method to deblur UAV images.  ...  With increasing image blur the number of detected and accepted feature points reduces rapidly.  ... 
doi:10.5194/isprsarchives-xl-3-321-2014 fatcat:3azrhf452zhvxcainb56x7uk54

Restoration of Partial Blurred Image Based on Blur Detection and Classification

Dong Yang, Shiyin Qin
2016 Journal of Electrical and Computer Engineering  
A new restoration algorithm for partial blurred image which is based on blur detection and classification is proposed in this paper.  ...  Firstly, a new blur detection algorithm is proposed to detect the blurred regions in the partial blurred image. Then, a new blur classification algorithm is proposed to classify the blurred regions.  ...  Shi's algorithm [5] performs well in blur detection, so it is adopted to get the coarse detection result. The pixel value of each point in their result image is corresponding to the blur degree.  ... 
doi:10.1155/2016/2374926 fatcat:4fmvhvxhrbdeflzfmxwexfpmxq

Iris Image Blur Detection with Multiple Kernel Learning

Lili PAN, Mei XIE, Ling MAO
2012 IEICE transactions on information and systems  
In this letter, we analyze the influence of motion and outof-focus blur on both frequency spectrum and cepstrum of an iris image.  ...  To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine.  ...  A significant point in no-reference image blur detection is modeling blur features.  ... 
doi:10.1587/transinf.e95.d.1698 fatcat:bnouti5s5jd4xhgbe7grh3pnxi

Blur Invariant Features For Exposing Region Duplication Forgery Using ANMS And Local Phase Quantization

Diaa Uliyan, Mohammed A. Fadhil Al-Husainy, Ahmad Mousse Altamimi
2018 Informatica (Ljubljana, Tiskana izd.)  
In digital image forensics, local interest points can be employed to faithfully detect region duplication forgery.  ...  They can be exposed based on Scale-Invariant Features Transform (SIFT) algorithm. Here, we provide an image forgery detection technique by using local interest points.  ...  Conversely, the Keypoint based method detects local interest points to find primitive features in the image.  ... 
doi:10.31449/inf.v42i4.1914 fatcat:updy5bpurvh3tmmp2ciuw45nqy

An Adaptive Deblurring Vehicle Detection Method for High-Speed Moving Drones: Resistance to Shake

Yan Liu, Jingwen Wang, Tiantian Qiu, Wenting Qi
2021 Entropy  
The proposed method can enhance the vehicle feature details in blurred images effectively and improve the detection accuracy of blurred aerial images, which shows good performance with regard to resistance  ...  There is a challenge for detectors to accurately locate vehicles in blurred images in the target detection process.  ...  Drone-GAN can enhance vehicle features in blurred images, so that vehicles in blurred images can be detected more easily and accurately, which has high robustness towards small target and target occlusion  ... 
doi:10.3390/e23101358 pmid:34682082 fatcat:zwbc5k6x4fddfih7c3wz5dt34a

Image Blur Assessment with Feature Points

Hao Cai, Leida Li, Jiansheng Qian, Jeng-Shyang Pan
2015 Journal of Information Hiding and Multimedia Signal Processing  
Blur is a key factor in the perception of image quality, leading to spread of edges in images. The quantity of feature points extracted from images can represent image shape changes.  ...  In this paper, we propose a new blind blur assessment metric based on feature points. First, we apply Gaussian blur to the blurred image, producing the re-blurred image.  ...  Blur is typically characterized by the spread of edges in images. Feature point detection is an important procedure in many computer vision applications, which is sensitive to image shape changes.  ... 
dblp:journals/jihmsp/CaiLQ015 fatcat:ribrq563wbcavmamfji5eces2y

Evaluating performance of feature extraction methods for practical 3D imaging systems

Deepak Dwarakanath, Alexander Eichhorn, Pål Halvorsen, Carsten Griwodz
2012 Proceedings of the 27th Conference on Image and Vision Computing New Zealand - IVCNZ '12  
In practical imaging systems, we encounter several issues such as blur, lens distortion and thermal noise that affect the accuracy of feature detectors.  ...  Our experimental results show significant performance differences between feature extractors' performance in terms of accuracy, execution time and robustness to blur, lens distortion and thermal noise  ...  This behavior is because SURF and ORB detect more features which are not supposed to be, in noisy images.  ... 
doi:10.1145/2425836.2425887 dblp:conf/ivcnz/DwarakanathEHG12 fatcat:zsnd6h2gunbcxj6jcaygyj6qqu

Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements [article]

Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä
2018 arXiv   pre-print
Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors.  ...  The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data.  ...  This may help in feature matching, but the number of detected features is usually greatly reduced in the presence of motion blur which is a problem especially when the number of distinctive features is  ... 
arXiv:1805.08542v1 fatcat:lbnjxr4i3vgwjf6sbwdctkjvdu

Reliable features matching for humanoid robots

Alberto Pretto, Emanuele Menegatti, Enrico Pagello
2007 2007 7th IEEE-RAS International Conference on Humanoid Robots  
This paper presents a method to detect image interest points (invariant to scale transformation and rotations) robust to motion-blur introduced by the camera motion.  ...  Our approach presents higher performances and higher reliability in matching features in the different images of a sequence affected by motion-blur.  ...  SIFT features implementations usually double the image size before starting features detection in order create more sample points.  ... 
doi:10.1109/ichr.2007.4813922 dblp:conf/humanoids/PrettoMP07 fatcat:vv3vzotk6ndmlkhqgvzakn2cb4

Automation in Blur Image Detection with Segmentation using Machine learning

Mahak Gupta, Jaspreet Kaur
2019 IJARCCE  
These set of images are trained using both support vector machine as well as logistic regression which are tested on the real time images which detects the blur from the images.  ...  Blur is classified in two types (Local and Global Blur).  ...  In technical language, blurred image is combination of sharp image and point spread function. The shape of PSF (Point Spread Function) depends on type of blur.  ... 
doi:10.17148/ijarcce.2019.8301 fatcat:hfmua2mgznbdtpn5nk2hkgcaue

Recognition of Birds in Blurred and Illumination Images by Local Features

Suresha .M, . Sandeep
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
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.  ...  When n>12, then the number of interest points identified are very low. For example, a blurred image. Fig. 10 shows the FAST algorithms detect the features for blurred image. C.  ... 
doi:10.23956/ijarcsse/v7i7/0128 fatcat:sewb7hrl2zdkjkk6rvgwn6nroy

Tracking and Blurring the Face in a Video File

Farah Saad Al-Mukhtar
2018 Al-Nahrain Journal of Science  
This paper shows how to detect, track and blur a face in video frame using the Viola-Jones detection algorithm for detection and KLT algorithm to tracks a set of feature points across the video frames.  ...  First, the detection locates the face, and then identifies feature points that can be reliably tracked.  ...  Blur is an optical feature of image in which can makes something to be unclear to observer.  ... 
doi:10.22401/anjs.00.1.27 fatcat:eq3dsjrrjfasljlx365o7olssq

Improving surf interest point detection for defocus blur robustness

Elhusain Saad, Keigo Hirakawa
2015 2015 IEEE International Conference on Image Processing (ICIP)  
In this article, we propose a modification to SURF (Speeded Up Robust Features) to make the feature detection invariant to defocus blur.  ...  The proposed defocus blur invariant SURF-which we refer to as DBI-SURF-does not require image deblurring nor blur kernel estimation, meaning that its accuracy does not depend on the quality of image deblurring  ...  Fig. 3 . 3 (left column) Repeatability of interest point detection as a function of blur using Figure 2(a,e,i) as reference images.  ... 
doi:10.1109/icip.2015.7351359 dblp:conf/icip/SaadH15 fatcat:aegbwvhtf5gyjjvdq367v2nqha

Blur-Countering Keypoint Detection via Eigenvalue Asymmetry

Chao Zhang, Xuequan Lu, Takuya Akashi
2020 IEEE Access  
However, detecting keypoints in the presence of blur has remained to be an unresolved issue.  ...  To settle this issue, we propose a blur-countering method for detecting valid keypoints for various types and degrees of blurred images.  ...  (a) Top-500 keypoints detected by Fast-Hessian from SURF [8] in both blurred/unblurred images are shown. Blobs detected by Fast-Hessian tend to be aggregated in regions with sharp features.  ... 
doi:10.1109/access.2020.3020561 fatcat:dp5mniqx4rekpktyybzlvvi72a

A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude

Shamik Tiwari, V. P. Shukla, S. R. Biradar, A. K. Singh
2014 Advances in Electrical Engineering  
This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation.  ...  In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps.  ...  In the ROC graph the recall (0 0 0.2 PC features GM features Hybrid features 0.4 False positive rate 0.6 0.8 1 Table 3 : 3 Blur detection results for 1D blurred and noisy barcode images.  ... 
doi:10.1155/2014/521027 fatcat:7757ucsydvawpixdwtoxl7fqfm
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