An Underwater Image Real-Time Registration Approach Based on SURF

2013 ◽  
Vol 437 ◽  
pp. 888-893 ◽  
Author(s):  
Chao Li ◽  
Yong Jie Pang ◽  
Ming Wei Sheng ◽  
Hai Huang
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Median Filter ◽  
Filter Method ◽  
Interest Points ◽  
Registration Method ◽  
Speeded Up Robust Features ◽  
Time Performance ◽  
Underwater Image ◽  
Image Contrast Enhancement

In order to meet the demands of real-time performance and robustness for underwater image registration, a novel image registration method based on the SURF (Speeded-Up Robust Features) algorithm is proposed. During the image acquisition process, noise was generated inevitably because of many influencing factors such as atmospheric turbulence, camera defocus during image capturing or relative motion between the camera and the object. Firstly, median filter method was involved during the image preprocessing for underwater image contrast enhancement. Secondly, the SURF algorithm was used to obtain the interest points of the reference and registering images, and the nearest neighbor method was applied to search for coarse matching points. To obtain the precise matching points, the dominant orientations of the coarse matching points were used to eliminate the mismatching points. Finally, the precise matching points were adapted to calculate the mapping relationship between the registering and reference images, the bilinear interpolation method was applied to resample the registering image, and then the registered image was obtained. Experimental results indicated that the proposed preprocessing methods obviously enhanced the image quality, and the introduced image registration approach effectively improved the real-time performance and guaranteed the robustness at the same time.

A Improved Infrared and Visible Images Matching Based on SURF

2013 ◽  
Vol 325-326 ◽  
pp. 1637-1640
Author(s):  
Dong Mei Li ◽  
Jing Lei Zhang
Keyword(s):  
Image Registration ◽  
Euclidean Distance ◽  
Experimental Results ◽  
Interest Points ◽  
Speeded Up Robust Features ◽  
Visible Images ◽  
Data Points

Images matching is the basis of image registration. For their difference, a improved SURF(speeded up robust features) algorithm was proposed for the infrared and visible images matching. Firstly, edges were extracted from the images to improve the similarity of infrared and visible images. Then SURF algorithm was used to detect interest points, and the dimension of the point descriptor was 64. Finally, found the matching points by Euclidean distance. Experimental results show that some invalid data points were eliminated.

Robust image registration using subspace method in Radon transform domain

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 645-651
Author(s):  
Ning Wei ◽  
Yu He ◽  
Junqing Liu ◽  
Peng Chen
Keyword(s):  
Image Registration ◽  
Radon Transform ◽  
Median Filter ◽  
Least Square ◽  
Phase Correlation ◽  
Transform Domain ◽  
Registration Method ◽  
Content Type ◽  
Structural Differences ◽  
Robust Image

Purpose The purpose of this paper is to represent a robust image registration method to align noisy and deformed images in their Radon transform domain. Due to the limitation of imaging mechanism, the images are often highly noisy. Even worse, the objects in images have structural differences from time to time. Design/methodology/approach To eliminate these degressions, the proposed method is equipped with subspace-based power spectrum analysis algorithm for rotation estimation and a new global median filter least square algorithm for displacement computation. Findings Experiments on strongly noisy and degenerated images show that the proposed method exhibits better accuracy and robustness than phase correlation-based method. In addition, the method can also be applied to multi-modal registration, where the results are comparable to mutual information method but spending much less time. Originality/value A robust image registration method is proposed, which has better performance than traditional methods.

scholarly journals Comparison between Three Registration Methods in the Case of Non-Georeferenced Close Range of Multispectral Images

2021 ◽  
Vol 13 (3) ◽  
pp. 396
Author(s):  
Claudio Ignacio Fernández ◽  
Ata Haddadi ◽  
Brigitte Leblon ◽  
Jinfei Wang ◽  
Keri Wang
Keyword(s):  
Image Registration ◽  
Detection System ◽  
Projective Transformation ◽  
Multispectral Images ◽  
Affine Transformations ◽  
Blue Band ◽  
Greenhouse Production ◽  
Registration Method ◽  
Speeded Up Robust Features ◽  
Close Range

Cucumber powdery mildew, which is caused by Podosphaera xanthii, is a major disease that has a significant economic impact in cucumber greenhouse production. It is necessary to develop a non-invasive fast detection system for that disease. Such a system will use multispectral imagery acquired at a close range with a camera attached to a mobile cart’s mechanic extension. This study evaluated three image registration methods applied to non-georeferenced multispectral images acquired at close range over greenhouse cucumber plants with a MicaSense® RedEdge camera. The detection of matching points was performed using Speeded-Up Robust Features (SURF), and outliers matching points were removed using the M-estimator Sample Consensus (MSAC) algorithm. Three geometric transformations (affine, similarity, and projective) were considered in the registration process. For each transformation, we mapped the matching points of the blue, green, red, and NIR band images into the red-edge band space and computed the root mean square error (RMSE in pixel) to estimate the accuracy of each image registration. Our results achieved an RMSE of less than 1 pixel with the similarity and affine transformations and of less than 2 pixels with the projective transformation, whatever the band image. We determined that the best image registration method corresponded to the affine transformation because the RMSE is less than 1 pixel and the RMSEs have a Gaussian distribution for all of the bands, but the blue band.

scholarly journals Photoacoustic-MR Images Registration Based on Co-Sparse Analysis Model to Compensate for Brain Shift

2021 ◽  
Author(s):  
Parastoo Farnia ◽  
Bahador Makkiabadi ◽  
Meysam Alimohammadi ◽  
Ebrahim Najafzadeh ◽  
Maryam Basij ◽  
...  
Keyword(s):  
Image Registration ◽  
Real Time ◽  
Mouse Brain ◽  
Brain Shift ◽  
Challenging Problem ◽  
Analysis Model ◽  
Mr Images ◽  
Registration Method ◽  
Phantom Data ◽  
Brain Deformation

Brain shift is an important obstacle for the application of image guidance during neurosurgical interventions. There has been a growing interest in intra-operative imaging systems to update the image-guided surgery systems with real-time data. However, due to the innate limitations of the current imaging modalities, accurate and real-time brain shift compensation remains as a challenging problem. In this study, application of the intra-operative photoacoustic (PA) imaging and registration of the intra-operative PA images with pre-operative brain MR images is proposed to compensate brain deformation during surgery. Finding a satisfactory multimodal image registration method is a challenging problem due to complicated and unpredictable nature of brain deformation. In this study, the co-sparse analysis model is proposed for PA-MR image registration which can capture the interdependency of two modalities. The proposed algorithm works based on the minimization of mapping transform by using a pair of analysis operators. These operators are learned by the alternating direction method of multipliers. The method was evaluated using experimental phantom and ex-vivo data obtained from mouse brain. The results of phantom data show about 60% and 63% improvement in root mean square error (RMSE) and target registration error (TRE) in comparison with commonly used normalized mutual information registration method. In addition, the results of mouse brain and phantom data shown more accurate performance for PA versus ultrasound imaging for brain shift calculation. Finally, by using the proposed registration method, the intra-operative PA images could become a promising tool when the brain shift invalidated pre-operative MRI.

Dimensionality Reduction Based on Feature Points of Underwater Image Mosaic Algorithm

2013 ◽  
Vol 462-463 ◽  
pp. 308-311
Author(s):  
Hong Hai Zhuang ◽  
Guo Gao Liu ◽  
Xue Wu Zhang ◽  
Zhuo Zhang ◽  
Min Li ◽  
...  
Keyword(s):  
Real Time ◽  
Feature Vector ◽  
Principal Component ◽  
Image Mosaic ◽  
Feature Points ◽  
The Real ◽  
Time Performance ◽  
Feature Descriptors ◽  
Underwater Image ◽  
Wide Perspective

Image mosaic is a wide perspective to create high resolution for image processing, computer graphics and new field of interdisciplinary research. According to the fact that the real-time performance of Harris feature mosaic algorithm is poor, this paper proposes an improved Harris algorithm based on feature point mosaic of principal component analysis to reduce the dimensionality of the feature points. The algorithm constructs feature descriptors with the feature points, and then uses PCA to reduce the dimension of feature vector descriptor to improve the real-time of the algorithm. Experimental results show that the algorithm can realize the underwater image mosaic and improve the real-time performance of the algorithm.

scholarly journals An Improved 3D Registration Method of Mobile Augmented Reality for Urban Built Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yong Wu ◽  
Weitao Che ◽  
Bihui Huang
Keyword(s):  
Augmented Reality ◽  
Built Environment ◽  
Real Time ◽  
Smart Phone ◽  
Experimental Result ◽  
3D Registration ◽  
Registration Method ◽  
Time Performance ◽  
Building Recognition ◽  
Mobile Ar

3D registration plays a pivotal role in augmented reality (AR) system. The existing methods are not suitable to be applied directly in the mobile AR system for the built environment, with the reasons of poor real-time performance and robustness. This paper proposes an improved 3D registration method of mobile AR for built environment, which is based on SURFREAK and KLT. This method increases the building efficiency of algorithm descriptors and maintains the robustness of the algorithms. To implement and evaluate the registration method, a smart phone-based mobile AR system for built environment is developed. The experimental result shows that the improved method is endowed with higher real-time performance and robustness, and the mobile AR 3D registration can realize a favorable performance and efficiency in the complex built environment. The mobile AR system could be used in building recognition and information augmentation for built environment and further to facilitate location-based games, urban heritage tourism, urban planning, and smart city.

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