scholarly journals Underwater Image Mosaic Algorithm Based on Improved Image Registration

2021 ◽  
Vol 11 (13) ◽  
pp. 5986
Author(s):  
Yinsen Zhao ◽  
Farong Gao ◽  
Jun Yu ◽  
Xing Yu ◽  
Zhangyi Yang
Keyword(s):  
Image Registration ◽  
Image Fusion ◽  
Image Feature ◽  
Practical Significance ◽  
Image Mosaic ◽  
Registration Accuracy ◽  
Scale Invariant ◽  
Low Contrast ◽  
Underwater Image ◽  
Panoramic Images

In order to obtain panoramic images in a low contrast underwater environment, an underwater panoramic image mosaic algorithm based on image enhancement and improved image registration (IIR) was proposed. Firstly, mixed filtering and sigma filtering are used to enhance the contrast of the original image and de-noise the image. Secondly, scale-invariant feature transform (SIFT) is used to detect image feature points. Then, the proposed IIR algorithm is applied to image registration to improve the matching accuracy and reduce the matching time. Finally, the weighted smoothing method is used for image fusion to avoid image seams. The results show that IIR algorithm can effectively improve the registration accuracy, shorten the registration time, and improve the image fusion effect. In the field of cruise research, instruments equipped with imaging systems, such as television capture and deep-drag camera systems, can produce a large number of image or video recordings. This algorithm provides support for fast and accurate underwater image mosaic and has important practical significance.

Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs

2020 ◽  
Vol 86 (3) ◽  
pp. 177-186
Author(s):  
Matthew Plummer ◽  
Douglas Stow ◽  
Emanuel Storey ◽  
Lloyd Coulter ◽  
Nicholas Zamora ◽  
...  
Keyword(s):  
Image Registration ◽  
Image Data ◽  
Aerial Image ◽  
Registration Accuracy ◽  
Illumination Variation ◽  
Scale Invariant ◽  
Match Point ◽  
Linear Correction ◽  
Multi Temporal ◽  
Image Pairs

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (<small>SIFT</small>) and Random Sample Consensus (<small>RANSAC</small>) Alignment (<small>SARA</small>). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

The Application of SIFT Method towards Image Registration

2014 ◽  
Vol 1044-1045 ◽  
pp. 1392-1396 ◽  
Author(s):  
Shu Guang Wu ◽  
Shu He ◽  
Xia Yang
Keyword(s):  
Image Registration ◽  
Memory Consumption ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Large Memory ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Computation Speed

The scale invariant features transform (SIFT) is commonly used in object recognition,According to the problems of large memory consumption and low computation speed in SIFT (Scale Invariant Feature Transform) algorithm.During the image registration methods based on point features,SIFT point feature is invariant to image scale and rotation, and provides robust matching across a substantial range of affine distortion. Experiments show that on the premise that registration accuracy is stable, the proposed algorithm solves the problem of high requirement of memory and the efficiency is improved greatly, which is applicable for registering remote sensing images of large areas.

Data Fusion of Radar Image and ECDIS Based on Matching of Harris Feature Points

2011 ◽  
Vol 317-319 ◽  
pp. 2026-2029 ◽  
Author(s):  
Chuang Zhang ◽  
Zhong Zhou Fan
Keyword(s):  
Image Registration ◽  
Image Fusion ◽  
Wavelet Transformation ◽  
Transformation Method ◽  
Image Features ◽  
Image Feature ◽  
Feature Points ◽  
Registration Algorithm ◽  
Image Registration Algorithm ◽  
Electronic Chart

This paper analyzes automatic image registration algorithm, and image features of radar and electronic chart, then, image registration algorithm based on Harris feature point are given, in which the methods are specially described. Wavelet transformation method on image feature should be considered. Moreover, simulate results of image fusion.

scholarly journals Satellite-Borne Optical Remote Sensing Image Registration Based on Point Features

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2695
Author(s):  
Xinan Hou ◽  
Quanxue Gao ◽  
Rong Wang ◽  
Xin Luo
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Nearest Neighbor ◽  
Optical Remote Sensing ◽  
Image Splicing ◽  
Registration Accuracy ◽  
Scale Invariant ◽  
Subsequent Work ◽  
Harris Corner ◽  
Matching Strategy

Since technologies in image fusion, image splicing, and target recognition have developed rapidly, as the basis of many image applications, the performance of image registration directly affects subsequent work. In this work, for rich features of satellite-borne optical imagery such as panchromatic and multispectral images, the Harris corner algorithm is combined with the scale invariant feature transform (SIFT) operator for feature point extraction. Our rough matching strategy uses the K-D (K-Dimensional) tree combined with the BBF (Best Bin First) method, and the similarity measure is the nearest neighbor/the second-nearest neighbor ratio. Finally, a triangle-area representation (TAR) algorithm is utilized to eliminate false matches in order to ensure registration accuracy. The performance of the proposed algorithm is compared with existing popular algorithms. The experimental results indicate that for visible light and multi-spectral satellite remote sensing images of different sizes and different sources, the proposed algorithm in this work is excellent in accuracy and efficiency.

scholarly journals SIFT Image Feature Extraction for an Efficient Image Registration

2020 ◽  
Vol 8 (6) ◽  
pp. 4090-4094
Keyword(s):  
Image Registration ◽  
Image Feature ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Image Registration Algorithm ◽  
Key Points ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
Ransac Algorithm ◽  
Unit Vectors

This paper presents an image registration algorithm based on SIFT (Scale Invariant Feature Transform).The obtained descriptors and key points by the SIFT confirms that, the algorithm is very robust to scaling, noise, translation and rotation. At the beginning, the key points are extracted from the image. Later to Match the obtained points, dot products between the unit vectors are calculated. Finally, transformation matrix is obtained by applying RANSAC algorithm. Experimental results shows that the algorithm extracts the better key points, which can be used for used for image registration applications.

scholarly journals Automated Mosaicking of Medical x-ray Images using SIFT Algorithm

2021 ◽  
Vol 23 (05) ◽  
pp. 686-693
Author(s):  
Sanjeevakumar Harihar ◽  
◽  
Dr. Manjunath R ◽  
Keyword(s):  
Image Registration ◽  
Feature Detection ◽  
Similarity Measures ◽  
Body Part ◽  
The Body ◽  
Scale Invariant ◽  
X Ray ◽  
Detection Techniques ◽  
Single Frame ◽  
Panoramic Images

Image registration is a process of joining any number of images that have similar overlapping regions of the same scene in order to make a panoramic image. In the field of medical, multimedia, and image processing applications image registration process stands challenging. The work presented here on medical images can be applicable for long limb operations and scoliosis operations. Traditional x-ray machines produce a single frame of x-ray image containing a portion of the body part, but they can not generate a large view of body x-ray image in a single frame. This problem can be solved by creating panoramic images by combining multiple images. The work proposed in this paper can automatically produce panoramic x-ray images by stitching multiple x-ray images. The proposed work uses scale-invariant feature transform (SIFT) for mosaicking x-ray images as a local feature point extractor which uses the difference of Gaussian (DOG) and invariant to orientation and scale. Based on the location relationship of x-ray images, random sample consensus (RANSAC) is incorporated to remove the effect of mismatched point pairs in x-ray images and to generate the panoramic view. The performance of the system is computed by using structural and time constraint parameters and is compared with different feature detection techniques. The experimental results show that combing SIFT and RANSAC yields less processing time with an increase in similarity measures.

scholarly journals Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

2021 ◽  
Vol 9 (2) ◽  
pp. 225
Author(s):  
Farong Gao ◽  
Kai Wang ◽  
Zhangyi Yang ◽  
Yejian Wang ◽  
Qizhong Zhang
Keyword(s):  
Image Enhancement ◽  
Local Contrast ◽  
Fusion Method ◽  
Formation Model ◽  
Low Contrast ◽  
White Balance ◽  
Multi Scale ◽  
Color Distortion ◽  
Underwater Image ◽  
Enhancement Method

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations
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