scholarly journals Research on Multidimensional Image Intelligent Matching Algorithm of Artificial Intelligence Image Recognition Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiao Liao ◽  
WeiJia Wang ◽  
Wei Wang ◽  
Chong Liang
Keyword(s):  
Original Data ◽  
Manhattan Distance ◽  
Reference Image ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Local Linear Embedding ◽  
Matching Process ◽  
Nonlinear Dimension Reduction ◽  
Nonlinear Dimension ◽  
Scale Invariant Feature

Image matching is a method of matching by analyzing the gray scale and texture information of the reference image and the image to be matched. Firstly, the scale invariant feature transform (SIFT) algorithm has long descriptor time and poor real time, a nonlinear dimension reduction method (LLE) based on local linear embedding is proposed to preserve the nonlinear information in the original data space as much as possible, shorten the running time of the algorithm, and improve the matching accuracy. Second, aiming at the problem that the Euclidean distance takes a large amount of calculation in the matching process, Manhattan distance is proposed to calculate the similarity between the reference image and the image to be matched, so as to further reduce the algorithm time. Through the improved LLE-SIFT algorithm, experimental results show that the algorithm has a high matching rate and improves the matching speed.

An Image Stitching Algorithm Based on Histogram Matching and SIFT Algorithm

2017 ◽  
Vol 31 (04) ◽  
pp. 1754006 ◽  
Author(s):  
Jing Zhang ◽  
Guangxue Chen ◽  
Zhaoyang Jia
Keyword(s):  
Weighted Average ◽  
Image Stitching ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Process ◽  
Histogram Matching ◽  
Key Points ◽  
Illumination Changes ◽  
Scale Invariant Feature ◽  
Ransac Algorithm

Image stitching among images that have significant illumination changes will lead to unnatural mosaic image. An image stitching algorithm based on histogram matching and scale-invariant feature transform (SIFT) algorithm is brought out to solve the problem in this paper. First, histogram matching is used for image adjustment, so that the images to be stitched are at the same level of illumination, then the paper adopts SIFT algorithm to extract the key points of the images and performs the rough matching process, followed by RANSAC algorithm for fine matches, and finally calculates the appropriate mathematical mapping model between two images and according to the mapping relationship, a simple weighted average algorithm is used for image blending. The experimental results show that the algorithm is effective.

Medical Image Feature Matching Based on Wavelet Transform and SIFT Algorithm

2011 ◽  
Vol 65 ◽  
pp. 497-502
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Wavelet Transform ◽  
Wavelet Transforms ◽  
Medical Image ◽  
Feature Matching ◽  
Image Feature ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Matching Algorithm ◽  
Invariant Feature ◽  
Scale Invariant Feature

A feature matching algorithm based on wavelet transform and SIFT is proposed in this paper, Firstly, Biorthogonal wavelet transforms algorithm is used for medical image to delaminating, and restoration the processed image. Then the SIFT (Scale Invariant Feature Transform) applied in this paper to abstracting key point. Experimental results show that our algorithm compares favorably in high-compressive ratio, the rapid matching speed and low storage of the image, especially for the tilt and rotation conditions.

scholarly journals Research on Remote Sensing Image Matching with Special Texture Background

Symmetry ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1380
Author(s):  
Sen Wang ◽  
Xiaoming Sun ◽  
Pengfei Liu ◽  
Kaige Xu ◽  
Weifeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Geometric Distortion ◽  
Reference Image ◽  
Remote Sensing Images ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Speeded Up Robust Features ◽  
Adaptive Quantization ◽  
Texture Background

The purpose of image registration is to find the symmetry between the reference image and the image to be registered. In order to improve the registration effect of unmanned aerial vehicle (UAV) remote sensing imagery with a special texture background, this paper proposes an improved scale-invariant feature transform (SIFT) algorithm by combining image color and exposure information based on adaptive quantization strategy (AQCE-SIFT). By using the color and exposure information of the image, this method can enhance the contrast between the textures of the image with a special texture background, which allows easier feature extraction. The algorithm descriptor was constructed through an adaptive quantization strategy, so that remote sensing images with large geometric distortion or affine changes have a higher correct matching rate during registration. The experimental results showed that the AQCE-SIFT algorithm proposed in this paper was more reasonable in the distribution of the extracted feature points compared with the traditional SIFT algorithm. In the case of 0 degree, 30 degree, and 60 degree image geometric distortion, when the remote sensing image had a texture scarcity region, the number of matching points increased by 21.3%, 45.5%, and 28.6%, respectively and the correct matching rate increased by 0%, 6.0%, and 52.4%, respectively. When the remote sensing image had a large number of similar repetitive regions of texture, the number of matching points increased by 30.4%, 30.9%, and −11.1%, respectively and the correct matching rate increased by 1.2%, 0.8%, and 20.8% respectively. When processing remote sensing images with special texture backgrounds, the AQCE-SIFT algorithm also has more advantages than the existing common algorithms such as color SIFT (CSIFT), gradient location and orientation histogram (GLOH), and speeded-up robust features (SURF) in searching for the symmetry of features between images.

An Improved Face Recognition Algorithm Based on SIFT and LBP

2013 ◽  
Vol 427-429 ◽  
pp. 1999-2004 ◽  
Author(s):  
Huai Ming Yang ◽  
Jin Guang Sun
Keyword(s):  
Recognition Rate ◽  
Recognition Algorithm ◽  
Image Feature ◽  
Local Similarity ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Face Images ◽  
Calculation Results ◽  
Scale Invariant Feature ◽  
Global And Local

A new face image feature extraction and recognition algorithm based on Scale Invariant Feature Transform (SIFT) and Local Linary Patterns (LBP) is proposed in this paper. Firstly, a set of keypoints are extracted from images by using the SIFT algorithm; Secondly, each keypoint is described by LBP patterns; Finally, a combination of the global and local similarity is adopted to calculate the matching results for face images. Calculation results show that the algorithm can reduce the matching dimension of feature points, improve the recognition rate and perspective; it has nice robustness against the interferences such as rotation, lighting and expression.

scholarly journals A Registration Scheme for Multispectral Systems Using Phase Correlation and Scale Invariant Feature Matching

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hanlun Li ◽  
Aiwu Zhang ◽  
Shaoxing Hu
Keyword(s):  
Phase Correlation ◽  
Feature Point ◽  
Reference Image ◽  
Feature Points ◽  
Scale Invariant ◽  
Matching Problem ◽  
Invariant Feature ◽  
Significant Difference ◽  
Scale Invariant Feature ◽  
Band Image

In the past few years, many multispectral systems which consist of several identical monochrome cameras equipped with different bandpass filters have been developed. However, due to the significant difference in the intensity between different band images, image registration becomes very difficult. Considering the common structural characteristic of the multispectral systems, this paper proposes an effective method for registering different band images. First we use the phase correlation method to calculate the parameters of a coarse-offset relationship between different band images. Then we use the scale invariant feature transform (SIFT) to detect the feature points. For every feature point in a reference image, we can use the coarse-offset parameters to predict the location of its matching point. We only need to compare the feature point in the reference image with the several near feature points from the predicted location instead of the feature points all over the input image. Our experiments show that this method does not only avoid false matches and increase correct matches, but also solve the matching problem between an infrared band image and a visible band image in cases lacking man-made objects.

Comparison of Contour Based and Feature Based Tracking Methods for Control of Microbiorobots

2009 ◽  
Author(s):  
Dal Hyung Kim ◽  
Edward Steager ◽  
Min Jun Kim
Keyword(s):  
Small Error ◽  
Small Scale ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Tracking Method ◽  
Miniature Robots ◽  
A Cell ◽  
Feature Based ◽  
Scale Invariant Feature ◽  
Exact Positions

Miniature robots should be precisely controlled because of a small workspace and size of their shapes. Small error of control could lead to failure of tasks such as an assembly. Tracking is one of the most important techniques because control of a small scale robot is hard to accomplish without object’s motion information. In this paper, we compare the feature based and the region based tracking methods with microbiorobot. Invariant features can be extracted using Scale Invariant Feature Transfrom (SIFT) algorithm because microbiorobot is a rigid body unlike a cell. We clearly showed that the feature based tracking method track exact positions of the objects than region based tracking method when objects are close contacted or overlapped. Also, the feature based tracking method allows tracking of objects even though partial object disappears or illumination is changed.

Recent Advances in Corneal Imaging

2011 ◽  
pp. 271-287
Author(s):  
A. Elbita ◽  
R. Qahwaji ◽  
S. Ipson ◽  
T. Y. Ahmed ◽  
K. Ramaesh ◽  
...  
Keyword(s):  
3D Model ◽  
Visual Quality ◽  
Scale Invariant ◽  
Image Capture ◽  
Diagnostic Aid ◽  
Sift Algorithm ◽  
Scale Invariant Feature ◽  
Future Work ◽  
Corneal Imaging

This chapter details work with sequences of corneal images from a confocal microscope to develop enhancement methods to improve the visual quality of the images. Due to involuntary movements of the subject’s eye during image capture, the images suffer both lateral and longitudinal translations, and work is ongoing to attempt to register adjacent images in the sequence. Currently this registration uses an approach based on the Scale Invariant Feature Transforms (SIFT) algorithm. Registration is a necessary stage in the construction of a 3D model of the subject’s cornea for use as a diagnostic aid. The algorithms, results, progress and suggestions for future work are presented in this chapter.

scholarly journals Robust Image Matching Algorithm Using SIFT on Multiple Layered Strategies

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Yong Chen ◽  
Lei Shang ◽  
Eric Hu
Keyword(s):  
Image Matching ◽  
Geometric Feature ◽  
Data Sets ◽  
Feature Points ◽  
Matching Method ◽  
Scale Invariant ◽  
Data Set ◽  
Sift Algorithm ◽  
Delta Rule ◽  
Scale Invariant Feature

As for the unsatisfactory accuracy caused by SIFT (scale-invariant feature transform) in complicated image matching, a novel matching method on multiple layered strategies is proposed in this paper. Firstly, the coarse data sets are filtered by Euclidean distance. Next, geometric feature consistency constraint is adopted to refine the corresponding feature points, discarding the points with uncoordinated slope values. Thirdly, scale and orientation clustering constraint method is proposed to precisely choose the matching points. The scale and orientation differences are employed as the elements ofk-means clustering in the method. Thus, two sets of feature points and the refined data set are obtained. Finally, 3 * delta rule of the refined data set is used to search all the remaining points. Our multiple layered strategies make full use of feature constraint rules to improve the matching accuracy of SIFT algorithm. The proposed matching method is compared to the traditional SIFT descriptor in various tests. The experimental results show that the proposed method outperforms the traditional SIFT algorithm with respect to correction ratio and repeatability.

scholarly journals MRI Images under the Optimized Registration Algorithm for Primary Open Angle Glaucoma Visual Path Damage

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Bin Zhou ◽  
Min Chen
Keyword(s):  
Primary Open Angle Glaucoma ◽  
Open Angle Glaucoma ◽  
Scale Invariant Feature Transform ◽  
Open Angle ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature ◽  
The Impact

To explore the impact of different image registration algorithms on the diagnosis of visual path damage in patients with primary open angle glaucoma (POAG), 60 cases of suspected POAG patients were selected as the research objects. Shape-preserving scale invariant feature transform (SP-SIFT) algorithm, scale invariant feature transform (SIFT) algorithm, and Kanade-Lucas-Tomasi (KLT) algorithm were compared and applied to MRI images of 60 POAG patients. It was found that the SP-SIFT algorithm converged after 33 iterations, which had a higher registration speed than the SIFT algorithm and the KLT algorithm. The mean errors of the SP-SIFT algorithm in the rotation angle, X-direction translation, and Y-direction translation were 2.11%, 4.56%, and 4.31%, respectively. Those of the SIFT algorithm were 5.55%, 9.98%, and 7.01%, respectively. Those of the KLT algorithm were 7.45%, 11.31%, and 8.56%, respectively, and the difference among algorithms was significant ( P < 0.05 ). The diagnostic sensitivity and accuracy of the SP-SIFT algorithm for POAG were 96.15% and 94.34%, respectively. Those of the SIFT algorithm were 94.68% and 90.74%, respectively. Those of the KLT algorithm were 94.21% and 90.57%, respectively, and the three algorithms had significant differences ( P < 0.05 ). The results of MRI images based on the SP-SIFT algorithm showed that the average thickness of the cortex of the patient’s left talar sulcus, right talar sulcus, left middle temporal gyrus, and left fusiform gyrus were 2.49 ± 0.15 mm, 2.62 ± 0.13 mm, 3.00 ± 0.10 mm, and 2.99 ± 0.17 mm, respectively. Those of the SIFT algorithm were 2.51 ± 0.17 mm, 2.69 ± 0.12 mm, 3.11 ± 0.13 mm, and 3.09 ± 0.14 mm, respectively. Those of the KLT algorithm were 2.35 ± 0.12 mm, 2.52 ± 0.16 mm, 2.77 ± 0.11 mm, and 2.87 ± 0.17 mm, respectively, and the three algorithms had significant differences ( P < 0.05 ). In summary, the SP-SIFT algorithm was ideal for POAG visual pathway diagnosis and was of great adoption potential in clinical diagnosis.

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