scholarly journals Panoramic image mosaic method based on image segmentation and Improved SIFT algorithm

2021 ◽  
Vol 2113 (1) ◽  
pp. 012066
Author(s):  
Lei Zhuang ◽  
Jiyan Yu ◽  
Yang Song
Keyword(s):  
Image Segmentation ◽  
Feature Point ◽  
Image Feature ◽  
Feature Descriptor ◽  
Image Mosaic ◽  
Panoramic Image ◽  
Feature Points ◽  
Sift Algorithm ◽  
Original Algorithm ◽  
Image Pairs

Abstract Aiming at the problem of large amount of calculation in extracting image feature points in panoramic image mosaic by SIFT algorithm, a panoramic image mosaic algorithm based on image segmentation and Improved SIFT is proposed in this paper. The algorithm fully considers the characteristics of panoramic image stitching. Firstly, the stitched image is divided into blocks, and the maximum overlapping block of image pairs is extracted by using mutual information. The SIFT key points are extracted by SIFT algorithm, and the dog is filtered before the spatial extreme value detection of SIFT algorithm to eliminate the feature points with small intensity value; When establishing the feature descriptor, the 128 dimension of the original algorithm is reduced to 64 dimensions to reduce the amount of calculation. In the feature point registration process, the feature descriptor is reduced to 32 dimensions, the feature point pairs are roughly extracted by the optimal node first BBF algorithm, and the feature point pairs are registered and screened by RANSAC; Finally, the image transformation matrix is obtained to realize panoramic image mosaic. The experimental results show that the proposed algorithm not only ensures the panoramic mosaic effect, but also extracts the feature points in 11% of the time of the traditional SIFT algorithm, and the feature point registration speed is 27.17% of the traditional SIFT algorithm.

A New Method of Panoramic Image Mosaic Based on SIFT

2013 ◽  
Vol 816-817 ◽  
pp. 562-565
Author(s):  
Tong Li ◽  
Zhen Hai Liu ◽  
Zhi Jun Ma
Keyword(s):  
Real Time ◽  
Feature Point ◽  
New Method ◽  
Image Mosaic ◽  
Panoramic Image ◽  
Feature Points ◽  
Sift Algorithm ◽  
Scaling Invariance ◽  
Transformation Parameters

When using the traditional SIFT algorithm extracts feature points, whose characteristics are rotation and scale scaling invariance, the problems are that the feature point extracted by the algorithm is too small and the shortages of real time and robustness. This article proposes a new method called Panoramic Image. This method can define the transformation parameters between the images by using the distances between cities to measure the similarity and using RANSAC with SPRT to diminish the mismatching. The consequence of the experiments demonstrates that this method can shorten the time of registration and prompt the matching feature points.

scholarly journals Image Mosaic Research and Realization Based on LoFTR Algorithm

2021 ◽  
Author(s):  
Aikui Tian ◽  
Kangtao Wang ◽  
liye zhang ◽  
Bingcai Wei
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
The Self ◽  
Image Feature ◽  
Feature Descriptor ◽  
Image Mosaic ◽  
Feature Points ◽  
Matching Method ◽  
Fusion Algorithm ◽  
The Matrix

Abstract Aiming at the problem of inaccurate extraction of feature points by the traditional image matching method, low robustness, and problems such as diffculty in inentifying feature points in area with poor texture. This paper proposes a new local image feature matching method, which replaces the traditional sequential image feature detection, description and matching steps. First, extract the coarse features with a resolution of 1/8 from the original image, then tile to a one-dimensional vector plus the positional encoding, feed them to the self-attention layer and cross-attention layer in the Transformer module, and finally get through the Differentiable Matching Layer and confidence matrix, after setting the threshold and the mutual closest standard, a Coarse-Level matching prediction is obtained. Secondly the fine matching is refined at the Fine-level match, after the Fine-level match is established, the image overlapped area is aligned by transforming the matrix to a unified coordinate, and finally the image is fused by the weighted fusion algorithm to realize the unification of seamless mosaic of images. This paper uses the self-attention layer and cross-attention layer in Transformers to obtain the feature descriptor of the image. Finally, experiments show that in terms of feature point extraction, LoFTR algorithm is more accurate than the traditional SIFT algorithm in both low-texture regions and regions with rich textures. At the same time, the image mosaic effect obtained by this method is more accurate than that of the traditional classic algorithms, the experimental effect is more ideal.

Substation Remote Viewing Image Mosaic Based on Fuzzy Cellular Automata Detection

2013 ◽  
Vol 846-847 ◽  
pp. 1213-1216
Author(s):  
Hong Tao Zai
Keyword(s):  
Cellular Automata ◽  
Feature Point ◽  
Image Mosaic ◽  
Feature Points ◽  
System A ◽  
Safety And Reliability ◽  
Fuzzy Cellular Automata ◽  
Remote Viewing ◽  
Matched Edge ◽  
Edge Feature

In order to improve the effect of remote video monitoring system, a new image mosaic technology based on fuzzy cellular automata detection is presented. Firstly, the edge feature points from two images are extracted by fuzzy cellular automata; secondly, the corresponding feature point pairs are got by the cross-correlation of the gray scale around the edge feature points; finally the images can be stitched by matched edge feature point pairs. The experiment of remote viewing image mosaic in substation shows that this method can achieve image mosaic effectively and it will be benefit to improve the safety and reliability of substation operation.

A Fast Underwater Image Stitching Algorithm with Adaptive Adjustment of Attitude Angle

2021 ◽  
Author(s):  
Lizhou Jiang ◽  
Zhijie Tang ◽  
Zhihang Luo ◽  
Chi Wang
Keyword(s):  
Feature Point ◽  
Reference Image ◽  
Image Stitching ◽  
Panoramic Image ◽  
Feature Points ◽  
Image Capture ◽  
Matching Process ◽  
Underwater Image ◽  
Adaptive Adjustment ◽  
The Impact

In underwater image acquisition process, due to the impact of water currents and other disturbances, the movement posture of the underwater machine will be unstable, which could lead to unusual problems such as twisting of underwater image capture. These factors will increase the error rate of feature point matching and lead to the failure of panoramic image mosaic. In this regard, we propose a new, highly applicable underwater image stitching algorithm. Firstly, the posture angle adjustment link is added to the underwater image processing, and the angle deflection problem of the underwater image is effectively improved by using the posture angle information. Secondly, the feature points of underwater images are extracted based on the accelerated robust feature (SURF) algorithm. Then, the reference image is matched with the feature points of the image to be registered, and effective feature point pairs are obtained by screening. Finally, the images are stitched based on OpenCV to obtain a good panoramic image. Afterexperimental analysis and comparison, our method can increase the number of matching feature point pairs between images. In addition, the Euclidean distance is significantly shortened during the matching process, which further makes the matching of feature points more accurate. Our method satisfactorily overcomes the adverse effects of actual underwater operations and has a better application prospect.

A New Method for Feature Point Matching: Inner and Exterior Product

2011 ◽  
Vol 48-49 ◽  
pp. 79-83
Author(s):  
Xu Guang Wang ◽  
Li Jun Lin ◽  
Hai Yan Cheng
Keyword(s):  
Feature Point ◽  
Feature Descriptor ◽  
Feature Points ◽  
Correlation Measure ◽  
Exterior Product ◽  
Illumination Change ◽  
Point Matching ◽  
Feature Point Matching ◽  
Estimate Precision ◽  
Better Than

In this paper, a novel feature descriptor called gradient correlation descriptor (GCD) is proposed. The GCD descriptor uses the gradient correlation measure defined by the inner and exterior product to characterize the gradient distributions in neighborhoods of feature points, and it has the following advantages: Its construction is very simple because of only the inner and exterior product operations are used; Its distinctive performance is better than the region-based SIFT descriptors since the gradient correlation measure can effectively characterize the gradient distributions in neighborhoods of feature points; In the gradient correlation measure the use of gradient mean makes it is not sensitive to the estimate precision of main orientation of feature point, and thus can provide a better stabilization to image rotation; The gradient correlation measure makes it also has very good adaptability to image affine transform, image blur, JPEG compression as well as illumination change.

Equi-affine differential invariants for invariant feature point detection

2019 ◽  
Vol 31 (2) ◽  
pp. 277-296
Author(s):  
STANLEY L. TUZNIK ◽  
PETER J. OLVER ◽  
ALLEN TANNENBAUM
Keyword(s):  
Feature Point ◽  
Image Feature ◽  
Differential Invariants ◽  
Feature Points ◽  
Affine Invariant ◽  
Moving Frames ◽  
Feature Detectors ◽  
Point Detection ◽  
Point Detector ◽  
Method Of Moving Frames

Image feature points are detected as pixels which locally maximise a detector function, two commonly used examples of which are the (Euclidean) image gradient and the Harris–Stephens corner detector. A major limitation of these feature detectors is that they are only Euclidean-invariant. In this work, we demonstrate the application of a 2D equi-affine-invariant image feature point detector based on differential invariants as derived through the equivariant method of moving frames. The fundamental equi-affine differential invariants for 3D image volumes are also computed.

Image Mosaics Algorithm Combined Feature Point Matching with Transformation Parameters

2013 ◽  
Vol 380-384 ◽  
pp. 4136-4139
Author(s):  
Peng Rui Qiu ◽  
Ying Liang ◽  
Hui Rong
Keyword(s):  
Projective Transformation ◽  
Feature Point ◽  
Image Mosaic ◽  
Feature Points ◽  
Geometric Information ◽  
Point Matching ◽  
Feature Point Matching ◽  
Transformation Parameters ◽  
Relationship Of ◽  
The Relationship

To solve the problem of the large amount of calculation, poor robustness and do not well in image mosaic of images who are in different scales in the traditional image mosaic method ,the article arise a mosaic algorithm of different scales images registration and adaptive. Through feature point matching and automatically recognizing of transform geometric parameters between images,It achieves the match and mosaic of different scale and rotated images. First, using SIFT to extract the feature points of the images and matching feature points according to the principal of mutual information maximum. Then based on the geometric information of the matching pairs, automatically recognize the relationship of transformation parameters. In the end, obtain the projective transformation and achieve the image stable mosaic.

scholarly journals Flexible point cloud matching method based on three-dimensional image feature points

2018 ◽  
Vol 10 (9) ◽  
pp. 168781401879503
Author(s):  
Haihua Cui ◽  
Wenhe Liao ◽  
Xiaosheng Cheng ◽  
Ning Dai ◽  
Changye Guo
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Feature Point ◽  
Image Feature ◽  
Surface Measurement ◽  
Surface Model ◽  
Feature Points ◽  
Matching Method ◽  
Dimensional Image

Flexible and robust point cloud matching is important for three-dimensional surface measurement. This article proposes a new matching method based on three-dimensional image feature points. First, an intrinsic shape signature algorithm is used to detect the key shape feature points, using a weighted three-dimensional occupational histogram of the data points within the angular space, which is a view-independent representation of the three-dimensional shape. Then, the point feature histogram is used to represent the underlying surface model properties at a point whose computation is based on the combination of certain geometrical relations between the point’s nearest k-neighbors. The two-view point clouds are robustly matched using the proposed double neighborhood constraint of minimizing the sum of the Euclidean distances between the local neighbors of the point and feature point. The proposed optimization method is immune to noise, reduces the search range for matching points, and improves the correct feature point matching rate for a weak surface texture. The matching accuracy and stability of the proposed method are verified using experiments. This method can be used for a flat surface with weak features and in other applications. The method has a larger application range than the traditional methods.

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