image mosaic
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2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yajun Pang

Panorama can reflect the image seen at any angle of view at a certain point of view. How to improve the quality of panorama stitching and use it as a data foundation in the “smart tourism” system has become a research hotspot in recent years. Image stitching means to use the overlapping area between the images to be stitched for registration and fusion to generate a new image with a wider viewing angle. This article takes the production of “Tai Chi” animation as an example to apply image stitching technology to the production of realistic 3D model textures to simplify the production of animation textures. A handheld camera is used to collect images in a certain overlapping area. After cylindrical projection, the Harris algorithm based on scale space is adopted to detect image feature points, the two-way normalized cross-correlation algorithm matches the feature points, and the algorithm to extract the threshold T iteratively removes mismatches. The transformation parameter model is quickly estimated through the improved RANSAC algorithm, and the spliced image is projected and transformed. The Szeliski grayscale fusion method directly calculates the grayscale average of the matching points to fuse the image, and finally, the best stitching method is used to eliminate the ghosting at the image mosaic. Data experiments based on Matlab show that the proposed image splicing technology has the advantages of high efficiency and clear spliced images and a more satisfactory panoramic image visual effect can be achieved.


2022 ◽  
Vol 71 ◽  
pp. 103261
Author(s):  
Ziyan Zhang ◽  
Lixiao Wang ◽  
Wenfeng Zheng ◽  
Lirong Yin ◽  
Rongrong Hu ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Aikui Tian ◽  
Kangtao Wang ◽  
liye zhang ◽  
Bingcai Wei

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.


2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shuang Wang

Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be spliced is a feature, and the genetic algorithm is used to solve the optimal parameters to achieve the maximum similarity. By smoothing the image, a clear image with minimum noise is obtained. Experimental results show that the proposed algorithm can effectively realize the image mosaic of the fundus. The method proposed in this paper can provide support for high-precision automatic stitching of multiple single-mode color fundus images.


2021 ◽  
pp. 111-120
Author(s):  
Xueya Liu ◽  
Shaoshi Wu ◽  
Dan Wang
Keyword(s):  

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiqi Zhu ◽  
Jian Jiang ◽  
Jian Wang ◽  
Yue Tang ◽  
Xiaoming Ge

The application value of image mosaic algorithm (IMA) based CT imaging technology in the analysis of pathological characteristics of gastric polyp (GP) patients was explored in this work. 588 cases of GP patients in the hospital were selected as the research objects, and CT images based on IMA were adopted for examination. The patient’s basic information, image performance, and gastroscopy results were recorded. The results showed that the absolute mean bright error (AMBE) index and information entropy of the IMA are 0.0625 and 7.0385, respectively. The clinical symptoms of patients were mostly abdominal pain (21.4%), abdominal distension (15.6%), and sour regurgitation (17.8%). The common size of GP was no more than 0.5 cm, and the common type was Yamada type II. There were notable differences between single and multiple GPs of different pathological types ( P < 0.05 ). Proliferative polyps were mostly found in the stomach and antrum, while fundus gland polyps were mostly in the stomach and fundus. There was significant difference between the growth location of the hyperplastic polyp and basal gland polyp ( P < 0.05 ). In summary, the CT images of IMA proposed in this paper can not only realize image splicing effectively but also were superior to the traditional SIFT method in the quality of splicing image and were conducive to the analysis of the pathological characteristics of GP patients, which had significant clinical promotion value.


2021 ◽  
Vol 17 (11) ◽  
pp. 688-692
Author(s):  
Zhe Zhang ◽  
Wanli Xue
Keyword(s):  

2021 ◽  
Vol 2113 (1) ◽  
pp. 012066
Author(s):  
Lei Zhuang ◽  
Jiyan Yu ◽  
Yang Song

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.


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