scholarly journals Application of Image Mosaic Technology in Tai Chi Animation Creation

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Yajun Pang
Keyword(s):  
Tai Chi ◽  
High Efficiency ◽  
Scale Space ◽  
Point Of View ◽  
Image Feature ◽  
Image Stitching ◽  
Image Mosaic ◽  
Feature Points ◽  
Image Splicing ◽  
Cylindrical Projection

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.

The Feature Extraction and Matching Algorithm Based on the Fire Video Image Orientation

2013 ◽  
Vol 380-384 ◽  
pp. 3986-3989
Author(s):  
Ying Lu ◽  
Hui Qin Wang ◽  
Fei Xu ◽  
Wei Guang Liu
Keyword(s):  
Scale Space ◽  
Video Image ◽  
Feature Point ◽  
Image Feature ◽  
Feature Points ◽  
Scale Invariant ◽  
Match Rate ◽  
Harris Corner ◽  
Computationally Intensive ◽  
Fire Flame

Because the SIFT (scale invariant feature transform) algorithm can not accurately locate the flame shape features and computationally intensive, this article proposed a stereo video image fire flame matching method which is a combination of Harris corner and SIFT algorithm. Firstly, the algorithm extracts image feature points using Harris operator in Gaussian scale space and defines the main directions for each feature point, and then calculates the 32-dimensional feature vectors of each feature point descriptor and the Euclidean distance to match two images. Experimental results of image matching demonstrate that the new algorithm improves the significance of the shape of the extracted feature points and keep a better match rate of 96%. At the same time the time complexity is reduced by 27.8%. This algorithm has a certain practicality.

scholarly journals Semantic segmentation guided feature point classification and seam fusion for image stitching

2021 ◽  
Vol 15 ◽  
pp. 174830262110653
Author(s):  
Huafeng Huang ◽  
Fei Chen ◽  
Hang Cheng ◽  
Liyao Li ◽  
Meiqing Wang
Keyword(s):  
Semantic Segmentation ◽  
Classification Problem ◽  
Feature Point ◽  
Image Feature ◽  
Image Stitching ◽  
Feature Points ◽  
Target Image ◽  
Distance Transformation ◽  
Cutting Algorithm

Image stitching can be employed to stitch images taken from different times, perspectives, or devices into a panorama with a wider view. However, the imaging specification of images to be stitched is strict. If the imaging specification is not satisfied, artefacts caused by inaccurate alignment and unnatural distortion will occur. Semantic segmentation can solve the classification problem at the pixel level; however, image stitching significantly depends on the accuracy of feature points. Therefore, this paper proposes an image stitching algorithm based on semantic segmentation to guide feature point classification and seam fusion. First, the images are recognized by a cascade semantic segmentation network, and the image feature points are classified. Thereafter, the corresponding homography transformations are calculated using different class feature points, and the best homography mapping for the entire target image is selected. Finally, a seam-cutting algorithm based on semantic segmentation is used to compute the seam, and a feathering Poisson fusion with distance transformation is used to eliminate artefacts and light differences. Experiments show that the algorithm can generate transitional natural and perceptual stitching results even under the influence of perspective and light differences.

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.

Image Feature Matching Based on SIFT Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 4157-4161
Author(s):  
Xin Zhang ◽  
Ya Sheng Zhang ◽  
Hong Yao
Keyword(s):  
Image Matching ◽  
Feature Matching ◽  
Quadratic Function ◽  
Scale Space ◽  
Feature Point ◽  
Image Feature ◽  
Three Dimension ◽  
Feature Points ◽  
Gradient Distribution ◽  
Sift Feature

In the process of image matching, it is involved such as image rotation, scale zooming, brightness change and other problems. In order to improve the precision of image matching, image matching algorithm based on SIFT feature point is proposed. First, the method of generating scale space is introduced. Then, the scale and position of feature points are determined through three dimension quadratic function and feature vectors are determined through gradient distribution characteristic of neighborhood pixels of feature points. Finally, feature matching is completed based on the Euclidean distance. The experiment result indicates that using SIFT feature point can achieve image matching effectively.

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.

scholarly journals Multifunctional Cantilevers as Working Elements in Solid-State Cooling Devices

Actuators ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 58
Author(s):  
Andraž Bradeško ◽  
Lovro Fulanović ◽  
Marko Vrabelj ◽  
Aleksander Matavž ◽  
Mojca Otoničar ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Ethylene Glycol ◽  
Contact Resistance ◽  
High Efficiency ◽  
Thermal Contact ◽  
Point Of View ◽  
Practical Implementation ◽  
Cooling Devices ◽  
Cascade Structure ◽  
Lead Magnesium

Despite the challenges of practical implementation, electrocaloric (EC) cooling remains a promising technology because of its good scalability and high efficiency. Here, we investigate the feasibility of an EC cooling device that couples the EC and electromechanical (EM) responses of a highly functionally, efficient, lead magnesium niobate ceramic material. We fabricated multifunctional cantilevers from this material and characterized their electrical, EM and EC properties. Two active cantilevers were stacked in a cascade structure, forming a proof-of-concept device, which was then analyzed in detail. The cooling effect was lower than the EC effect of the material itself, mainly due to the poor solid-to-solid heat transfer. However, we show that the use of ethylene glycol in the thermal contact area can significantly reduce the contact resistance, thereby improving the heat transfer. Although this solution is most likely impractical from the design point of view, the results clearly show that in this and similar cooling devices, a non-destructive, surface-modification method, with the same effectiveness as that of ethylene glycol, will have to be developed to reduce the thermal contact resistance. We hope this study will motivate the further development of multifunctional cooling devices.

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

scholarly journals Performance Evaluation of Gradient Domain and Pyramid Blending Used in Image Stitching Process with ORB Binary Descriptor

2019 ◽  
Vol 9 (1) ◽  
pp. 1849-1854
Keyword(s):  
Feature Detection ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Feature ◽  
Image Stitching ◽  
Imaging Data ◽  
Multiple Images ◽  
Binary Descriptor ◽  
Blending Method

Panorama development is the basically method of integrating multiple images captured of the same scene under consideration to get high resolution image. This process is useful for combining multiple images which are overlapped to obtain larger image. Usefulness of Image stitching is found in the field related to medical imaging, data from satellites, computer vision and automatic target recognition in military applications. The goal objective of this research paper is basically for developing an high improved resolution and its quality panorama having with high accuracy and minimum computation time. Initially we compared different image feature detectors and tested SIFT, SURF, ORB to find out the rate of detection of the corrected available key points along with processing time. Later on, testing is done with some common techniques of image blending or fusion for improving the mosaicing quality process. In this experimental results, it has been found out that ORB image feature detection and description algorithm is more accurate, fastest which gives a higher performance and Pyramid blending method gives the better stitching quality. Lastly panorama is developed based on combination of ORB binary descriptor method for finding out image features and pyramid blending method.

scholarly journals Low-Temperature Catalytic Pyrolysis of Cellulose in Acidic [Bmim]Otf: A Mechanism Model of Product Evolution

2021 ◽  
Author(s):  
Zhishuncheng Li ◽  
GuangFei Qu ◽  
Yanhua He ◽  
Ping Ning ◽  
Ruosong Xie ◽  
...  
Keyword(s):  
Low Temperature ◽  
Catalytic Pyrolysis ◽  
High Efficiency ◽  
Point Of View ◽  
The Novel ◽  
Catalytic Systems ◽  
Mechanism Model ◽  
Pyrolysis Behavior ◽  
Product Evolution ◽  
The Media

Abstract In this paper, we studied the catalytic pyrolysis behavior of microcrystalline cellulose (MC) in catalytic systems with acidic [Bmim]OTf as the media at temperatures of 140°C, 180°C, 220°C, 260°C, and 300°C. The pyrolysis behavior was investigated via SEM, XRD, FTIR, and GC-MS. During the catalysis of [Bmim]OTf, the pyrolysis temperature of MC was reduced to 140°C significantly and the crystalline structure of MC was destroyed rapidly. The novel synergistic catalytic effect of CF3SO3- and [Bmim]+ was discovered, which may lead to MC-selective cleavage of glycosidic, C-C, C-O, and C-H bonds, accompanied by new bond formation, which showed the production of many small molecular compounds. Furthermore, a novel mechanism model of evolution in [Bmim]OTf at low temperature was developed from a microscopic point of view. This research had obvious significance for the mechanism of directional regulation of target products, finally realizing the high efficiency utilization of biomass.

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