Applying image registration algorithm combined with CNN model to video image stitching

Cao, Weiran

doi:10.1007/s11227-021-03840-2

Cited by 16 publications

(12 citation statements)

References 36 publications

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“…e algorithm uses the thumbnail of the image to be coded to search for similar images in the cloud dataset and uses the superresolution reconstruction method similar to the cloud dataset to obtain the preliminary reconstructed image as the edge information of the distributed source coding and then iteratively optimizes between the final reconstruction and the edge information through the bit plane based checker coding to improve the reconstruction quality. is algorithm can obtain good color reconstruction and geometric reconstruction, but the iterative reconstruction process using edge information has high complexity [5]. Wang and others proposed a region prediction coding algorithm based on cloud storage image.…”

Section: Literature Reviewmentioning

confidence: 99%

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Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

2022

Mathematical Problems in Engineering

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In order to effectively improve the quality of video image transmission, this paper proposes a method of digital multimedia video image coding. The transmission of digital multimedia video image fault-tolerant coding requires sparse decomposition of a digital multimedia video image to obtain the linear form of the image and complete the transmission of video image fault-tolerant coding. The traditional method of fault-tolerant coding is based on human visual characteristics but ignores the linear form of the digital multimedia video image, which leads to the unsatisfactory effect of coding and transmission. In this paper, a fault-tolerant coding method based on wavelet transform and vector quantization is proposed to decompose and reconstruct digital multimedia video images. The smoothness of wavelet transform can remove visual redundancy; the decomposed image is vector quantized. The mean square deviation method and the similar scalar optimal quantization method are used to select and calculate the image vector, construct the over complete database of a digital multimedia video image, and normalize it; the digital multimedia video image is thinly decomposed by asymmetric atoms, and a linear representation of the image is obtained. According to the above-given operations, we can master the distribution range and law of pixels and realize fault-tolerant coding. The experimental results show that when the number of iterations is 15, the CR index is the same, PSNR increases by 8.7%, coding is 23.7% faster and decoding is 15% faster. Conclusion. The proposed method can not only improve the speed of fault-tolerant coding but also improve the quality of video image transmission.

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Section: Literature Reviewmentioning

confidence: 99%

“…e digital multimedia video image is divided into several subimages, and the digital multimedia video image is quantized and encoded by the following formula (5). e specific expression is as follows:…”

Section: Fault Tolerant Digital Video Transmission Systemmentioning

confidence: 99%

Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

2022

Mathematical Problems in Engineering

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“…Cao [25] proposed an image registration algorithm using a convolutional neural network (CNN) for video image stitching. e proposed method is mostly used in UAVs that enhance the performance rate of video image stitching techniques.…”

Section: Related Workmentioning

confidence: 99%

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Peng¹,

Yu²

2022

International Transactions on Electrical Energy Systems

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Image stitching in 3D film production synchronizes multiple independent frame images as a single high-resolution output. The stitching process is common for displaying a large coverage or shot in a single frame containing distinct images. For improving the realistic accuracy of 3D film images, this manuscript introduces an Itinerant Pixel-Matching-based Stitching Process (IPMSP). The proposed stitching process relies on cross-sectional pixels identified in merging two or more images. Based on the linear cumulative distribution in augmenting images, the homogeneity feature is identified. If the homogeneity is high then the stitching for linear pixels occurs increasing the frame resolution and size. The cumulative distribution is determined using the recurrent neural network in verifying homogeneity and contrast. For the contrast pixels identified, the cross-sectional matching is performed for substituting similar pixels in the missing sections. The process is repeated until the stitching region is the same based on the homogeneity feature and increased dimensions. Therefore, this process is capable of improving accuracy, precision, substitution, and reducing errors and complexity.

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“…Vision is the most direct way for people to get information. Images are the main way for humans to visually obtain information, and images can vividly and vividly describe the dynamic state of images and can convey information more intuitively and speci cally [1][2][3]. With the advancement of science and technology, the development of computer vision technology has gradually made up for and improved the ability of people's vision that they do not have.…”

Section: Introductionmentioning

confidence: 99%

Video Image Moving Target Recognition Method Based on Generated Countermeasure Network

Dai-hong

Wang

et al. 2022

Computational Intelligence and Neuroscience

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In order to improve the accuracy of video image moving target recognition and shorten the recognition time, a video image moving target recognition method based on a generation countermeasure network is proposed. Firstly, the image sensor is used to collect the video image and obtain the video image sequence. The Roberts operator is used for edge detection and Gaussian smoothing of the video image. Secondly, the normalization method is used to extract the key features of moving targets in video images. Finally, training is carried out alternately to generate the countermeasure network model, and the video image moving target recognition sample results are output according to the training results to realize the video image moving target recognition. The experimental results show that the highest recognition accuracy of the proposed method is 98.1%, and the longest recognition time is only 5.7 s, indicating that its recognition effect is good.

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Applying image registration algorithm combined with CNN model to video image stitching

Cited by 16 publications

References 36 publications

Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

Simulation of Video Image Fault Tolerant Coding Transmission in Digital Multimedia

Image Stitching Technology in 3D Film Production Based on Digital Image Processing

Video Image Moving Target Recognition Method Based on Generated Countermeasure Network

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