End‐to‐end learning interpolation for object tracking in low frame‐rate video

Liu, Liqiang; Cao, Jianzhong

doi:10.1049/iet-ipr.2019.0944

Cited by 12 publications

(2 citation statements)

References 37 publications

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“…In the power grid operation site, it is necessary to transmit the video and image of the field operation in the channel with limited bandwidth at low bit rate, but the transmission bandwidth and storage capacity of the video image are difficult to meet the requirements when the definition and resolution of the video image are continuously enhanced. erefore, it is necessary to compress the low frame rate video animation video [2].…”

Section: Introductionmentioning

confidence: 99%

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Yang¹,

Xiang²,

2021

Security and Communication Networks

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When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.

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Section: Introductionmentioning

confidence: 99%

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Yang¹,

Xiang²,

2021

Security and Communication Networks

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“…The segmentation of interpolated synthetic images can provide additional overlapping cell masks to ease cell tracking. Similar approaches have been developed to track objects in low frame rate videos 78,79 , enhance computer animation 80,81 , or improve temporal resolution in microscopy 82 . By focusing on the overlap of single cell masks, FIEST avoided tracking requirements that can change during life cycle transitions, such as overreliance on cell centroids 55 , matching of cell structural information features 83 , or particle linking calculations 84 .…”

Section: Frame Interpolation Enhanced Single-cell Tracking (Fiest) Du...mentioning

confidence: 99%

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Ramakanth,

Kennedy,

Yalcinkaya

et al. 2024

Preprint

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The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

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A Cross-Layer Fusion Multi-target Detection and Recognition Method Based on Improved FPN Model in Complex Traffic Environment

Li¹,

Chen²,

Jun-ji³

et al. 2021

Lecture Notes in Computer Science

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End‐to‐end learning interpolation for object tracking in low frame‐rate video

Cited by 12 publications

References 37 publications

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Research on Low Frame Rate Video Compression Algorithm in the Context of New Media

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

A Cross-Layer Fusion Multi-target Detection and Recognition Method Based on Improved FPN Model in Complex Traffic Environment

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