The Implementation of Image Conceptualization Split-Screen Stitching and Positioning Technology in Film and Television Production

In order to study the technology of image conception, splitting, stitching and positioning in film and television production, this paper first discusses the relevant research literature, then designs an improved biomedical image segmentation convolution network model applied in film and television production, and then verifies the effectiveness of the proposed model. Ultimately, the paper summarizes the research findings. Aiming at the problem that the traditional image mosaic positioning model has poor robustness because of its insufficient ability to extract features and inaccurate segmentation and positioning areas, this study proposes a biomedical image segmentation convolutional network model that is based on dense block and void space convolutional pooling pyramidal module. Additionally, an attention mechanism is introduced to enhance the biomedical image segmentation convolutional network model. The results show that the accuracy, recall, and F1 value of the biomedical image segmentation convolutional network model are 96.48%, 95.24%, and 95.96%, respectively, on the Colombian uncompressed image stitching detection dataset, and the accuracy, recall, and F1 value of the improved biomedical image segmentation convolutional network model are 98.19%, 96.23%, and F1 value of 97.21%. In summary, the improved convolution network model for biomedical image segmentation has excellent performance, and it has certain application value in image conception, mirror splicing and positioning in film and television production.

@article{Deng2023,
  title     = {The Implementation of Image Conceptualization Split-Screen Stitching and Positioning Technology in Film and Television Production},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {8},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Zhouzhou Deng and Rongshen Zhu},
  doi       = {10.14569/IJACSA.2023.01408122},
  url       = {https://doi.org/10.14569/IJACSA.2023.01408122}
}