Research on intelligent analysis and identification of visualization scenes in transport supervision hall based on image processing technology

[1] Turini, J., & Vo, L. H. (2020). Effects of spatial layout and object content on visual scene recognition. Journal of Vision, 20(11), 1070. Search in Google Scholar

[2] Lu, X., Li, X., & Mou, L. (2017). Semi-supervised multitask learning for scene recognition. IEEE Transactions on Cybernetics, 45(9), 1967-1976. Search in Google Scholar

[3] Xie, L., Lee, F., Liu, L., Yin, Z., & Chen, Q. (2020). Hierarchical coding of convolutional features for scene recognition. IEEE Transactions on Multimedia, 22(5), 1182-1192. Search in Google Scholar

[4] E, Y. Z. A., B, Z. F., C, F. H., & D, Y. L. (2021). Pmmn: pre-trained multi-modal network for scene text recognition. Pattern Recognition Letters. Search in Google Scholar

[5] Lukavsky, J. D. F. (2017). Visual properties and memorising scenes: effects of image-space sparseness and uniformity. Attention, perception & psychophysics, 79(7). Search in Google Scholar

[6] Liu, M., & Siegwart, R. (2017). Topological mapping and scene recognition with lightweight color descriptors for an omnidirectional camera. IEEE Transactions on Robotics, 30(2), 310-324. Search in Google Scholar

[7] Tang, P., Wang, H., & Kwong, S. (2017). G-ms2f: googlenet based multi-stage feature fusion of deep cnn for scene recognition. Neurocomputing. Search in Google Scholar

[8] Savchenko, A. V., Demochkin, K. V., & Grechikhin, I. S. (2022). Preference prediction based on a photo gallery analysis with scene recognition and object detection. Pattern Recognition, 121, 108248-. Search in Google Scholar

[9] A, L. X., A, F. L., B, L. L., A, Z. Y., A, Y. Y., & A, W. W., et al. (2018). Improved spatial pyramid matching for scene recognition. Pattern Recognition, 82, 118-129. Search in Google Scholar

[10] Naiemi, F., Ghods, V., & Khalesi, H. (2020). A novel pipeline framework for multi oriented scene text image detection and recognition. Expert Systems with Applications, 170(3), 114549. Search in Google Scholar

[11] Anbarasu, B., & Anitha, G. (2018). Indoor scene recognition for micro aerial vehicles navigation using enhanced-gist descriptors. Defence Science Journal, 68(2), 129-137. Search in Google Scholar

[12] Chan, Y. T. (2019). Deep learning-based scene-awareness approach for intelligent change detection in videos. Journal of Electronic Imaging, 28(1), 1. Search in Google Scholar

[13] Zhang, XinWang, YongchengZhang, NingXu, DongdongChen, BoBen, GuangliWang, Xue. (2019). Scene classification of high-resolution remote sensing images based on imfnet. Journal of Applied Remote Sensing, 13(4). Search in Google Scholar

[14] Harel, A. (2020). P2: a novel erp marker of global scene perception. Journal of Vision, 20(11), 908. Search in Google Scholar

[15] Zhang, X., Wang, L., & Su, Y. (2020). Visual place recognition: a survey from deep learning perspective. Pattern Recognition. Search in Google Scholar

[16] Puthenputhussery, A., Liu, Q., & Liu, C. (2017). A sparse representation model using the complete marginal fisher analysis framework and its applications to visual recognition. IEEE Transactions on Multimedia, 1-1. Search in Google Scholar

[17] Parhizkar, M., Amirfakhrian, M., & Darba, A. (2022). Recognizing the damaged surface parts of cars in the real scene using a deep learning framework. Mathematical Problems in Engineering, 2022. Search in Google Scholar

[18] Gao, W., Zhu, Y., Zhang, W., Zhang, K., & Gao, H. (2019). A hierarchical recurrent approach to predict scene graphs from a visual-attention-oriented perspective. Computational Intelligence. Search in Google Scholar

[19] Cai, Z., Long, Y., & Shao, L. (2018). Adaptive rgb image recognition by visual-depth embedding. IEEE Transactions on Image Processing, 1-1. Search in Google Scholar