Abstract
In this demonstration paper, we present “e2evideo” a versatile Python package composed of domain-independent modules. These modules can be seamlessly customised to suit specialised tasks by modifying specific attributes, allowing users to tailor functionality to meet the requirements of a targeted task. The package offers a variety of functionalities, such as interpolating missing video frames, background subtraction, image resizing, and extracting features utilising state-of-the-art machine learning techniques. With its comprehensive set of features, “e2evideo” stands as a facilitating tool for developers in the creation of image and video processing applications, serving diverse needs across various fields of computer vision.
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References
Al Outa, A., et al.: Cellular, a cell autophagy imaging dataset. Sci. Data 10(1), 806 (2023)
Culjak, I., Abram, D., Pribanic, T., Dzapo, H., Cifrek, M.: A brief introduction to OpenCV. In: 2012 Proceedings of the 35th International Convention MIPRO, pp. 1725–1730. IEEE (2012)
Harris, C.R., et al.: Array programming with NumPy. Nature 585(7825), 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Khalid, S., Khalil, T., Nasreen, S.: A survey of feature selection and feature extraction techniques in machine learning. In: 2014 Science and Information Conference, pp. 372–378. IEEE (2014)
Klein, A., et al.: imageio/imageio: v2.31.3 (2023). https://doi.org/10.5281/zenodo.8313513
Lu, T., Palaiahnakote, S., Tan, C.L., Liu, W.: Video preprocessing. In: Video Text Detection. ACVPR, pp. 19–47. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6515-6_2
Müller, D., Kramer, F.: MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning. BMC Med. Imaging 21, 1–11 (2021). https://doi.org/10.1186/s12880-020-00543-7
Nordmo, T.A.S., et al.: Njord: a fishing trawler dataset. In: Proceedings of the 13th ACM Multimedia Systems Conference, pp. 197–202 (2022)
Oquab, M., et al.: Dinov2: learning robust visual features without supervision. arXiv preprint: arXiv:2304.07193 (2023)
Svoren, H., et al.: Toadstool: a dataset for training emotional intelligent machines playing super mario bros. In: Proceedings of the 11th ACM Multimedia Systems Conference, pp. 309–314 (2020)
Van der Walt, S., et al.: scikit-image: image processing in python. PeerJ 2, e453 (2014)
Zivkovic, Z., Van Der Heijden, F.: Efficient adaptive density estimation per image pixel for the task of background subtraction. Pattern Recogn. Lett. 27(7), 773–780 (2006)
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Alawad, F., Halvorsen, P., Riegler, M.A. (2024). E2Evideo: End to End Video and Image Pre-processing and Analysis Tool. In: Rudinac, S., et al. MultiMedia Modeling. MMM 2024. Lecture Notes in Computer Science, vol 14557. Springer, Cham. https://doi.org/10.1007/978-3-031-53302-0_19
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