Abstract
An adaptive algorithm for processing monochrome images in order to detect and isolate objects of interest from the background noise is investigated. Objects differ from the background in that they form compact structures during threshold processing. Their parameters are the area and geometric compactness coefficient. In the course of multi-threshold processing, a set of binary slices is obtained, on which the parameters of objects are measured. When combining the slices, a three-dimensional structure is created using the percolation effect. To select each object, the most suitable slice is selected, on which the object satisfies the accepted restrictions on area and compactness. To simulate the algorithm, objects in the form of a disk and a square are selected. The tasks of detecting objects and distinguishing them are solved. Theoretical and experimental characteristics of the quality of detection and discrimination are obtained. To demonstrate the efficiency of the algorithm, the results of processing real images obtained by remote surveillance systems are presented. In addition to such systems, the algorithm makes it possible to isolate bacteria and spores in biology and medicine, and is useful in the study of heterogeneities of materials and tissues.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS J. Photogrammetry Remote Sens. 117, 11–28 (2016)
Arias-Castro, E., Grimmett, G.R.: Cluster detection in networks using percolation. Bernoulli 19(2), 676–719 (2013)
Patil, G.P., Taillie, C.: Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ. Ecol. Stat. 11, 183–197 (2004)
Zhou, W., Troy, A.: An object-oriented approach for analyzing and characterizing urban landscape at the parcel level. Int. J. of Remote Sens. 29(11), 3119–3135 (2008)
Gu, H., et al.: An efficient parallel multi-scale segmentation method for remote sensing imagery. Remote Sens. 10(4), 590–608 (2018)
Volkov, V.Y., Bogachev, M.I.: Detection and extraction of objects in digital images. In: Proceedings 9th Mediterranean Conference on Embedded Computing, 9134228 (2020)
Shivahare, B.D., Gupta, S.K.: Multilevel thresholding-based image segmentation using whale optimization algorithm. Int. J. Innovative Technol. Exploring Eng. (IJITEE) 8(12) (2019)
Cuevas, E., González, A.: Algorithm based on the behavior of locust swarms. Math. Problems Eng. (2015). Article ID 805357
Gonzales, R.C., Woods, R.E.: Digital Image Processing. 4 Edn. Pearson (2018)
Sheikh, A.: Principles of transmission and detection of digital signals. In: Digital Communication (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Volkov, V. (2023). Compact Objects Extraction in Noisy Images. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_18
Download citation
DOI: https://doi.org/10.1007/978-981-99-0923-0_18
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-0922-3
Online ISBN: 978-981-99-0923-0
eBook Packages: EngineeringEngineering (R0)