Abstract
Image segmentation is a prominent task done in computer vision. Image thresholding is one such technique in image segmentation. Thresholding is a method of categorizing image intensities into two classes and on the basis of that yielding an image which is a binary image, and ideally also has all the fine details of region of interest which an image should have for analysis. Image thresholding is widely used as it reduces the computational cost of processing the image and makes processing feasible in real-world applications like medical imaging, object detection, recognition task, character recognition, etc. This paper dwells into the depth of thresholding techniques to know which technique can perform better on all kinds of images so as to extract region of interest. We found out that not every technique is good for all cases, Otsu’s global thresholding is a promising and faster way to segment and generate a binary image, but works well with images having negligible noise and region of interest already being very much clear in the original image, whereas applying methods like Otsu’s thresholding on sliced blocks of images and then merging them or applying moving averages (sliding windows) on images having noise which is distributed in a specific region of image, moving averages gave result better on images which have distributed gradient noise. Whereas the hybrid technique used are a combination of global and local thresholding.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonzalez RC, Woods RE, Eddins SL (2018) Digital image processing, 3rd edn. Pearson, London
Khan AM, Ravi S (2013) Image segmentation methods: a comparative study
Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294
Yogamangalam R, Karthikeyan B (2013) Segmentation techniques comparison in image processing. Int. J Eng SciTechnol 5(1):307–313
Gurusamy V, Kannan S, Nalini G (2013) Review on image segmentation techniques. J Pharm Res 20125:4548–4553
Bhargavi K, Jyothi S (2014) A survey on threshold based segmentation technique in image processing. Int J Innovative Res Dev 3(12):234–239
Kumar N (2018) Thresholding in salient object detection: a survey. Multimedia Tools Appl 77(15):19139–19170
Chang KY, Liu TL, Chen HT, Lai SH (2011) Fusing generic objectness and visual saliency for salient object detection. In: 2011 international conference on computer vision. IEEE, pp 914–921
Kuo TY, Lai YY, Lo YC (2010) A novel image binarization method using hybrid thresholding. In: 2010 IEEE international conference on multimedia and expo. IEEE, pp 608–612
Chou CH, Huang CC, Lin WH, Chang F (2005). Learning to binarize document images using a decision cascade. In: IEEE international conference on image processing 2005, vol 2. IEEE, pp II–518
Niblack W (1986) An introduction to digital image processing, vol 34. Prentice-Hall, Englewood Cliffs
Sauvola J, Pietikäinen M (2000) Adaptive document image binarization. Pattern Recogn 33(2):225–236
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Saxena, S., Jain, S., Tripathi, S., Gupta, K. (2021). Comparative Analysis of Image Segmentation Techniques. In: Hura, G.S., Singh, A.K., Siong Hoe, L. (eds) Advances in Communication and Computational Technology. ICACCT 2019. Lecture Notes in Electrical Engineering, vol 668. Springer, Singapore. https://doi.org/10.1007/978-981-15-5341-7_26
Download citation
DOI: https://doi.org/10.1007/978-981-15-5341-7_26
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5340-0
Online ISBN: 978-981-15-5341-7
eBook Packages: EngineeringEngineering (R0)