Abstract
The study of image classification is based on various aspects. One of the dimension-based image classifications is shape description technique. This region-based multidimensional descriptor acts as a morphometric tool for contour analysis of close convex images. However, in some cases, we may get same information from extrinsic structural feature components of different cluster-based regions as well as different hidden information from several intrinsic structural components of the same cluster region. At the same time, the problem becomes severe if the image undergoes homogeneous transformations, such as translation, rotation and shearing. Analyzing the image object using an invariant shape descriptor may alleviate such problems. In this paper, we introduce a new class of invariant shape descriptor tool known as circularity which works locally and globally throughout the image texture pattern. A comparative study on variant and invariant transformations which work on multiresolution, multilateral and multicluster image texture has also been discussed.
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
References
E. Gonzalez, F. Bianconi, A. Fernandez, An investigation on the use of local multi-resolution pattern for image classification. Inf. Sci. (2016). (0020-0255, Elsevier)
D. Zhang, G. Lu, Review of shape representation and description techniques. Pattern Recogn. 37, 1–19 (2004). https://doi.org/10.1016/j.patcog.2003.07.008 (0031-3203)
E.R. Dougherty, Y. Cheng, Morphological pattern spectrum classification of noisy shapes: exterior granulometries. Pattern Recogn. 28(1), 81–98 (1995). 0031-3203(94)00083-2
X. Yu, X. Lin, Y. Dai, K. Zhu, Image edge detection based tool condition monitoring with morphological component analysis, 0 019-0578/& ISA Published by Elsevier Ltd. (2017)
S. Loncaric, A survey of shape analysis techniques. Pattern Recogn. 31(8), 983–1001 (1998). PII: S0031-2023(97)00122-2
P. Maragos, Pattern spectrum and multiscale shape representation. IEEE Trans. Pattern Anal. Mach. Intell. II(7) (1989) (0162-8828/89/0700-0701)
W. Shen, Y. Wang, X. Bai, H. Wang, L.J. Latecki, Shape clustering: common structure discovery. Pattern Recogn. 46(2013), 539–550 (2012). (0031-3203, Elsevier Ltd.)
X. Wang, Z. Wang, A novel method for image retrieval based on structure elements’ descriptor. J. Vis. Commun. Image Represent. 24, 63–74 (2013) (1047-3203/2012, Elsevier Inc.)
G.-H. Liu, Z.-Y. Li, L. Zhang, Y. Xu, Image retrieval based on micro-structure descriptor. Pattern Recogn. 44, 2123–2133 (2011). (0031-3203, Published by Elsevier Ltd.)
http://docplayer.net/27418028-Lecture-9-shape-description-regions.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Santra, S., Mandal, S. (2020). A New Approach Toward Invariant Shape Descriptor Tools for Shape Classification Through Morphological Analysis of Image. In: Maharatna, K., Kanjilal, M., Konar, S., Nandi, S., Das, K. (eds) Computational Advancement in Communication Circuits and Systems. Lecture Notes in Electrical Engineering, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-13-8687-9_27
Download citation
DOI: https://doi.org/10.1007/978-981-13-8687-9_27
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8686-2
Online ISBN: 978-981-13-8687-9
eBook Packages: EngineeringEngineering (R0)