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.
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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
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DOI: https://doi.org/10.1007/978-981-13-8687-9_27
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