A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN
In this article, we have a tendency to embrace a novel framework for learning morphological operations using counter-harmonic mean. It combines the conception of morphology with convolutional neural networks. Similarly, the elemental morphological operators of dilation and erosion, opening and closi...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2024-12-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v6-n4/12.pdf |
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| author | Savya Sachi D. Ganesh Rajesh Tiwari S. P. Manikanta L. Bhagyalakshmi Ankita Nigam Sanjay Kumar Suman Rajeev Shrivastava |
| author_facet | Savya Sachi D. Ganesh Rajesh Tiwari S. P. Manikanta L. Bhagyalakshmi Ankita Nigam Sanjay Kumar Suman Rajeev Shrivastava |
| author_sort | Savya Sachi |
| collection | DOAJ |
| description | In this article, we have a tendency to embrace a novel framework for learning morphological operations using counter-harmonic mean. It combines the conception of morphology with convolutional neural networks. Similarly, the elemental morphological operators of dilation and erosion, opening and closing, as well as the more refined top-hat transform, for which we disclose a real-world application from the steel industry, are all subjected to a rigorous experimental validation. Our system learns about the structuring element and the operator's composition via online learning and stochastic gradient descent. It works effectively with massive datasets and in online environments. |
| format | Article |
| id | doaj-art-4f881b5f8b244c0b80d5e2ccbd4482ee |
| institution | OA Journals |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-4f881b5f8b244c0b80d5e2ccbd4482ee2025-08-20T01:55:04ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112024-12-01641531154010.24874/PES06.04.012A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEANSavya Sachi 0https://orcid.org/0000-0002-7920-5628D. Ganesh 1https://orcid.org/0000-0001-5920-7864Rajesh Tiwari 2https://orcid.org/0000-0001-5391-3231S. P. Manikanta 3https://orcid.org/0000-0001-5920-7864L. Bhagyalakshmi 4https://orcid.org/0000-0002-9996-2386Ankita Nigam 5https://orcid.org/0000-0002-6495-2284Sanjay Kumar Suman 6https://orcid.org/0000-0003-3278-9942Rajeev Shrivastava 7https://orcid.org/0000-0001-7907-3701Dept. of IT L N Mishra College of Business Management, Muzaffarpur Bihar, India Dept. of CSE, Mohan Babu University, Tirupati, AP, India Department of CSE(AIML), CMR Engineering College, Hyderabad, Telangana, India Department of ECE, St. Martin's Engineering College, Secunderabad, Telangana, India Dept. of ECE, Rajalakshmi Engineering College, Chennai, TN, India Princeton Institute of Engineering and Technology for Women, Hyderabad, Telangana, India Dept. of ECE, and Dean R&D, St. Martin's Engineering College, Secunderabad, Telangana, India Princeton Institute of Engineering and Technology for Women, Hyderabad, Telangana, India In this article, we have a tendency to embrace a novel framework for learning morphological operations using counter-harmonic mean. It combines the conception of morphology with convolutional neural networks. Similarly, the elemental morphological operators of dilation and erosion, opening and closing, as well as the more refined top-hat transform, for which we disclose a real-world application from the steel industry, are all subjected to a rigorous experimental validation. Our system learns about the structuring element and the operator's composition via online learning and stochastic gradient descent. It works effectively with massive datasets and in online environments.https://pesjournal.net/journal/v6-n4/12.pdfmathematical morphologyconvolutional neural networkson-line mastering and strutting element |
| spellingShingle | Savya Sachi D. Ganesh Rajesh Tiwari S. P. Manikanta L. Bhagyalakshmi Ankita Nigam Sanjay Kumar Suman Rajeev Shrivastava A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN Proceedings on Engineering Sciences mathematical morphology convolutional neural networks on-line mastering and strutting element |
| title | A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN |
| title_full | A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN |
| title_fullStr | A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN |
| title_full_unstemmed | A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN |
| title_short | A FRAMEWORK FOR MORPHOLOGICAL OPERATIONS USING COUNTER HARMONIC MEAN |
| title_sort | framework for morphological operations using counter harmonic mean |
| topic | mathematical morphology convolutional neural networks on-line mastering and strutting element |
| url | https://pesjournal.net/journal/v6-n4/12.pdf |
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