Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques
Enhanced Image content classification has improved dramatically with the advent of CNNs. This paper presents an enhanced method for semantic partitioning through merging traditional convolutional level and atrous (extended) convolution techniques. Our approach takes advantage of the hierarchical...
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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2091/ http://eprints.intimal.edu.my/2091/2/630 http://eprints.intimal.edu.my/2091/3/joit2024_41b.pdf |
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| author | Meghana, H.V. Ushashree, R. |
| author_facet | Meghana, H.V. Ushashree, R. |
| author_sort | Meghana, H.V. |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Enhanced Image content classification has improved dramatically with the advent of CNNs.
This paper presents an enhanced method for semantic partitioning through merging traditional
convolutional level and atrous (extended) convolution techniques. Our approach takes
advantage of the hierarchical feature extraction capabilities of CNNs, while incorporating atrous
convolutions to capture multi-scale contextual information without increasing the
computational load. The proposed feature combines standard diffraction layers for detailed
feature extraction that broadens the perceptive field, thus improving segmentation accuracy,
especially on multiscale features Extensive testing on the datasets including PASCAL VOC
2012 and Cityscapes. |
| first_indexed | 2025-11-14T11:58:46Z |
| format | Article |
| id | intimal-2091 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:46Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20912025-07-12T03:18:30Z http://eprints.intimal.edu.my/2091/ Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques Meghana, H.V. Ushashree, R. QA75 Electronic computers. Computer science RA Public aspects of medicine T Technology (General) Enhanced Image content classification has improved dramatically with the advent of CNNs. This paper presents an enhanced method for semantic partitioning through merging traditional convolutional level and atrous (extended) convolution techniques. Our approach takes advantage of the hierarchical feature extraction capabilities of CNNs, while incorporating atrous convolutions to capture multi-scale contextual information without increasing the computational load. The proposed feature combines standard diffraction layers for detailed feature extraction that broadens the perceptive field, thus improving segmentation accuracy, especially on multiscale features Extensive testing on the datasets including PASCAL VOC 2012 and Cityscapes. INTI International University 2024-12 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2091/2/630 text en cc_by_4 http://eprints.intimal.edu.my/2091/3/joit2024_41b.pdf Meghana, H.V. and Ushashree, R. (2024) Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques. Journal of Innovation and Technology, 2024 (41). pp. 1-5. ISSN 2805-5179 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science RA Public aspects of medicine T Technology (General) Meghana, H.V. Ushashree, R. Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title | Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title_full | Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title_fullStr | Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title_full_unstemmed | Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title_short | Enhanced Semantic Image Segmentation Through Convolutional and Atrous Convolution Techniques |
| title_sort | enhanced semantic image segmentation through convolutional and atrous convolution techniques |
| topic | QA75 Electronic computers. Computer science RA Public aspects of medicine T Technology (General) |
| url | http://eprints.intimal.edu.my/2091/ http://eprints.intimal.edu.my/2091/ http://eprints.intimal.edu.my/2091/2/630 http://eprints.intimal.edu.my/2091/3/joit2024_41b.pdf |