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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
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 |
| Summary: | 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. |
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