l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences
Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & major...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9328234/ |
| _version_ | 1818616606019813376 |
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| author | Premith Kumar Chilukuri Preethi Padala Pushkal Padala Venkata Subbaiah Desanamukula Prasad Reddy Pvgd |
| author_facet | Premith Kumar Chilukuri Preethi Padala Pushkal Padala Venkata Subbaiah Desanamukula Prasad Reddy Pvgd |
| author_sort | Premith Kumar Chilukuri |
| collection | DOAJ |
| description | Image-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & major-disadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each sub-module is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoED-network) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-feature-maps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & feature-mapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L<sub>α,β,γ</sub>, FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs. |
| first_indexed | 2024-12-16T16:52:28Z |
| format | Article |
| id | doaj.art-98a8593b8b14482ea688c7e1185b39aa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T16:52:28Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-98a8593b8b14482ea688c7e1185b39aa2022-12-21T22:23:59ZengIEEEIEEE Access2169-35362021-01-019167611678210.1109/ACCESS.2021.30524749328234l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image SequencesPremith Kumar Chilukuri0https://orcid.org/0000-0002-9392-7264Preethi Padala1https://orcid.org/0000-0003-1380-0966Pushkal Padala2Venkata Subbaiah Desanamukula3https://orcid.org/0000-0001-7850-5306Prasad Reddy Pvgd4Department of CS and SE, Andhra University College of Engineering (A), Visakhapatnam, IndiaDepartment of Computer Science and Engineering (CSE), National Institute of Technology Surathkal, Mangalore, IndiaDepartment of Computer Science and Engineering (CSE), The National Institute of Engineering, Mysore, IndiaDepartment of CS and SE, Andhra University College of Engineering (A), Visakhapatnam, IndiaDepartment of CS and SE, Andhra University College of Engineering (A), Visakhapatnam, IndiaImage-stitching (or) mosaicing is considered an active research-topic with numerous use-cases in computer-vision, AR/VR, computer-graphics domains, but maintaining homogeneity among the input image sequences during the stitching/mosaicing process is considered as a primary-limitation & major-disadvantage. To tackle these limitations, this article has introduced a robust and reliable image stitching methodology (l,r-Stitch Unit), which considers multiple non-homogeneous image sequences as input to generate a reliable panoramically stitched wide view as the final output. The l,r-Stitch Unit further consists of a pre-processing, post-processing sub-modules & a l,r-PanoED-network, where each sub-module is a robust ensemble of several deep-learning, computer-vision & image-handling techniques. This article has also introduced a novel convolutional-encoder-decoder deep-neural-network (l,r-PanoED-network) with a unique split-encoding-network methodology, to stitch non-coherent input left, right stereo image pairs. The encoder-network of the proposed l,r-PanoED extracts semantically rich deep-feature-maps from the input to stitch/map them into a wide-panoramic domain, the feature-extraction & feature-mapping operations are performed simultaneously in the l,r-PanoED's encoder-network based on the split-encoding-network methodology. The decoder-network of l,r-PanoED adaptively reconstructs the output panoramic-view from the encoder networks' bottle-neck feature-maps. The proposed l,r-Stitch Unit has been rigorously benchmarked with alternative image-stitching methodologies on our custom-built traffic dataset and several other public-datasets. Multiple evaluation metrics (SSIM, PSNR, MSE, L<sub>α,β,γ</sub>, FM-rate, Average-latency-time) & wild-Conditions (rotational/color/intensity variances, noise, etc) were considered during the benchmarking analysis, and based on the results, our proposed method has outperformed among other image-stitching methodologies and has proved to be effective even in wild non-homogeneous inputs.https://ieeexplore.ieee.org/document/9328234/Deep feature extractionencoder-decoder cnnimage mosaicingmulti-image registrationnon-homogeneous image stitching |
| spellingShingle | Premith Kumar Chilukuri Preethi Padala Pushkal Padala Venkata Subbaiah Desanamukula Prasad Reddy Pvgd l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences IEEE Access Deep feature extraction encoder-decoder cnn image mosaicing multi-image registration non-homogeneous image stitching |
| title | l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences |
| title_full | l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences |
| title_fullStr | l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences |
| title_full_unstemmed | l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences |
| title_short | l, r-Stitch Unit: Encoder-Decoder-CNN Based Image-Mosaicing Mechanism for Stitching Non-Homogeneous Image Sequences |
| title_sort | l r stitch unit encoder decoder cnn based image mosaicing mechanism for stitching non homogeneous image sequences |
| topic | Deep feature extraction encoder-decoder cnn image mosaicing multi-image registration non-homogeneous image stitching |
| url | https://ieeexplore.ieee.org/document/9328234/ |
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