Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models
Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In t...
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| Format: | Article |
| Language: | English |
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Slovenian Society for Stereology and Quantitative Image Analysis
2023-04-01
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| Series: | Image Analysis and Stereology |
| Subjects: | |
| Online Access: | https://www.ias-iss.org/ojs/IAS/article/view/2872 |
| _version_ | 1797844708926423040 |
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| author | Ayoub Abderrazak Maarouf Fella Hachouf Soumia Kharfouchi |
| author_facet | Ayoub Abderrazak Maarouf Fella Hachouf Soumia Kharfouchi |
| author_sort | Ayoub Abderrazak Maarouf |
| collection | DOAJ |
| description | Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches. |
| first_indexed | 2024-04-09T17:26:55Z |
| format | Article |
| id | doaj.art-4141f3c5c04b4baaaee90e86ac14cc6a |
| institution | Directory Open Access Journal |
| issn | 1580-3139 1854-5165 |
| language | English |
| last_indexed | 2024-04-09T17:26:55Z |
| publishDate | 2023-04-01 |
| publisher | Slovenian Society for Stereology and Quantitative Image Analysis |
| record_format | Article |
| series | Image Analysis and Stereology |
| spelling | doaj.art-4141f3c5c04b4baaaee90e86ac14cc6a2023-04-18T11:15:23ZengSlovenian Society for Stereology and Quantitative Image AnalysisImage Analysis and Stereology1580-31391854-51652023-04-01421374910.5566/ias.28721092Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive ModelsAyoub Abderrazak Maarouf0Fella Hachouf1Soumia Kharfouchi2Laboratoire d’Automatique et de Robotique, Département d’Electronique Université des fréres Mentouri,Constantine 1 AlgerieLaboratoire d’Automatique et de Robotique, Département d’Electronique Université des fréres Mentouri,Constantine 1 AlgerieDépartement de médecine, Bon Pasteur Chalet des Pins, Université Constantine 3 AlgerieExample-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches.https://www.ias-iss.org/ojs/IAS/article/view/2872exemplar based methodgmmlocal approximated imagestexture synthesis2d-rca models |
| spellingShingle | Ayoub Abderrazak Maarouf Fella Hachouf Soumia Kharfouchi Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models Image Analysis and Stereology exemplar based method gmm local approximated images texture synthesis 2d-rca models |
| title | Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models |
| title_full | Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models |
| title_fullStr | Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models |
| title_full_unstemmed | Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models |
| title_short | Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models |
| title_sort | exemplar based texture synthesis using two random coefficients autoregressive models |
| topic | exemplar based method gmm local approximated images texture synthesis 2d-rca models |
| url | https://www.ias-iss.org/ojs/IAS/article/view/2872 |
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