{"id":19420099,"url":"https://github.com/wenbihan/octobos_ijcv2016","last_synced_at":"2025-02-25T04:13:00.075Z","repository":{"id":109827579,"uuid":"110599638","full_name":"wenbihan/octobos_IJCV2016","owner":"wenbihan","description":"OCTOBOS, overcomplete transform, learning and application codes, Matlab implementation, IJCV2015 paper","archived":false,"fork":false,"pushed_at":"2017-11-13T21:08:05.000Z","size":1119,"stargazers_count":9,"open_issues_count":0,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-01-07T18:18:42.711Z","etag":null,"topics":["clustering","computer-vision","denoising","ijcv","sparse-coding","transform-learning","unsupervised-learning"],"latest_commit_sha":null,"homepage":"http://transformlearning.csl.illinois.edu/","language":"Matlab","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/wenbihan.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-11-13T20:42:08.000Z","updated_at":"2023-08-28T08:39:28.000Z","dependencies_parsed_at":"2023-05-26T22:15:09.214Z","dependency_job_id":null,"html_url":"https://github.com/wenbihan/octobos_IJCV2016","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenbihan%2Foctobos_IJCV2016","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenbihan%2Foctobos_IJCV2016/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenbihan%2Foctobos_IJCV2016/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wenbihan%2Foctobos_IJCV2016/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wenbihan","download_url":"https://codeload.github.com/wenbihan/octobos_IJCV2016/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240599197,"owners_count":19826959,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","computer-vision","denoising","ijcv","sparse-coding","transform-learning","unsupervised-learning"],"created_at":"2024-11-10T13:21:02.590Z","updated_at":"2025-02-25T04:12:59.969Z","avatar_url":"https://github.com/wenbihan.png","language":"Matlab","funding_links":[],"categories":[],"sub_categories":[],"readme":"# OCTOBOS learning and applications\n=============\n\nOCTOBOS learning accompanies the following publications: \n\n1. \"Structured overcomplete sparsifying transform learning with convergence guarantees and applications\", International Journal of Computer Vision (IJCV), 2015. [IJCV 2015](http://link.springer.com/article/10.1007/s11263-014-0761-1), [PDF available](http://transformlearning.csl.illinois.edu/assets/Sai/JournalPapers/SaiBihanIJCV2014OCTOBOS.pdf)\n\n2. \"Learning overcomplete sparsifying transforms with block cosparsity\", Proc. IEEE International Conference on Image Processing (ICIP), 2014. [ICIP 2014](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=7025161), [PDF available](http://transformlearning.csl.illinois.edu/assets/Sai/ConferencePapers/SaiBihanICIP2014OCTOBOS.pdf), [Slides](http://transformlearning.csl.illinois.edu/assets/Sai/ConferenceSlidesandPosters/SaiBihanICIP2014OCTOBOS_slides.pdf)\n\nDescription:\n-----\n\nOCTOBOS is a formulation and an algorithm that adaptively learns a structured overcomplete sparsifying transform with block cosparsity, or equivalently a union of square sparsifying transforms, and simultaneously clusters the data via sparse coding.\n\nThe OCTOBOS package includes (1) a collection of the SALT Matlab functions, and (2) example demo data used in the OCOTOBOS paper including image denoising, reconstruction, and texture segmentation.\n\nYou can download our other software packages at: [My Homepage](http://web.engr.illinois.edu/~bwen3/) and [Transform Learning Site](http://transformlearning.csl.illinois.edu/).\n\nPaper\n\nIn case of use, please cite our publications:\n\n1. B. Wen, S. Ravishankar, and Y. Bresler. \"Structured overcomplete sparsifying transform learning with convergence guarantees and applications.\" International Journal of Computer Vision (IJCV), vol. 114, no. 2-3, pp. 137-167, 2015.\n\n```\n@article{wen2015octobos,\n  title={Structured overcomplete sparsifying transform learning with convergence guarantees and applications},\n  author={Wen, Bihan and Ravishankar, Saiprasad and Bresler, Yoram},\n  journal={International Journal of Computer Vision (IJCV)},\n  volume={114},\n  number={2-3},\n  pages={137--167},\n  year={2015},\n  publisher={Springer}\n}\n```\n\n2. B. Wen, S. Ravishankar, and Y. Bresler. “Learning overcomplete sparsifying transforms with block cosparsity.\" IEEE International Conference on Image Processing (ICIP), pp. 803-807, 2014.\n\n```\n@inproceedings{wen2014octobos,\n  title={Learning overcomplete sparsifying transforms with block cosparsity},\n  author={Wen, Bihan and Ravishankar, Saiprasad and Bresler, Yoram},\n  booktitle={IEEE International Conference on Image Processing (ICIP)},\n  pages={803--807},\n  year={2014},\n  organization={IEEE}\n}\n```\n\nUse\n---\nAll codes are subject to copyright and may only be used for non-commercial research. In case of use, please cite our publication.\n\nContact Bihan Wen (bihan.wen.uiuc@gmail.com) for any questions.\n\nAcknowledgement\n---\nThe development of this software was supported in part by the National Science Foundation (NSF) under grants CCF 06-35234 and CCF 10-18660.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwenbihan%2Foctobos_ijcv2016","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwenbihan%2Foctobos_ijcv2016","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwenbihan%2Foctobos_ijcv2016/lists"}