{"id":22804443,"url":"https://github.com/jingew/morning-glory-detection","last_synced_at":"2025-10-31T07:52:11.384Z","repository":{"id":187393501,"uuid":"272489053","full_name":"JingeW/Morning-Glory-Detection","owner":"JingeW","description":"A detection tool with Matlab UI","archived":false,"fork":false,"pushed_at":"2024-06-01T00:59:44.000Z","size":21106,"stargazers_count":4,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-08T07:28:34.283Z","etag":null,"topics":["agriculture","detection","image-processing","matlab","ui"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/JingeW.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2020-06-15T16:25:47.000Z","updated_at":"2024-12-05T16:39:35.000Z","dependencies_parsed_at":"2023-08-10T06:53:04.142Z","dependency_job_id":"f9333a5c-26fb-484d-b081-ff7b96169fd4","html_url":"https://github.com/JingeW/Morning-Glory-Detection","commit_stats":null,"previous_names":["jingew/morning-gloary-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/JingeW/Morning-Glory-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JingeW%2FMorning-Glory-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JingeW%2FMorning-Glory-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JingeW%2FMorning-Glory-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JingeW%2FMorning-Glory-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/JingeW","download_url":"https://codeload.github.com/JingeW/Morning-Glory-Detection/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/JingeW%2FMorning-Glory-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281953496,"owners_count":26589146,"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","status":"online","status_checked_at":"2025-10-31T02:00:07.401Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["agriculture","detection","image-processing","matlab","ui"],"created_at":"2024-12-12T10:08:10.366Z","updated_at":"2025-10-31T07:52:11.356Z","avatar_url":"https://github.com/JingeW.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Morning Glory Detection\n[![License: MIT](https://img.shields.io/badge/License-MIT-brightgreen.svg)](LICENSE)\n\nA detection tool with Matlab UI.\n\n📃 [Read the Full Paper](https://www.mdpi.com/2624-7402/6/1/34).\n\n## Instruction:\nNeed to install Matlab first.\\\nRun \"main.m\" to activate the app interface.  \n\nPlay with the following steps:\n1. Click \"Open New Image\" to load the image you want to detect. Browse and choose the image in the pop-up window.\\\n   \u003cimg src=\"Figs/img1.png\" alt=\"Sample Image\" width=\"600\"/\u003e\n2. Choose the segmentation method for shadow removal, and click \"Segment\" to apply it.\n\t- 2.1 K-means\n\t\tThe parameter of K-means is the number of the cluster. The default k equals 3. Small k's can avoid most of the noise in the shadow. Larger k provides more details.\n\t- 2.2 Mean shift\n\t\tThe parameter of Mean shift is the bandwidth of the kernel. The default bw equals to 0.2. It's a faster method for segmentation. The detection result is very similar to the K-means when k = 3.\n    \u003cimg src=\"Figs/img2.png\" alt=\"Sample Image\" width=\"600\"/\u003e\n3. Click \"Mask\" to generate the binary mask for shadow removal.\\\n   \u003cimg src=\"Figs/img3.png\" alt=\"Sample Image\" width=\"600\"/\u003e\n4. Click \"Output\" to get the detection result.\\\n   \u003cimg src=\"Figs/img4.png\" alt=\"Sample Image\" width=\"600\"/\u003e\n5. (optional) Save the image.\n6. Click \"Count\" to count the number of detected clusters.\\\n   \u003cimg src=\"Figs/img5.png\" alt=\"Sample Image\" width=\"600\"/\u003e\n\n* After each step you might need to wait for a few seconds to see the image change, which indicates the current step finished. \n  The waiting time depends on the input file size and computing speed.\n\n* This app referenced the mean shift method from *K. Fukunaga and L.D. Hosteler, \n  \"The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition\".* [PDF](https://ieeexplore.ieee.org/document/1055330)\n\n## Citation\n\n@article{valicharla2024morning,\n  title={Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models},\n  author={Valicharla, Sruthi Keerthi and Wang, Jinge and Li, Xin and Gururajan, Srikanth and Karimzadeh, Roghaiyeh and Park, Yong-Lak},\n  journal={AgriEngineering},\n  volume={6},\n  number={1},\n  pages={555--573},\n  year={2024},\n  publisher={MDPI}\n}\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjingew%2Fmorning-glory-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjingew%2Fmorning-glory-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjingew%2Fmorning-glory-detection/lists"}