{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/depth-based-selective-blurring-in-stereo","title":"Depth-Based Selective Blurring in Stereo Images Using Accelerated Framework","arxiv_id":"2001.07809","date":"2020-01-21","proceeding":null,"authors":["Subhayan Mukherjee","Ram Mohana Reddy Guddeti"],"abstract":"We propose a hybrid method for stereo disparity estimation by combining block and region-based stereo matching approaches. It generates dense depth maps from disparity measurements of only 18 % image pixels (left or right). The methodology involves segmenting pixel lightness values using fast K-Means implementation, refining segment boundaries using morphological filtering and connected components analysis; then determining boundaries' disparities using sum of absolute differences (SAD) cost function. Complete disparity maps are reconstructed from boundaries' disparities. We consider an application of our method for depth-based selective blurring of non-interest regions of stereo images, using Gaussian blur to de-focus users' non-interest regions. Experiments on Middlebury dataset demonstrate that our method outperforms traditional disparity estimation approaches using SAD and normalized cross correlation by up to 33.6 % and some recent methods by up to 6.1 %. Further, our method is highly parallelizable using CPU and GPU framework based on Java Thread Pool and APARAPI with speed-up of 5.8 for 250 stereo video frames (4,096 x 2,304).","url_abs":"https://arxiv.org/abs/2001.07809v1","url_pdf":"https://arxiv.org/pdf/2001.07809v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"depth-based-selective-blurring-in-stereo","repo_url":"https://github.com/subhayanmukherjee/depthblur","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"CPU"},{"task_slug":"disparity-estimation","task_name":"Disparity Estimation"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"stereo-disparity-estimation","task_name":"Stereo Disparity Estimation"},{"task_slug":"stereo-matching-1","task_name":"Stereo Matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}