Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wibisono, Jan Kristanto | en_US |
dc.contributor.author | Hang, Hsueh-Ming | en_US |
dc.date.accessioned | 2018-08-21T05:57:14Z | - |
dc.date.available | 2018-08-21T05:57:14Z | - |
dc.date.issued | 2017-01-01 | en_US |
dc.identifier.uri | http://hdl.handle.net/11536/147198 | - |
dc.description.abstract | In the past a few decades, many schemes have been proposed for segmenting a color image into meaningful regions. However, the newly availability of depth data provides opportunities to explore and improve the image segmentation performance. In addition, the new image processing tools based on deep learning technology are aggressively developed recently. This paper proposes a method of combining color and depth data to segment an image. As an initial stage, we partition a color image into regions using the DeepEdge tool, an image edge detection scheme developed based on the CNN (Convolutional Neural Net) technique. Then, we use the RANSAC tool to identify and merge regions with similar planar geometry (based on the depth information). At the final stage, guided by the DeepEdge information, a region merging method is employed to fine-tune the merged regions based on the color and depth similarity. Comparing to our previous results, the DeepEdge method together with the depth information helps in improving the segmentation result in most cases. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | RGBD Segmentation | en_US |
dc.subject | DeepEdge | en_US |
dc.subject | RANSAC | en_US |
dc.title | RGBD IMAGE SEGMENTATION USING DEEP EDGE | en_US |
dc.type | Proceedings Paper | en_US |
dc.identifier.journal | 2017 INTERNATIONAL SYMPOSIUM ON INTELLIGENT SIGNAL PROCESSING AND COMMUNICATION SYSTEMS (ISPACS 2017) | en_US |
dc.citation.spage | 565 | en_US |
dc.citation.epage | 569 | en_US |
dc.contributor.department | 交大名義發表 | zh_TW |
dc.contributor.department | National Chiao Tung University | en_US |
dc.identifier.wosnumber | WOS:000428142000108 | en_US |
Appears in Collections: | Conferences Paper |