View source for Publications/movn.22.pr

{{Publication
| published = true
| date = 2022-12-03
| authors = Minh Ôn Vũ Ngoc, Edwin Carlinet, Jonathan Fabrizio, Thierry Géraud
| title = The Dahu Graph-Cut for Interactive Segmentation on 2D/3D Images
| journal = Pattern Recognition
| volume = 136
| number = 109207
| abstract = Interactive image segmentation is an important application in computer vision for selecting objects of interest in images. Several interactive segmentation methods are based on distance transform algorithms. However, the most known distance transform, geodesic distance, is sensitive to noise in the image and to seed placement. Recently, the Dahu pseudo-distance, a continuous version of the minimum barrier distance (MBD), is proved to be more powerful than the geodesic distance in noisy and blurred images. This paper presents a method for combining the Dahu pseudo-distance with edge information in a graph-cut optimization framework and leveraging each's complementary strengths. Our method works efficiently on both 2D/3D images and videos. Results show that our method achieves better performance than other distance-based and graph-cut methods, thereby reducing the user's efforts.
| lrdeprojects = Olena
| lrdekeywords = Image
| lrdenewsdate = 2022-12-03
| type = article
| id = movn.22.pr
| identifier = doi:10.1016/j.patcog.2022.109207
| bibtex = 
@Article<nowiki>{</nowiki>	  movn.22.pr,
  author	= <nowiki>{</nowiki>Minh \^On V\~<nowiki>{</nowiki>u<nowiki>}</nowiki> Ng\d<nowiki>{</nowiki>o<nowiki>}</nowiki>c and Edwin Carlinet and Jonathan
		  Fabrizio and Thierry G\'eraud<nowiki>}</nowiki>,
  title		= <nowiki>{</nowiki>The <nowiki>{</nowiki>D<nowiki>}</nowiki>ahu Graph-Cut for Interactive Segmentation on
		  <nowiki>{</nowiki>2D/3D<nowiki>}</nowiki> Images<nowiki>}</nowiki>,
  journal	= <nowiki>{</nowiki>Pattern Recognition<nowiki>}</nowiki>,
  year		= <nowiki>{</nowiki>2023<nowiki>}</nowiki>,
  volume	= <nowiki>{</nowiki>136<nowiki>}</nowiki>,
  number	= <nowiki>{</nowiki>109207<nowiki>}</nowiki>,
  month		= apr,
  abstract	= <nowiki>{</nowiki>Interactive image segmentation is an important application
		  in computer vision for selecting objects of interest in
		  images. Several interactive segmentation methods are based
		  on distance transform algorithms. However, the most known
		  distance transform, geodesic distance, is sensitive to
		  noise in the image and to seed placement. Recently, the
		  Dahu pseudo-distance, a continuous version of the minimum
		  barrier distance (MBD), is proved to be more powerful than
		  the geodesic distance in noisy and blurred images. This
		  paper presents a method for combining the Dahu
		  pseudo-distance with edge information in a graph-cut
		  optimization framework and leveraging each's complementary
		  strengths. Our method works efficiently on both 2D/3D
		  images and videos. Results show that our method achieves
		  better performance than other distance-based and graph-cut
		  methods, thereby reducing the user's efforts.<nowiki>}</nowiki>,
  doi		= <nowiki>{</nowiki>10.1016/j.patcog.2022.109207<nowiki>}</nowiki>
<nowiki>}</nowiki>

}}