Taking into account inclusion and adjacency information in morphological hierarchical representations, with application to the extraction of text in natural images and videos.

Authors

Lê Duy Huỳnh

Place

Paris, France

Type

phdthesis

Projects

Olena

Keywords

Image

Date

2018-12-01

Abstract

The inclusion and adjacency relationship between image regions usually carry contextual information. The later is widely used since it tells how regions are arranged in images. The former is usually not taken into account although it parallels the object-background relationship. The mathematical morphology framework provides several hierarchical image representations. They include the Tree of Shapes (ToS), which encodes the inclusion of level-lineand the hierarchies of segmentation (e.g., alpha-treeBPT), which is useful in the analysis of the adjacency relationship. In this work, we take advantage of both inclusion and adjacency information in these representations for computer vision applications. We introduce the spatial alignment graph w.r.t inclusion that is constructed by adding a new adjacency relationship to nodes of the ToS. In a simple ToS such as our Tree of Shapes of Laplacian sign, which encodes the inclusion of Morphological Laplacian 0-crossings, the graph is reduced to a disconnected graph where each connected component is a semantic group. In other cases, e.g., classic ToS, the spatial alignment graph is more complex. To address this issue, we expand the shape-spaces morphology. Our expansion has two primary results: 1)It allows the manipulation of any graph of shapes. 2)It allows any tree filtering strategy proposed by the connected operators frameworks. With this expansion, the spatial graph could be analyzed with the help of an alpha-tree. We demonstrated the application aspect of our method in the application of text detection. The experiment results show the efficiency and effectiveness of our methods, which is appealing to mobile applications.

Documents

• Paper

• Slides

Bibtex (lrde.bib)

@PhDThesis{	  huynh.18.phd,
  author	= {L\^e Duy {Hu\`ynh}},
  title		= {Taking into account inclusion and adjacency information in
		  morphological hierarchical representations, with
		  application to the extraction of text in natural images and
		  videos.},
  school	= {Sorbonne Universit\'e},
  year		= 2018,
  address	= {Paris, France},
  month		= dec,
  abstract	= {The inclusion and adjacency relationship between image
		  regions usually carry contextual information. The later is
		  widely used since it tells how regions are arranged in
		  images. The former is usually not taken into account
		  although it parallels the object-background relationship.
		  The mathematical morphology framework provides several
		  hierarchical image representations. They include the Tree
		  of Shapes (ToS), which encodes the inclusion of level-line,
		  and the hierarchies of segmentation (e.g., alpha-tree,
		  BPT), which is useful in the analysis of the adjacency
		  relationship. In this work, we take advantage of both
		  inclusion and adjacency information in these
		  representations for computer vision applications. We
		  introduce the spatial alignment graph w.r.t inclusion that
		  is constructed by adding a new adjacency relationship to
		  nodes of the ToS. In a simple ToS such as our Tree of
		  Shapes of Laplacian sign, which encodes the inclusion of
		  Morphological Laplacian 0-crossings, the graph is reduced
		  to a disconnected graph where each connected component is a
		  semantic group. In other cases, e.g., classic ToS, the
		  spatial alignment graph is more complex. To address this
		  issue, we expand the shape-spaces morphology. Our expansion
		  has two primary results: 1)It allows the manipulation of
		  any graph of shapes. 2)It allows any tree filtering
		  strategy proposed by the connected operators frameworks.
		  With this expansion, the spatial graph could be analyzed
		  with the help of an alpha-tree. We demonstrated the
		  application aspect of our method in the application of text
		  detection. The experiment results show the efficiency and
		  effectiveness of our methods, which is appealing to mobile
		  applications.},
  doi		= {FIXME}
}