7,133 research outputs found
Hierarchical morphological segmentation for image sequence coding
This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.Peer ReviewedPostprint (published version
A graph-based mathematical morphology reader
This survey paper aims at providing a "literary" anthology of mathematical
morphology on graphs. It describes in the English language many ideas stemming
from a large number of different papers, hence providing a unified view of an
active and diverse field of research
SuperpixelGraph: Semi-automatic generation of building footprint through semantic-sensitive superpixel and neural graph networks
Most urban applications necessitate building footprints in the form of
concise vector graphics with sharp boundaries rather than pixel-wise raster
images. This need contrasts with the majority of existing methods, which
typically generate over-smoothed footprint polygons. Editing these
automatically produced polygons can be inefficient, if not more time-consuming
than manual digitization. This paper introduces a semi-automatic approach for
building footprint extraction through semantically-sensitive superpixels and
neural graph networks. Drawing inspiration from object-based classification
techniques, we first learn to generate superpixels that are not only
boundary-preserving but also semantically-sensitive. The superpixels respond
exclusively to building boundaries rather than other natural objects, while
simultaneously producing semantic segmentation of the buildings. These
intermediate superpixel representations can be naturally considered as nodes
within a graph. Consequently, graph neural networks are employed to model the
global interactions among all superpixels and enhance the representativeness of
node features for building segmentation. Classical approaches are utilized to
extract and regularize boundaries for the vectorized building footprints.
Utilizing minimal clicks and straightforward strokes, we efficiently accomplish
accurate segmentation outcomes, eliminating the necessity for editing polygon
vertices. Our proposed approach demonstrates superior precision and efficacy,
as validated by experimental assessments on various public benchmark datasets.
A significant improvement of 8% in AP50 was observed in vector graphics
evaluation, surpassing established techniques. Additionally, we have devised an
optimized and sophisticated pipeline for interactive editing, poised to further
augment the overall quality of the results
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