5,975 research outputs found
Improving Spatial Codification in Semantic Segmentation
This paper explores novel approaches for improving the spatial codification
for the pooling of local descriptors to solve the semantic segmentation
problem. We propose to partition the image into three regions for each object
to be described: Figure, Border and Ground. This partition aims at minimizing
the influence of the image context on the object description and vice versa by
introducing an intermediate zone around the object contour. Furthermore, we
also propose a richer visual descriptor of the object by applying a Spatial
Pyramid over the Figure region. Two novel Spatial Pyramid configurations are
explored: Cartesian-based and crown-based Spatial Pyramids. We test these
approaches with state-of-the-art techniques and show that they improve the
Figure-Ground based pooling in the Pascal VOC 2011 and 2012 semantic
segmentation challenges.Comment: Paper accepted at the IEEE International Conference on Image
Processing, ICIP 2015. Quebec City, 27-30 September. Project page:
https://imatge.upc.edu/web/publications/improving-spatial-codification-semantic-segmentatio
Contains and Inside relationships within combinatorial Pyramids
Irregular pyramids are made of a stack of successively reduced graphs
embedded in the plane. Such pyramids are used within the segmentation framework
to encode a hierarchy of partitions. The different graph models used within the
irregular pyramid framework encode different types of relationships between
regions. This paper compares different graph models used within the irregular
pyramid framework according to a set of relationships between regions. We also
define a new algorithm based on a pyramid of combinatorial maps which allows to
determine if one region contains the other using only local calculus.Comment: 35 page
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects
- …