13,645 research outputs found
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
Shape Factors for Irregularly-Shaped Matrix Blocks
Imperial Users onl
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
In recent years, with the development of aerospace technology, we use more
and more images captured by satellites to obtain information. But a large
number of useless raw images, limited data storage resource and poor
transmission capability on satellites hinder our use of valuable images.
Therefore, it is necessary to deploy an on-orbit semantic segmentation model to
filter out useless images before data transmission. In this paper, we present a
detailed comparison on the recent deep learning models. Considering the
computing environment of satellites, we compare methods from accuracy,
parameters and resource consumption on the same public dataset. And we also
analyze the relation between them. Based on experimental results, we further
propose a viable on-orbit semantic segmentation strategy. It will be deployed
on the TianZhi-2 satellite which supports deep learning methods and will be
lunched soon.Comment: 8 pages, 3 figures, ICNC-FSKD 201
Remote (Dis)engagement: Shifting Corporate Risk to the 'Bottom of the Pyramid'
Untapped markets are often deemed institutional voids, terra incognita ripe with economic possibility. The conversion of institutional voids into viable markets has become the ambition of many corporations today, which view marginal and under-served areas such as urban slums as opportunities to achieve the dual aims of market growth and poverty reduction, particularly through ‘bottom of the pyramid’ (BoP) programmes. This article examines how firms manage institutional voids and the consequences of these approaches for workers through a case study of a BoP ‘route to market’ programme designed by a global food manufacturer in Kibera, Africa's largest slum, located in Nairobi. Instead of engaging with Kibera by upgrading informal markets or generating formal employment, the corporation focused on harnessing existing informal systems through composite arrangements of NGOs, social networks and informal enterprises, a strategy the authors term ‘remote (dis)engagement’. The article describes the logics and outcome of this strategy of formal engagement with informal markets, concluding that the BoP business model depends on ‘gig practices’ of flexibility, irregular work and insecurity to realize the much-heralded ‘fortune at the bottom of the pyramid’
TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images
In the remote sensing field, Change Detection (CD) aims to identify and
localize the changed regions from dual-phase images over the same places.
Recently, it has achieved great progress with the advances of deep learning.
However, current methods generally deliver incomplete CD regions and irregular
CD boundaries due to the limited representation ability of the extracted visual
features. To relieve these issues, in this work we propose a novel
Transformer-based learning framework named TransY-Net for remote sensing image
CD, which improves the feature extraction from a global view and combines
multi-level visual features in a pyramid manner. More specifically, the
proposed framework first utilizes the advantages of Transformers in long-range
dependency modeling. It can help to learn more discriminative global-level
features and obtain complete CD regions. Then, we introduce a novel pyramid
structure to aggregate multi-level visual features from Transformers for
feature enhancement. The pyramid structure grafted with a Progressive Attention
Module (PAM) can improve the feature representation ability with additional
inter-dependencies through spatial and channel attentions. Finally, to better
train the whole framework, we utilize the deeply-supervised learning with
multiple boundary-aware loss functions. Extensive experiments demonstrate that
our proposed method achieves a new state-of-the-art performance on four optical
and two SAR image CD benchmarks. The source code is released at
https://github.com/Drchip61/TransYNet.Comment: This work is accepted by TGRS2023. It is an extension of our ACCV2022
paper and arXiv:2210.0075
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