160,778 research outputs found
Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing
Automatic defect detection for 3D printing processes, which shares many
characteristics with change detection problems, is a vital step for quality
control of 3D printed products. However, there are some critical challenges in
the current state of practice. First, existing methods for computer
vision-based process monitoring typically work well only under specific camera
viewpoints and lighting situations, requiring expensive pre-processing,
alignment, and camera setups. Second, many defect detection techniques are
specific to pre-defined defect patterns and/or print schematics. In this work,
we approach the defect detection problem using a novel Semi-Siamese deep
learning model that directly compares a reference schematic of the desired
print and a camera image of the achieved print. The model then solves an image
segmentation problem, precisely identifying the locations of defects of
different types with respect to the reference schematic. Our model is designed
to enable comparison of heterogeneous images from different domains while being
robust against perturbations in the imaging setup such as different camera
angles and illumination. Crucially, we show that our simple architecture, which
is easy to pre-train for enhanced performance on new datasets, outperforms more
complex state-of-the-art approaches based on generative adversarial networks
and transformers. Using our model, defect localization predictions can be made
in less than half a second per layer using a standard MacBook Pro while
achieving an F1-score of more than 0.9, demonstrating the efficacy of using our
method for in-situ defect detection in 3D printing
Structure of Heterogeneous Networks
Heterogeneous networks play a key role in the evolution of communities and
the decisions individuals make. These networks link different types of
entities, for example, people and the events they attend. Network analysis
algorithms usually project such networks unto simple graphs composed of
entities of a single type. In the process, they conflate relations between
entities of different types and loose important structural information. We
develop a mathematical framework that can be used to compactly represent and
analyze heterogeneous networks that combine multiple entity and link types. We
generalize Bonacich centrality, which measures connectivity between nodes by
the number of paths between them, to heterogeneous networks and use this
measure to study network structure. Specifically, we extend the popular
modularity-maximization method for community detection to use this centrality
metric. We also rank nodes based on their connectivity to other nodes. One
advantage of this centrality metric is that it has a tunable parameter we can
use to set the length scale of interactions. By studying how rankings change
with this parameter allows us to identify important nodes in the network. We
apply the proposed method to analyze the structure of several heterogeneous
networks. We show that exploiting additional sources of evidence corresponding
to links between, as well as among, different entity types yields new insights
into network structure
Local wavelet features for statistical object classification and localisation
This article presents a system for texture-based
probabilistic classification and localisation of 3D objects in 2D digital images and discusses selected applications. The objects are described by local feature vectors computed using the wavelet transform. In the training phase, object features are statistically modelled as normal density functions. In the recognition phase, a maximisation algorithm compares the learned density functions
with the feature vectors extracted from a real scene and yields the classes and poses of objects found in it. Experiments carried out on a real dataset of over 40000 images demonstrate the robustness of the system in terms of classification and localisation accuracy. Finally, two important application scenarios are discussed, namely classification of museum artefacts and classification of
metallography images
Nonparametric Edge Detection in Speckled Imagery
We address the issue of edge detection in Synthetic Aperture Radar imagery.
In particular, we propose nonparametric methods for edge detection, and
numerically compare them to an alternative method that has been recently
proposed in the literature. Our results show that some of the proposed methods
display superior results and are computationally simpler than the existing
method. An application to real (not simulated) data is presented and discussed.Comment: Accepted for publication in Mathematics and Computers in Simulatio
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of Dempster–Shafer evidence theory
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
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A lab-on-a-disc platform enables serial monitoring of individual CTCs associated with tumor progression during EGFR-targeted therapy for patients with NSCLC
Rationale: Unlike traditional biopsy, liquid biopsy, which is a largely non-invasive diagnostic and monitoring tool, can be performed more frequently to better track tumors and mutations over time and to validate the efficiency of a cancer treatment. Circulating tumor cells (CTCs) are considered promising liquid biopsy biomarkers; however, their use in clinical settings is limited by high costs and a low throughput of standard platforms for CTC enumeration and analysis. In this study, we used a label-free, high-throughput method for CTC isolation directly from whole blood of patients using a standalone, clinical setting-friendly platform. Methods: A CTC-based liquid biopsy approach was used to examine the efficacy of therapy and emergent drug resistance via longitudinal monitoring of CTC counts, DNA mutations, and single-cell-level gene expression in a prospective cohort of 40 patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer. Results: The change ratio of the CTC counts was associated with tumor response, detected by CT scan, while the baseline CTC counts did not show association with progression-free survival or overall survival. We achieved a 100% concordance rate for the detection of EGFR mutation, including emergence of T790M, between tumor tissue and CTCs. More importantly, our data revealed the importance of the analysis of the epithelial/mesenchymal signature of individual pretreatment CTCs to predict drug responsiveness in patients. Conclusion: The fluid-assisted separation technology disc platform enables serial monitoring of CTC counts, DNA mutations, as well as unbiased molecular characterization of individual CTCs associated with tumor progression during targeted therapy
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
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