1,373 research outputs found
Generalizing Deep Models for Overhead Image Segmentation Through Getis-Ord Gi* Pooling
That most deep learning models are purely data driven is both a strength and a weakness. Given sufficient training data, the optimal model for a particular problem can be learned. However, this is usually not the case and so instead the model is either learned from scratch from a limited amount of training data or pre-trained on a different problem and then fine-tuned. Both of these situations are potentially suboptimal and limit the generalizability of the model. Inspired by this, we investigate methods to inform or guide deep learning models for geospatial image analysis to increase their performance when a limited amount of training data is available or when they are applied to scenarios other than which they were trained on. In particular, we exploit the fact that there are certain fundamental rules as to how things are distributed on the surface of the Earth and these rules do not vary substantially between locations. Based on this, we develop a novel feature pooling method for convolutional neural networks using Getis-Ord Gi* analysis from geostatistics. Experimental results show our proposed pooling function has significantly better generalization performance compared to a standard data-driven approach when applied to overhead image segmentation
Feature Tracking Cardiac Magnetic Resonance via Deep Learning and Spline Optimization
Feature tracking Cardiac Magnetic Resonance (CMR) has recently emerged as an
area of interest for quantification of regional cardiac function from balanced,
steady state free precession (SSFP) cine sequences. However, currently
available techniques lack full automation, limiting reproducibility. We propose
a fully automated technique whereby a CMR image sequence is first segmented
with a deep, fully convolutional neural network (CNN) architecture, and
quadratic basis splines are fitted simultaneously across all cardiac frames
using least squares optimization. Experiments are performed using data from 42
patients with hypertrophic cardiomyopathy (HCM) and 21 healthy control
subjects. In terms of segmentation, we compared state-of-the-art CNN
frameworks, U-Net and dilated convolution architectures, with and without
temporal context, using cross validation with three folds. Performance relative
to expert manual segmentation was similar across all networks: pixel accuracy
was ~97%, intersection-over-union (IoU) across all classes was ~87%, and IoU
across foreground classes only was ~85%. Endocardial left ventricular
circumferential strain calculated from the proposed pipeline was significantly
different in control and disease subjects (-25.3% vs -29.1%, p = 0.006), in
agreement with the current clinical literature.Comment: Accepted to Functional Imaging and Modeling of the Heart (FIMH) 201
Segmentation-guided privacy preservation in visual surveillance monitoring
Treballs Finals de Grau d'Enginyeria Informà tica, Facultat de Matemà tiques, Universitat de Barcelona, Any: 2022, Director: Sergio Escalera Guerrero, Zenjie Li i Kamal Nasrollahi[en] Video surveillance has become a necessity to ensure safety and security. Today, with the advancement
of technology, video surveillance has become more accessible and widely available. Furthermore, it can be useful
in an enormous amount of applications and situations. For instance, it can be useful in ensuring public safety by
preventing vandalism, robbery, and shoplifting. The same applies to more intimate situations, like home monitoring to detect unusual behavior of residents or in similar situations like hospitals and assisted living facilities. Thus, cameras are installed in public places like malls, metro stations, and on-roads for traffic control, as well as in sensitive settings like hospitals, embassies, and private homes. Video surveillance has always been as-
sociated with the loss of privacy. Therefore, we developed a real-time visualization of privacy-protected video
surveillance data by applying a segmentation mask to protect privacy while still being able to identify existing
risk behaviors. This replaces existing privacy safeguards such as blanking, masking, pixelation, blurring, and
scrambling. As we want to protect human personal data that are visual such as appearance, physical information, clothing, skin, eye and hair color, and facial gestures. Our main aim of this work is to analyze and compare the most successful deep-learning-based state-of-the-art approaches for semantic segmentation. In this study, we
perform an efficiency-accuracy comparison to determine which segmentation methods yield accurate segmentation results while performing at the speed and execution required for real-life application scenarios. Furthermore, we also provide a modified dataset made from a combination of three existing datasets, COCO_stuff164K, PASCAL VOC 2012, and ADE20K, to make our comparison fair and generate privacyprotecting human segmentation masks
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images
Complex scenario of ultrasound image, in which adjacent tissues (i.e.,
background) share similar intensity with and even contain richer texture
patterns than lesion region (i.e., foreground), brings a unique challenge for
accurate lesion segmentation. This work presents a decomposition-coupling
network, called DC-Net, to deal with this challenge in a
(foreground-background) saliency map disentanglement-fusion manner. The DC-Net
consists of decomposition and coupling subnets, and the former preliminarily
disentangles original image into foreground and background saliency maps,
followed by the latter for accurate segmentation under the assistance of
saliency prior fusion. The coupling subnet involves three aspects of fusion
strategies, including: 1) regional feature aggregation (via differentiable
context pooling operator in the encoder) to adaptively preserve local
contextual details with the larger receptive field during dimension reduction;
2) relation-aware representation fusion (via cross-correlation fusion module in
the decoder) to efficiently fuse low-level visual characteristics and
high-level semantic features during resolution restoration; 3) dependency-aware
prior incorporation (via coupler) to reinforce foreground-salient
representation with the complementary information derived from background
representation. Furthermore, a harmonic loss function is introduced to
encourage the network to focus more attention on low-confidence and hard
samples. The proposed method is evaluated on two ultrasound lesion segmentation
tasks, which demonstrates the remarkable performance improvement over existing
state-of-the-art methods.Comment: 18 pages, 18 figure
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