1,021,307 research outputs found
An advanced level set method based on Bregman divergence for inhomogeneous image segmentation
© 2017 IEEE. Intensity inhomogeneity often occurs in real images. Local information based level set methods are comparatively effective in segmenting image with inhomogeneous intensity. However, in practice, these models suffer from local minima and high computational cost. In this paper, a novel region-based level set method based on Bregman divergence and local binary fitting, hereafter referred to as Bregman-LBF, is proposed for image segmentation. The proposed method utilizes global and local information to formulate a new energy function. The Bregman-LBF model enjoys the following advantages: (1) Bregman-LBF outperforms the piece-wise constant(PC) model in handling intensity inhomogeneity. (2) Bregman-LBF is more effective than the local binary fitting (LBF) model and more robust than the global and local intensity fitting (GLIF) model. The relationship between the Bregman-LBF model and the existing models, e.g. the Chan-Vese(CV) model, is discussed. The experiments conducted on synthetic and benchmark image datasets have shown that the proposed Bregman-LBF outperforms the piece-wise constant (PC) model in handling intensity inhomogeneity. The experimental results have also shown that the Bregman-LBF is more effective than the local binary fitting (LBF) model and more robust than the global and local intensity fitting (GLIF) model
Active Contour Model for Image Segmentation with Dilated Convolution Filter
ACMs have been demonstrated to be highly suitable as image segmentation models for computer vision tasks. Among other ACM, the local region-based models show better performance because they extract the local information regarding intensity in the neighborhood and embed it into the energy minimization function to guide the active contour to the boundary of the desired object. However, the online segmentation of noisy and inhomogeneous is still a challenging task for local region-based ACM models. To overcome this challenge, the paper proposes a novel region-based active contour model, named active contour model with local dilated convolution filter (ACLD). The ACLD integrates local image information in the form of a signed pressure force function. Then, a Gaussian kernel is applied using dilated convolution instead of discrete convolution for regularizing the level set formulation. Finally, instead of using a constant stopping condition, the ACLD automatically stops at the object boundaries. The proposed model shows improved image segmentation results visually combined with less computational time in the case of synthetic and natural images compared with the state-of-the-art models. Further, on the ISIC2017 dataset, the ACLD yields segmentation results with the highest accuracy. </p
Federated Inference With Reliable Uncertainty Quantification Over Wireless Channels via Conformal Prediction
In this paper, we consider a wireless federated inference scenario in which devices and a server share a pre-trained machine learning model. The devices communicate statistical information about their local data to the server over a common wireless channel, aiming to enhance the quality of the inference decision at the server. Recent work has introduced federated conformal prediction (CP), which leverages devices-to-server communication to improve the reliability of the server's decision. With federated CP, devices communicate to the server information about the loss accrued by the shared pre-trained model on the local data, and the server leverages this information to calibrate a decision interval , or set , so that it is guaranteed to contain the correct answer with a pre-defined target reliability level. Previous work assumed noise-free communication, whereby devices can communicate a single real number to the server. In this paper, we study for the first time federated CP in a wireless setting. We introduce a novel protocol, termed wireless federated conformal prediction (WFCP), which builds on type-based multiple access (TBMA) and on a novel quantile correction strategy. WFCP is proved to provide formal reliability guarantees in terms of coverage of the predicted set produced by the server. Using numerical results, we demonstrate the significant advantages of WFCP against digital implementations of existing federated CP schemes, especially in regimes with limited communication resources and/or large number of devices
Sparse Spatial Transformers for Few-Shot Learning
Learning from limited data is a challenging task since the scarcity of data
leads to a poor generalization of the trained model. The classical global
pooled representation is likely to lose useful local information. Recently,
many few shot learning methods address this challenge by using deep descriptors
and learning a pixel-level metric. However, using deep descriptors as feature
representations may lose the contextual information of the image. And most of
these methods deal with each class in the support set independently, which
cannot sufficiently utilize discriminative information and task-specific
embeddings. In this paper, we propose a novel Transformer based neural network
architecture called Sparse Spatial Transformers (SSFormers), which can find
task-relevant features and suppress task-irrelevant features. Specifically, we
first divide each input image into several image patches of different sizes to
obtain dense local features. These features retain contextual information while
expressing local information. Then, a sparse spatial transformer layer is
proposed to find spatial correspondence between the query image and the entire
support set to select task-relevant image patches and suppress task-irrelevant
image patches. Finally, we propose to use an image patch matching module for
calculating the distance between dense local representations, thus to determine
which category the query image belongs to in the support set. Extensive
experiments on popular few-shot learning benchmarks show that our method
achieves the state-of-the-art performance
A Bayesian Spatio-Temporal Extension to Poisson Auto-Regression: Modeling the Disease Infection Rate of COVID-19 in England
The COVID-19 pandemic provided many modeling challenges to investigate the
evolution of an epidemic process over areal units. A suitable encompassing
model must describe the spatio-temporal variations of the disease infection
rate of multiple areal processes while adjusting for local and global inputs.
We develop an extension to Poisson Auto-Regression that incorporates
spatio-temporal dependence to characterize the local dynamics while borrowing
information among adjacent areas. The specification includes up to two sets of
space-time random effects to capture the spatio-temporal dependence and a
linear predictor depending on an arbitrary set of covariates. The proposed
model, adopted in a fully Bayesian framework and implemented through a novel
sparse-matrix representation in Stan, provides a framework for evaluating local
policy changes over the whole spatial and temporal domain of the study. It has
been validated through a substantial simulation study and applied to the weekly
COVID-19 cases observed in the English local authority districts between May
2020 and March 2021. The model detects substantial spatial and temporal
heterogeneity and allows a full evaluation of the impact of two alternative
sets of covariates: the level of local restrictions in place and the value of
the Google Mobility Indices. The paper also formalizes various novel
model-based investigation methods for assessing additional aspects of disease
epidemiology.Comment: 24 pages + supplementary, 8 figures, 12 table
A hybrid active contour segmentation method for myocardial D-SPECT images
Ischaemic heart disease has become one of the leading causes of mortality worldwide. Dynamic single-photon emission computed tomography (D-SPECT) is an advanced routine diagnostic tool commonly used to validate myocardial function in patients suffering from various heart diseases. Accurate automatic localization and segmentation of myocardial regions is helpful in creating a three-dimensional myocardial model and assisting clinicians to perform assessments of myocardial function. Thus, image segmentation is a key technology in preclinical cardiac studies. Intensity inhomogeneity is one of the common challenges in image segmentation and is caused by image artefacts and instrument inaccuracy. In this paper, a novel region-based active contour model that can segment the myocardial D-SPECT image accurately is presented. First, a local region-based fitting image is defined based on information related to the intensity. Second, a likelihood fitting image energy function is built in a local region around each point in a given vector-valued image. Next, the level set method is used to present a global energy function with respect to the neighbourhood centre. The proposed approach guarantees precision and computational efficiency by combining the region-scalable fitting energy (RSF) model and local image fitting energy (LIF) model, and it can solve the issue of high sensitivity to initialization for myocardial D-SPECT segmentation
Flow-based Autoregressive Structured Prediction of Human Motion
A new method is proposed for human motion predition by learning temporal and
spatial dependencies in an end-to-end deep neural network. The joint
connectivity is explicitly modeled using a novel autoregressive structured
prediction representation based on flow-based generative models. We learn a
latent space of complex body poses in consecutive frames which is conditioned
on the high-dimensional structure input sequence. To construct each latent
variable, the general and local smoothness of the joint positions are
considered in a generative process using conditional normalizing flows. As a
result, all frame-level and joint-level continuities in the sequence are
preserved in the model. This enables us to parameterize the inter-frame and
intra-frame relationships and joint connectivity for robust long-term
predictions as well as short-term prediction. Our experiments on two
challenging benchmark datasets of Human3.6M and AMASS demonstrate that our
proposed method is able to effectively model the sequence information for
motion prediction and outperform other techniques in 42 of the 48 total
experiment scenarios to set a new state-of-the-art
- …