3,542 research outputs found
An image retrieval system based on explicit and implicit feedback on a tablet computer
Our research aims at developing a image retrieval system which uses relevance feedback to build a hybrid search /recommendation system for images according to usersâ inter ests. An image retrieval application running on a tablet computer gathers explicit feedback through the touchscreen but also uses multiple sensing technologies to gather implicit feedback such as emotion and action. A recommendation mechanism driven by collaborative filtering is implemented to verify our interaction design
Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection
In the field of connectomics, neuroscientists seek to identify cortical
connectivity comprehensively. Neuronal boundary detection from the Electron
Microscopy (EM) images is often done to assist the automatic reconstruction of
neuronal circuit. But the segmentation of EM images is a challenging problem,
as it requires the detector to be able to detect both filament-like thin and
blob-like thick membrane, while suppressing the ambiguous intracellular
structure. In this paper, we propose multi-stage multi-recursive-input fully
convolutional networks to address this problem. The multiple recursive inputs
for one stage, i.e., the multiple side outputs with different receptive field
sizes learned from the lower stage, provide multi-scale contextual boundary
information for the consecutive learning. This design is
biologically-plausible, as it likes a human visual system to compare different
possible segmentation solutions to address the ambiguous boundary issue. Our
multi-stage networks are trained end-to-end. It achieves promising results on
two public available EM segmentation datasets, the mouse piriform cortex
dataset and the ISBI 2012 EM dataset.Comment: Accepted by ICCV201
A High-Low Model of Daily Stock Price Ranges
We observe that daily highs and lows of stock prices do not diverge over time and, hence, adopt the cointegration concept and the related vector error correction model (VECM) to model the daily high, the daily low, and the associated daily range data. The in-sample results attest the importance of incorporating high-low interactions in modeling the range variable. In evaluating the out-of-sample forecast performance using both mean-squared forecast error and direction of change criteria, it is found that the VECM-based low and high forecasts offer some advantages over some alternative forecasts. The VECM-based range forecasts, on the other hand, do not always dominate âthe forecast rankings depend on the choice of evaluation criterion and the variables being forecasted.daily high, daily low, VECM model, forecast performance, implied volatility
Monitoring insulator contamination level under dry condition with a microwave reflectometer
âBuild-up of surface contamination on high voltage
insulators can lead to an increase in leakage current and partial
discharge, which may eventually develop into flashover.
Conventional contamination level monitoring systems based on
leakage current, partial discharge, infrared and ultraviolet
camera are only effective when the contamination layer has been
wetted by rain, fog or condensation; under these conditions
flashover might occur before there is time to implement remedial
measures such as cleaning. This paper describes studies exploring
the feasibility of applying microwave reflectometry techniques to
monitor insulator contamination levels. This novel method
measures the power generated by a 10.45 GHz source and
reflected at the insulator contamination layer. A theoretical
model establishes the relationship between equivalent salt deposit
density (ESDD) levels, dielectric properties and geometry of
contamination layers. Experimental results demonstrate that the
output from the reflectometer is able to clearly distinguish
between samples with different contamination levels under dry
conditions. This contamination monitoring method could
potentially provide advance warning of the future failure of wet
insulators in climates where insulators can experience dry
conditions for extended periods
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
Deep neural networks have been widely adopted for automatic organ
segmentation from abdominal CT scans. However, the segmentation accuracy of
some small organs (e.g., the pancreas) is sometimes below satisfaction,
arguably because deep networks are easily disrupted by the complex and variable
background regions which occupies a large fraction of the input volume. In this
paper, we formulate this problem into a fixed-point model which uses a
predicted segmentation mask to shrink the input region. This is motivated by
the fact that a smaller input region often leads to more accurate segmentation.
In the training process, we use the ground-truth annotation to generate
accurate input regions and optimize network weights. On the testing stage, we
fix the network parameters and update the segmentation results in an iterative
manner. We evaluate our approach on the NIH pancreas segmentation dataset, and
outperform the state-of-the-art by more than 4%, measured by the average
Dice-S{\o}rensen Coefficient (DSC). In addition, we report 62.43% DSC in the
worst case, which guarantees the reliability of our approach in clinical
applications.Comment: Accepted to MICCAI 2017 (8 pages, 3 figures
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