40,543 research outputs found
Image operator learning coupled with CNN classification and its application to staff line removal
Many image transformations can be modeled by image operators that are
characterized by pixel-wise local functions defined on a finite support window.
In image operator learning, these functions are estimated from training data
using machine learning techniques. Input size is usually a critical issue when
using learning algorithms, and it limits the size of practicable windows. We
propose the use of convolutional neural networks (CNNs) to overcome this
limitation. The problem of removing staff-lines in music score images is chosen
to evaluate the effects of window and convolutional mask sizes on the learned
image operator performance. Results show that the CNN based solution
outperforms previous ones obtained using conventional learning algorithms or
heuristic algorithms, indicating the potential of CNNs as base classifiers in
image operator learning. The implementations will be made available on the
TRIOSlib project site.Comment: To appear in ICDAR 201
Recommended from our members
Spectral filtering as a method of visualising and removing striped artefacts in digital elevation data
Spectral filtering was compared with traditional mean spatial filters to assess their ability to identify and remove striped artefacts in digital elevation data. The techniques were applied to two datasets: a 100 m contour derived digital elevation model (DEM) of southern Norway and a 2 m LiDAR DSM of the Lake District, UK. Both datasets contained diagonal data artefacts that were found to propagate into subsequent terrain analysis. Spectral filtering used fast Fourier transformation (FFT) frequency data to identify these data artefacts in both datasets. These were removed from the data by applying a cut filter, prior to the inverse transform. Spectral filtering showed considerable advantages over mean spatial filters, when both the absolute and spatial distribution of elevation changes made were examined. Elevation changes from the spectral filtering were restricted to frequencies removed by the cut filter, were small in magnitude and consequently avoided any global smoothing. Spectral filtering was found to avoid the smoothing of kernel based data editing, and provided a more informative measure of data artefacts present in the FFT frequency domain. Artefacts were found to be heterogeneous through the surfaces, a result of their strong correlations with spatially autocorrelated variables: landcover and landsurface geometry. Spectral filtering performed better on the 100 m DEM, where signal and artefact were clearly distinguishable in the frequency data. Spectrally filtered digital elevation datasets were found to provide a superior and more precise representation of the landsurface and be a more appropriate dataset for any subsequent geomorphological applications
Healthcare Robotics
Robots have the potential to be a game changer in healthcare: improving
health and well-being, filling care gaps, supporting care givers, and aiding
health care workers. However, before robots are able to be widely deployed, it
is crucial that both the research and industrial communities work together to
establish a strong evidence-base for healthcare robotics, and surmount likely
adoption barriers. This article presents a broad contextualization of robots in
healthcare by identifying key stakeholders, care settings, and tasks; reviewing
recent advances in healthcare robotics; and outlining major challenges and
opportunities to their adoption.Comment: 8 pages, Communications of the ACM, 201
Evaluation of a Phosphate Management Protocol to Achieve Optimum Serum Phosphate Levels in Hemodialysis Patients
Original article can be found at: http://www.sciencedirect.com/science/journal/10512276 Copyright National Kidney Foundation, Inc. DOI: 10.1053/j.jrn.2008.05.003To evaluate the effectiveness of a protocol designed to optimize serum phosphate levels in patients undergoing regular hemodialysis (HD).Peer reviewe
International Veterinary Epilepsy Task Force recommendations for systematic sampling and processing of brains from epileptic dogs and cats
Traditionally, histological investigations of the epileptic brain are required to identify epileptogenic brain lesions, to evaluate the impact of seizure activity, to search for mechanisms of drug-resistance and to look for comorbidities. For many instances, however, neuropathological studies fail to add substantial data on patients with complete clinical work-up. This may be due to sparse training in epilepsy pathology and or due to lack of neuropathological guidelines for companion animals.
The protocols introduced herein shall facilitate systematic sampling and processing of epileptic brains and therefore increase the efficacy, reliability and reproducibility of morphological studies in animals suffering from seizures.
Brain dissection protocols of two neuropathological centres with research focus in epilepsy have been optimised with regards to their diagnostic yield and accuracy, their practicability and their feasibility concerning clinical research requirements.
The recommended guidelines allow for easy, standardised and ubiquitous collection of brain regions, relevant for seizure generation. Tissues harvested the prescribed way will increase the diagnostic efficacy and provide reliable material for scientific investigations
A comparative study of image processing thresholding algorithms on residual oxide scale detection in stainless steel production lines
The present work is intended for residual oxide scale detection and classification through the application of image processing
techniques. This is a defect that can remain in the surface of stainless steel coils after an incomplete pickling process in a
production line. From a previous detailed study over reflectance of residual oxide defect, we present a comparative study of
algorithms for image segmentation based on thresholding methods. In particular, two computational models based on multi-linear
regression and neural networks will be proposed. A system based on conventional area camera with a special lighting was
installed and fully integrated in an annealing and pickling line for model testing purposes. Finally, model approaches will be
compared and evaluated their performance..Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by
characterizing both local smoothness and nonlocal self-similarity of natural
images in a unified statistical manner. The main contributions are three-folds.
First, from the perspective of image statistics, a joint statistical modeling
(JSM) in an adaptive hybrid space-transform domain is established, which offers
a powerful mechanism of combining local smoothness and nonlocal self-similarity
simultaneously to ensure a more reliable and robust estimation. Second, a new
form of minimization functional for solving image inverse problem is formulated
using JSM under regularization-based framework. Finally, in order to make JSM
tractable and robust, a new Split-Bregman based algorithm is developed to
efficiently solve the above severely underdetermined inverse problem associated
with theoretical proof of convergence. Extensive experiments on image
inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise
removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions
on Circuits System and Video Technology (TCSVT). High resolution pdf version
and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM
- …