41 research outputs found
Accurate depth from defocus estimation with video-rate implementation
The science of measuring depth from images at video rate using âdefocusâ has been investigated. The method required two differently focussed images acquired from a single view point using a single camera. The relative blur between the images was used to determine the in-focus axial points of each pixel and hence depth.
The depth estimation algorithm researched by Watanabe and Nayar was employed to recover the depth estimates, but the broadband filters, referred as the Rational filters were designed using a new procedure: the Two Step Polynomial Approach. The filters designed by the new model were largely insensitive to object texture and were shown to model the blur more precisely than the previous method. Experiments with real planar images demonstrated a maximum RMS depth error of 1.18% for the proposed filters, compared to 1.54% for the previous design.
The researched software program required five 2D convolutions to be processed in parallel and these convolutions were effectively implemented on a FPGA using a two channel, five stage pipelined architecture, however the precision of the filter coefficients and the variables had to be limited within the processor. The number of multipliers required for each convolution was reduced from 49 to 10 (79.5% reduction) using a Triangular design procedure. Experimental results suggested that the pipelined processor provided depth estimates comparable in accuracy to the full precision Matlabâs output, and generated depth maps of size 400 x 400 pixels in 13.06msec, that is faster than the video rate.
The defocused images (near and far-focused) were optically registered for magnification using Telecentric optics. A frequency domain approach based on phase correlation was employed to measure the radial shifts due to magnification and also to optimally position the external aperture. The telecentric optics ensured pixel to pixel registration between the defocused images was correct and provided more accurate depth estimates
Accurate depth from defocus estimation with video-rate implementation
The science of measuring depth from images at video rate using âdefocusâ has been investigated. The method required two differently focussed images acquired from a single view point using a single camera. The relative blur between the images was used to determine the in-focus axial points of each pixel and hence depth. The depth estimation algorithm researched by Watanabe and Nayar was employed to recover the depth estimates, but the broadband filters, referred as the Rational filters were designed using a new procedure: the Two Step Polynomial Approach. The filters designed by the new model were largely insensitive to object texture and were shown to model the blur more precisely than the previous method. Experiments with real planar images demonstrated a maximum RMS depth error of 1.18% for the proposed filters, compared to 1.54% for the previous design. The researched software program required five 2D convolutions to be processed in parallel and these convolutions were effectively implemented on a FPGA using a two channel, five stage pipelined architecture, however the precision of the filter coefficients and the variables had to be limited within the processor. The number of multipliers required for each convolution was reduced from 49 to 10 (79.5% reduction) using a Triangular design procedure. Experimental results suggested that the pipelined processor provided depth estimates comparable in accuracy to the full precision Matlabâs output, and generated depth maps of size 400 x 400 pixels in 13.06msec, that is faster than the video rate. The defocused images (near and far-focused) were optically registered for magnification using Telecentric optics. A frequency domain approach based on phase correlation was employed to measure the radial shifts due to magnification and also to optimally position the external aperture. The telecentric optics ensured pixel to pixel registration between the defocused images was correct and provided more accurate depth estimates.EThOS - Electronic Theses Online ServiceUniversity of Warwick (UoW)GBUnited Kingdo
Formulation of Pattern Recognition Framework - Analysis and Detection of Tyre Cracks Utilizing Integrated Texture Features and Ensemble Learning Methods
For a safe drive with a vehicle and better
tyre life, it is important to regularly monitor the tyre
damages to diagnose its condition and chose appropri-
ate solution. This paper proposes a framework based
on pattern recognition utilizing the strength of texture
attributes and ensemble learning to detect the damages
on the tyre surfaces. In this paper, a concatenation of
the statistical and edge response based texture features
derived from Gray Level Co-occurrence Matrix and
Local directional pattern are proposed to describe
and represent the tyre surface characteristics and their
variations due to any damages. The derived fea-
tures are provided to train machine learning algorithms
using ensemble learning methods for a better under-
standing to discriminate the tyre surfaces into normal
or damaged. The experiments of tyre surface classifica-
tion were conducted on the tyre surface images acquired
from Kaggle tyre dataset. The results demonstrated the
ability of the combined texture features and ensemble
learning methods in effectively analysing the tyre sur-
faces and discriminate them with better performance
provided by adaboost and histogram gradient boosting
methods
Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks
Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on edge optimization is proposed. The experimental results show that the clarity of the output image, the maintenance of facial features, and the color processing of the image are enhanced best by the image translation model based on the generative adversarial network. Finally, the results are compared with other existing methods. Analyzing the experimental results shows that the color face image generated by our method is closer to the target image, and has achieved a better performance in term of Structural Similarity (SSIM)
SearchMorph:Multi-scale Correlation Iterative Network for Deformable Registration
Deformable image registration can obtain dynamic information about images,
which is of great significance in medical image analysis. The unsupervised deep
learning registration method can quickly achieve high registration accuracy
without labels. However, these methods generally suffer from uncorrelated
features, poor ability to register large deformations and details, and
unnatural deformation fields. To address the issues above, we propose an
unsupervised multi-scale correlation iterative registration network
(SearchMorph). In the proposed network, we introduce a correlation layer to
strengthen the relevance between features and construct a correlation pyramid
to provide multi-scale relevance information for the network. We also design a
deformation field iterator, which improves the ability of the model to register
details and large deformations through the search module and GRU while ensuring
that the deformation field is realistic. We use single-temporal brain MR images
and multi-temporal echocardiographic sequences to evaluate the model's ability
to register large deformations and details. The experimental results
demonstrate that the method in this paper achieves the highest registration
accuracy and the lowest folding point ratio using a short elapsed time to
state-of-the-art
FPGA-based systolic deconvolution architecture for upsampling
A deconvolution accelerator is proposed to upsample n Ă n input to 2n Ă 2n output by convolving with a k Ă k kernel. Its architecture avoids the need for insertion and padding of zeros and thus eliminates the redundant computations to achieve high resource efficiency with reduced number of multipliers and adders. The architecture is systolic and governed by a reference clock, enabling the sequential placement of the module to represent a pipelined decoder framework. The proposed accelerator is implemented on a Xilinx XC7Z020 platform, and achieves a performance of 3.641 giga operations per second (GOPS) with resource efficiency of 0.135 GOPS/DSP for upsampling 32 Ă 32 input to 256 Ă 256 output using a 3 Ă 3 kernel at 200 MHz. Furthermore, its high peak signal to noise ratio of almost 80 dB illustrates that the upsampled outputs of the bit truncated accelerator are comparable to IEEE double precision results
Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19
IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19.
Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19.
DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 nonâcritically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022).
INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (nâ=â257), ARB (nâ=â248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; nâ=â10), or no RAS inhibitor (control; nâ=â264) for up to 10 days.
MAIN OUTCOMES AND MEASURES The primary outcome was organ supportâfree days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes.
RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ supportâfree days among critically ill patients was 10 (â1 to 16) in the ACE inhibitor group (nâ=â231), 8 (â1 to 17) in the ARB group (nâ=â217), and 12 (0 to 17) in the control group (nâ=â231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ supportâfree days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively).
CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes.
TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570
Rational filter design for depth from defocus
The paper describes a new, simple procedure to determine the rational filters that are used in the depth from defocus (DfD) procedure previously researched by Watanabe and Nayar [4]. Their DfD uses two differently defocused images and the filters accurately model the relative defocus in the images and provide a fast calculation of distance. This paper presents a simple method to determine the filter coefficients by separating the M/P ratio into a linear and a cubic error correction model. The method avoids the previous iterative minimisation technique and computes efficiently. The model has been verified by comparison with the theoretical M/P ratio. The proposed filters have been compared with the previous for frequency response, closeness of fit to M/P, rotational symmetry, and measurement accuracy. Experiments were performed for several defocus conditions. It was observed that the new filters were largely insensitive to object texture and modelled the blur more precisely than the previous. Experiments with real planar images demonstrated a maximum RMS depth error of 1.18% for the proposed, compared to 1.54% for the previous filters. Complicated objects were also accurately measured