scholarly journals ENHANCEMENT OF STEREO IMAGERY BY ARTIFICIAL TEXTURE PROJECTION GENERATED USING A LIDAR

2016 ◽  
Vol XLI-B5 ◽  
pp. 599-606 ◽  
Author(s):  
Joshua Veitch-Michaelis ◽  
Jan-Peter Muller ◽  
David Walton ◽  
Jonathan Storey ◽  
Michael Foster ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Ground Truth ◽  
Laser Speckle ◽  
Image Texture ◽  
High Signal ◽  
Optical Elements ◽  
Image Texture Analysis ◽  
Ground Truth Data ◽  
Pattern Projection

Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image.

scholarly journals ENHANCEMENT OF STEREO IMAGERY BY ARTIFICIAL TEXTURE PROJECTION GENERATED USING A LIDAR

2016 ◽  
Vol XLI-B5 ◽  
pp. 599-606 ◽  
Author(s):  
Joshua Veitch-Michaelis ◽  
Jan-Peter Muller ◽  
David Walton ◽  
Jonathan Storey ◽  
Michael Foster ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Speckle Pattern ◽  
Ground Truth ◽  
Laser Speckle ◽  
Image Texture ◽  
High Signal ◽  
Optical Elements ◽  
Image Texture Analysis ◽  
Ground Truth Data ◽  
Pattern Projection

Passive stereo imaging is capable of producing dense 3D data, but image matching algorithms generally perform poorly on images with large regions of homogenous texture due to ambiguous match costs. Stereo systems can be augmented with an additional light source that can project some form of unique texture onto surfaces in the scene. Methods include structured light, laser projection through diffractive optical elements, data projectors and laser speckle. Pattern projection using lasers has the advantage of producing images with a high signal to noise ratio. We have investigated the use of a scanning visible-beam LIDAR to simultaneously provide enhanced texture within the scene and to provide additional opportunities for data fusion in unmatched regions. The use of a LIDAR rather than a laser alone allows us to generate highly accurate ground truth data sets by scanning the scene at high resolution. This is necessary for evaluating different pattern projection schemes. Results from LIDAR generated random dots are presented and compared to other texture projection techniques. Finally, we investigate the use of image texture analysis to intelligently project texture where it is required while exploiting the texture available in the ambient light image.

scholarly journals Different methods for characterizing surface roughness using laser speckle technique

2019 ◽  
Vol 12 (23) ◽  
pp. 21-29
Author(s):  
Mayada B. Al-Quzweny
Keyword(s):  
Surface Roughness ◽  
Speckle Pattern ◽  
Point Of View ◽  
Laser Speckle ◽  
Image Texture ◽  
Object Surface ◽  
Image Texture Analysis ◽  
Measuring Surface ◽  
Wide Range

In this work, results from an optical technique (laser speckle technique) for measuring surface roughness was done by using statistical properties of speckle pattern from the point of view of computer image texture analysis. Four calibration relationships were used to cover wide range of measurement with the same laser speckle technique. The first one is based on intensity contrast of the speckle, the second is based on analysis of speckle binary image,  the third is on size of speckle pattern spot, and the latest one is based on characterization of the energy feature of the gray level co-occurrence matrices for the speckle pattern. By these calibration relationships surface roughness of an object surface can be evaluated within these relations ranges from single speckle pattern image which was taken from the surface.

scholarly journals A benchmark case study for seismic event relative location

2020 ◽  
Vol 223 (2) ◽  
pp. 1313-1326
Author(s):  
S J Gibbons ◽  
T Kværna ◽  
T Tiira ◽  
E Kozlovskaya
Keyword(s):  
Time Delay ◽  
Signal To Noise Ratio ◽  
Source Region ◽  
Ground Truth ◽  
Double Difference ◽  
High Signal ◽  
Relative Location ◽  
Data Set ◽  
Velocity Models ◽  
Event Location

Summary ‘Precision seismology’ encompasses a set of methods which use differential measurements of time-delays to estimate the relative locations of earthquakes and explosions. Delay-times estimated from signal correlations often allow far more accurate estimates of one event location relative to another than is possible using classical hypocentre determination techniques. Many different algorithms and software implementations have been developed and different assumptions and procedures can often result in significant variability between different relative event location estimates. We present a Ground Truth (GT) dataset of 55 military surface explosions in northern Finland in 2007 that all took place within 300 m of each other. The explosions were recorded with a high signal-to-noise ratio to distances of about 2°, and the exceptional waveform similarity between the signals from the different explosions allows for accurate correlation-based time-delay measurements. With exact coordinates for the explosions, we are able to assess the fidelity of relative location estimates made using any location algorithm or implementation. Applying double-difference calculations using two different 1-D velocity models for the region results in hypocentre-to-hypocentre distances which are too short and it is clear that the wavefield leaving the source region is more complicated than predicted by the models. Using the GT event coordinates, we are able to measure the slowness vectors associated with each outgoing ray from the source region. We demonstrate that, had such corrections been available, a significant improvement in the relative location estimates would have resulted. In practice we would of course need to solve for event hypocentres and slowness corrections simultaneously, and significant work will be needed to upgrade relative location algorithms to accommodate uncertainty in the form of the outgoing wavefield. We present this data set, together with GT coordinates, raw waveforms for all events on six regional stations, and tables of time-delay measurements, as a reference benchmark by which relative location algorithms and software can be evaluated.

A Seismic Event Relative Location Benchmark Case Study

2020 ◽  
Author(s):  
Tormod Kvaerna ◽  
Steven J. Gibbons ◽  
Timo Tiira ◽  
Elena Kozlovskaya
Keyword(s):  
Time Delay ◽  
Signal To Noise Ratio ◽  
Source Region ◽  
Ground Truth ◽  
Double Difference ◽  
High Signal ◽  
Relative Location ◽  
Hypocenter Determination ◽  
Velocity Models ◽  
Event Location

<p>"Precision seismology'' encompasses a set of methods which use differential measurements of time-delays to estimate the relative locations of earthquakes and explosions.  Delay-times estimated from signal correlations often allow far more accurate estimates of one event location relative to another than is possible using classical hypocenter determination techniques.  Many different algorithms and software implementations have been developed and different assumptions and procedures can often result in significant variability between different relative event location estimates.  We present a Ground Truth (GT) database of 55 military surface explosions in northern Finland in 2007 that all took place within 300 meters of each other.  The explosions were recorded with a high signal-to-noise ratio to distances of about 2 degrees, and the exceptional waveform similarity between the signals from the different explosions allows for accurate correlation-based time-delay measurements.  With exact coordinates for the explosions, we can assess the fidelity of relative location estimates made using any location algorithm or implementation.  Applying double-difference calculations using two different 1-d velocity models for the region results in hypocenter-to-hypocenter distances which are too short and the wavefield leaving the source region is more complicated than predicted by the models.  Using the GT event coordinates, we can measure the slowness vectors associated with each outgoing ray from the source region. We demonstrate that, had such corrections been available, a significant improvement in the relative location estimates would have resulted.  In practice we would of course need to solve for event hypocenters and slowness corrections simultaneously, and significant work will be needed to upgrade relative location algorithms to accommodate uncertainty in the form of the outgoing wavefield.  We present this dataset, together with GT coordinates, raw waveforms for all events on six regional stations, and tables of time-delay measurements, as a reference benchmark by which relative location algorithms and software can be evaluated.</p>

scholarly journals Multi-Frequency, Multi-Sonar Mapping of Shallow Habitats—Efficacy and Management Implications in the National Marine Park of Zakynthos, Greece

2019 ◽  
Vol 11 (4) ◽  
pp. 461 ◽  
Author(s):  
Elias Fakiris ◽  
Philippe Blondel ◽  
George Papatheodorou ◽  
Dimitris Christodoulou ◽  
Xenophon Dimas ◽  
...  
Keyword(s):  
Marine Protected Area ◽  
Ground Truth ◽  
Posidonia Oceanica ◽  
Image Texture ◽  
Sidescan Sonar ◽  
Dual Frequency ◽  
Benthic Habitats ◽  
Ground Truth Data ◽  
Feature Extraction Method ◽  
Marine Park

In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km2 with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats.

Effect of Scan Time on Neuro 18F-Fluorodeoxyglucose Positron Emission Tomography Image Generated Using Deep Learning

2021 ◽  
Vol 11 (3) ◽  
pp. 681-687
Author(s):  
Jaewon Kim ◽  
Sungsik Kang ◽  
Konsu Lee ◽  
Jin Ho Jung ◽  
Garam Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
Signal To Noise Ratio ◽  
Ground Truth ◽  
High Signal ◽  
Ct Scanner ◽  
Signal To Noise ◽  
Scan Time ◽  
Mean Square Errors

The purpose of this study was to generate the PET images with high signal-to-noise ratio (SNR) acquired for typical scan durations (H-PET) from short scan time PET images with low SNR (L-PET) using deep learning and to evaluate the effect of scan time on the quality of predicted PET image. A convolutional neural network (CNN) with a concatenated connection and residual learning framework was implemented. PET data from 27 patients were acquired for 900 s, starting 60 minutes after the intravenous administration of FDG using a commercial PET/CT scanner. To investigate the effect of scan time on the quality of the predicted H-PETs, 10 s, 30 s, 60 s, and 120 s PET data were generated by sorting the 900 s LMF data into the LMF data acquired for each scan time. Twenty-three of the 27 patient images were used for training of the proposed CNN and the remaining four patient images were used for test of the CNN. The predicted H-PETs generated by the CNN were compared to ground-truth H-PETs, L-PETs, and filtered L-PETs processed with four commonly used denoising algorithms. The peak signal-to-noise ratios (PSNRs), normalized root mean square errors (NRMSEs), and average regionof- interest (ROI) differences as a function of scan time were calculated. The quality of the predicted H-PETs generated by the CNN was superior to that of the L-PETs and filtered L-PETs. Lower NRMSEs and higher PSNRs were also obtained from predicted H-PETs compared to the L-PETs and filtered L-PETs. ROI differences in the predicted H-PETs were smaller than those of the L-PETs. The quality of the predicted H-PETs gradually improved with increasing scan times. The lowest NRMSEs, highest PSNRs, and smallest ROI differences were obtained using the predicted H-PETs for 120 s. Various performance test results for the proposed CNN indicate that it is possible to generate H-PETs from neuro FDG L-PETs using the proposed CNN method, which might allow reductions in both scan time and injection dose.

A compact guided-wave EMAT with pulsed electromagnet for ferromagnetic tube inspection

2020 ◽  
Vol 64 (1-4) ◽  
pp. 951-958
Author(s):  
Tianhao Liu ◽  
Yu Jin ◽  
Cuixiang Pei ◽  
Jie Han ◽  
Zhenmao Chen
Keyword(s):  
Power Plants ◽  
Signal To Noise Ratio ◽  
Small Diameter ◽  
Performance Testing ◽  
Guided Wave ◽  
High Signal ◽  
Receiver Coil ◽  
Signal To Noise ◽  
Corrosion Defects

Small-diameter tubes that are widely used in petroleum industries and power plants experience corrosion during long-term services. In this paper, a compact inserted guided-wave EMAT with a pulsed electromagnet is proposed for small-diameter tube inspection. The proposed transducer is noncontact, compact with high signal-to-noise ratio and unattractive to ferromagnetic tubes. The proposed EMAT is designed with coils-only configuration, which consists of a pulsed electromagnet and a meander pulser/receiver coil. Both the numerical simulation and experimental results validate its feasibility on generating and receiving L(0,2) mode guided wave. The parameters for driving the proposed EMAT are optimized by performance testing. Finally, feasibility on quantification evaluation for corrosion defects was verified by experiments.

Application of Novel Low Current OBIRCH Amplifier and Nanoprobing to Identify Subtle Leakages in Advanced Node Technologies

2018 ◽  
Author(s):  
Satish Kodali ◽  
Liangshan Chen ◽  
Yuting Wei ◽  
Tanya Schaeffer ◽  
Chong Khiam Oh
Keyword(s):  
Signal To Noise Ratio ◽  
Semiconductor Industry ◽  
Fault Isolation ◽  
Ring Oscillator ◽  
High Signal ◽  
The Novel ◽  
Resistance Change ◽  
Three Samples ◽  
Relative Threshold ◽  
First Time

Abstract Optical beam induced resistance change (OBIRCH) is a very well-adapted technique for static fault isolation in the semiconductor industry. Novel low current OBIRCH amplifier is used to facilitate safe test condition requirements for advanced nodes. This paper shows the differences between the earlier and novel generation OBIRCH amplifiers. Ring oscillator high standby leakage samples are analyzed using the novel generation amplifier. High signal to noise ratio at applied low bias and current levels on device under test are shown on various samples. Further, a metric to demonstrate the SNR to device performance is also discussed. OBIRCH analysis is performed on all the three samples for nanoprobing of, and physical characterization on, the leakage. The resulting spots were calibrated and classified. It is noted that the calibration metric can be successfully used for the first time to estimate the relative threshold voltage of individual transistors in advanced process nodes.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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