scholarly journals Co-axial depth map sensor with an extended depth range

2019 ◽  
Author(s):  
Mohan Xu ◽  
Hong Hua
Keyword(s):  
Depth Map ◽  
Depth Range ◽  
Axial Depth

scholarly journals Guided, Fusion-Based, Large Depth-of-field 3D Imaging Using a Focal Stack

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4845 ◽  
Author(s):  
Yuhao Xiao ◽  
Guijin Wang ◽  
Xiaowei Hu ◽  
Chenbo Shi ◽  
Long Meng ◽  
...  
Keyword(s):  
3D Imaging ◽  
Depth Map ◽  
Shape Measurement ◽  
Depth Range ◽  
High Quality ◽  
Large Depth ◽  
3D Shape ◽  
3D Shape Measurement ◽  
Working Volume ◽  
Focal Stack

Three dimensional (3D) imaging technology has been widely used for many applications, such as human–computer interactions, making industrial measurements, and dealing with cultural relics. However, existing active methods often require both large apertures of projector and camera to maximize light throughput, resulting in a shallow working volume in which projector and camera are simultaneously in focus. In this paper, we propose a novel method to extend the working range of the structured light 3D imaging system based on the focal stack. Specifically in the case of large depth variation scenes, we first adopted the gray code method for local, 3D shape measurement with multiple focal distance settings. Then we extracted the texture map of each focus position into a focal stack to generate a global coarse depth map. Under the guidance of the global coarse depth map, the high-quality 3D shape measurement of the overall scene was obtained by local, 3D shape-measurement fusion. To validate the method, we developed a prototype system that can perform high-quality measurements in the depth range of 400 mm with a measurement error of 0.08%.

scholarly journals Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4434 ◽  
Author(s):  
Sangwon Kim ◽  
Jaeyeal Nam ◽  
Byoungchul Ko
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Fundamental Problem ◽  
Depth Map ◽  
Ground Truth ◽  
Depth Estimation ◽  
Depth Range ◽  
Single Image ◽  
Special Equipment ◽  
Multiple Images

Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive. Therefore, software-based methods for estimating depth from a single image using machine learning or deep learning are emerging as new alternatives. In this paper, we propose an algorithm that generates a depth map in real time using a single image and an optimized lightweight efficient neural network (L-ENet) algorithm instead of physical equipment, such as an infrared sensor or multi-view camera. Because depth values have a continuous nature and can produce locally ambiguous results, pixel-wise prediction with ordinal depth range classification was applied in this study. In addition, in our method various convolution techniques are applied to extract a dense feature map, and the number of parameters is greatly reduced by reducing the network layer. By using the proposed L-ENet algorithm, an accurate depth map can be generated from a single image quickly and, in a comparison with the ground truth, we can produce depth values closer to those of the ground truth with small errors. Experiments confirmed that the proposed L-ENet can achieve a significantly improved estimation performance over the state-of-the-art algorithms in depth estimation based on a single image.

4 Pattern Recognition for Healing Burns to Compute Evidence: Space Syntax and Machine Learning Analysis of Burns Center Karachi

2013 ◽  
Vol 1 (1) ◽  
pp. 13
Author(s):  
Javaria Manzoor Shaikh ◽  
JaeSeung Park
Keyword(s):  
Pattern Recognition ◽  
Prediction Models ◽  
Uv Light ◽  
Healing Process ◽  
Space Syntax ◽  
Depth Map ◽  
Routine Tasks ◽  
Hyper Plane ◽  
First Time ◽  
Learning Analysis

Usually elongated hospitalization is experienced byBurn patients, and the precise forecast of the placement of patientaccording to the healing acceleration has significant consequenceon healthcare supply administration. Substantial amount ofevidence suggest that sun light is essential to burns healing andcould be exceptionally beneficial for burned patients andworkforce in healthcare building. Satisfactory UV sunlight isfundamental for a calculated amount of burn to heal; this delicaterather complex matrix is achieved by applying patternclassification for the first time on the space syntax map of the floorplan and Browder chart of the burned patient. On the basis of thedata determined from this specific healthcare learning technique,nurse can decide the location of the patient on the floor plan, hencepatient safety first is the priority in the routine tasks by staff inhealthcare settings. Whereas insufficient UV light and vitamin Dcan retard healing process, hence this experiment focuses onmachine learning design in which pattern recognition andtechnology supports patient safety as our primary goal. In thisexperiment we lowered the adverse events from 2012- 2013, andnearly missed errors and prevented medical deaths up to 50%lower, as compared to the data of 2005- 2012 before this techniquewas incorporated.In this research paper, three distinctive phases of clinicalsituations are considered—primarily: admission, secondly: acute,and tertiary: post-treatment according to the burn pattern andhealing rate—and be validated by capable AI- origin forecastingtechniques to hypothesis placement prediction models for eachclinical stage with varying percentage of burn i.e. superficialwound, partial thickness or full thickness deep burn. Conclusivelywe proved that the depth of burn is directly proportionate to thedepth of patient’s placement in terms of window distance. Ourfindings support the hypothesis that the windowed wall is mosthealing wall, here fundamental suggestion is support vectormachines: which is most advantageous hyper plane for linearlydivisible patterns for the burns depth as well as the depth map isused.

Anisotropy of Nanoindentation – a tool for the determination of nanocrystal orientation ?

2003 ◽  
Vol 779 ◽  
Author(s):  
David Christopher ◽  
Steven Kenny ◽  
Roger Smith ◽  
Asta Richter ◽  
Bodo Wolf ◽  
...  
Keyword(s):  
Crystal Surface ◽  
Scanning Force Microscopy ◽  
Depth Range ◽  
Dislocation Loops ◽  
Force Microscopy ◽  
Pile Up ◽  
Out Of Plane ◽  
Scanning Force ◽  
Dynamics Simulations

AbstractThe pile up patterns arising in nanoindentation are shown to be indicative of the sample crystal symmetry. To explain and interpret these patterns, complementary molecular dynamics simulations and experiments have been performed to determine the atomistic mechanisms of the nanoindentation process in single crystal Fe{110}. The simulations show that dislocation loops start from the tip and end on the crystal surface propagating outwards along the four in-plane <111> directions. These loops carry material away from the indenter and form bumps on the surface along these directions separated from the piled-up material around the indenter hole. Atoms also move in the two out-of-plane <111> directions causing propagation of subsurface defects and pile-up around the hole. This finding is confirmed by scanning force microscopy mapping of the imprint, the piling-up pattern proving a suitable indicator of the surface crystallography. Experimental force-depth curves over the depth range of a few nanometers do not appear smooth and show distinct pop-ins. On the sub-nanometer scale these pop-ins are also visible in the simulation curves and occur as a result of the initiation of the dislocation loops from the tip.

Framework of a Contour Based Depth Map Coding Method

2012 ◽  
Vol E95.A (8) ◽  
pp. 1270-1279
Author(s):  
Minghui WANG ◽  
Xun HE ◽  
Xin JIN ◽  
Satoshi GOTO
Keyword(s):  
Depth Map ◽  
Coding Method

GEOPHYSICAL AND WATER QUALITY INDEX SURVEYS OF GROUNDWATER QUALITY AROUND UGBOR DUMPSITE IN BENIN CITY, EDO STATE, NIGERIA, WEST AFRICA.

2016 ◽  
Vol 12 (3) ◽  
pp. 4383-4393
Author(s):  
Osabuohien Idehen
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Dry Season ◽  
Depth Range ◽  
Benin City ◽  
Physico Chemical ◽  
Vertical Electric Sounding ◽  
Geoelectric Layer

This study takes a look into groundwater quality at Ugbor Dumpsite area using water quality index (WQI), 2-Dimensional (2-D) geophysical resistivity tomography and vertical electric sounding (VES).The geophysical resistivity methods employed revealed the depth to aquifer, the geoelectric layers being made up of lateritic topsoil, clayed sand and sand. Along the trasverse line in the third geoelectric layer of lateral distance of 76 m to 100 m is a very low resistivity of 0.9 to 13 m from a depth range o f about 3 to 25 m beneath the surface- indicating contamination. Water samples were collected and analyzed at the same site during the raining season and during the dry season. The value of water quality index during the raining season was 115.92 and during the dry season was 147.43. Since values at both seasons were more than 100, it implies that the water is contaminated to some extent and therefore poor for drinking purpose. The Water Quality Index was established from important analyses of biological and physico-chemical parameters with significant health importance. These values computed for dumpsite area at Ugbor were mostly contributed by the seasonal variations in the concentrations of some parameters, such as, conductivity, total dissolved solids, hardness, alkalinity, chlorides, nitrates, calcium,  phosphates, zinc, which showed significant differences (P<0.01 and P<0.05) in seasonal variation.

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