scholarly journals Fast computer-generated hologram computation using rendered depth map image

2017 ◽  
Author(s):  
Seyedmahdi Kazempourradi ◽  
Erdem Ulusoy ◽  
Hakan Urey
Keyword(s):  
Depth Map ◽  
Computer Generated Hologram ◽  
Fast Computer

Fast Computer Generated Hologram Algorithm of Triangle Mesh Models

2013 ◽  
Vol 40 (7) ◽  
pp. 0709001 ◽  
Author(s):  
张亚萍 Zhang Yaping ◽  
张建强 Zhang Jianqiang ◽  
陈伟 Chen Wei ◽  
王鹏 Wang Peng ◽  
吴上 Wu Shang ◽  
...  
Keyword(s):  
Triangle Mesh ◽  
Computer Generated Hologram ◽  
Fast Computer ◽  
Mesh Models

Computer Generated Hologram of Complex Three-Dimensional Object Based on a Two-Dimensional Color Image and the Depth Map

2014 ◽  
Vol 41 (6) ◽  
pp. 0609002
Author(s):  
曹雪梅 Cao Xuemei ◽  
桑新柱 Sang Xinzhu ◽  
陈志东 Chen Zhidong ◽  
冷俊敏 Leng Junmin ◽  
张明 Zhang Ming ◽  
...  
Keyword(s):  
Color Image ◽  
Three Dimensional ◽  
Depth Map ◽  
Two Dimensional ◽  
Computer Generated Hologram ◽  
Object Based ◽  
Dimensional Object

Fast Computer-Generated Hologram Generation Method for Three-Dimensional Point Cloud Model

2016 ◽  
Vol 12 (12) ◽  
pp. 1688-1694 ◽  
Author(s):  
Ping Su ◽  
Wenbo Cao ◽  
Jianshe Ma ◽  
Bingchao Cheng ◽  
Xianting Liang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Three Dimensional ◽  
Computer Generated Hologram ◽  
Fast Computer ◽  
Point Cloud Model

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.

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

Automated Detection of Pin Defects on Counterfeit Microelectronics

2018 ◽  
Author(s):  
Pallabi Ghosh ◽  
Domenic Forte ◽  
Damon L. Woodard ◽  
Rajat Subhra Chakraborty
Keyword(s):  
Integrated Circuits ◽  
Depth Map ◽  
Optical Microscope ◽  
Automated Detection ◽  
Detection Methods ◽  
Global Supply Chains ◽  
Subject Matter Experts ◽  
Security Breaches ◽  
The Past ◽  
Skilled Subject

Abstract Counterfeit electronics constitute a fast-growing threat to global supply chains as well as national security. With rapid globalization, the supply chain is growing more and more complex with components coming from a diverse set of suppliers. Counterfeiters are taking advantage of this complexity and replacing original parts with fake ones. Moreover, counterfeit integrated circuits (ICs) may contain circuit modifications that cause security breaches. Out of all types of counterfeit ICs, recycled and remarked ICs are the most common. Over the past few years, a plethora of counterfeit IC detection methods have been created; however, most of these methods are manual and require highly-skilled subject matter experts (SME). In this paper, an automated bent and corroded pin detection methodology using image processing is proposed to identify recycled ICs. Here, depth map of images acquired using an optical microscope are used to detect bent pins, and segmented side view pin images are used to detect corroded pins.

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