scholarly journals Implementation of Image Segmentation Using RGB Color Detection on Virtual Reality Robot as Spy Tool

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Rossi Passarella ◽  
Sutarno . ◽  
Ahmad Rezqy FF
Keyword(s):  
Virtual Reality ◽  
Image Segmentation ◽  
Color Detection

scholarly journals Invisibility Cloak using Color Detection and Segmentation with Open CV

2020 ◽  
Vol 9 (4) ◽  
pp. 1621-1624
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Harry Potter ◽  
Invisibility Cloak ◽  
Invisible Cloak ◽  
The World ◽  
Color Detection ◽  
False Sense ◽  
Collective Imagination ◽  
Science Fantasy

Have you ever thought of making visible things invisible, just like the Harry Potter? Have you ever thought how does one supersede backgrounds and add effects in a movie? The cloak was magical and invisible in Harry Potter, the movie. As we know there is no magic and no invisible cloak which exists in the world. It’s all about the graphics tricks. The concept of an invisibility cloak is a mixture of science, fantasy, and the collective imagination. This paper helps to create one’s own ‘Invisibility Cloak’.It will make use of Python and OpenCV module specifically targeting Image Processing and Image Segmentation to create a false sense of invisibility in the frame. It will explore how an object of a specific color or texture can be manipulated using the OpenCV library of python. To achieve this, initially we’ll be capturing and storing the backdrop frame . Thereafter we’ll be identifying the red coloured fabric by making use of the above mentioned algorithms. Then we’ll segment out the red colored fabric by generating a mask and then finally , we’ll generate the final augmented(magical) output to create Invisibility cloak. These steps are discussed deeper in the paper

3D Medical Image Segmentation in Virtual Reality

2019 ◽  
Vol 2019 (2) ◽  
pp. 188-1-188-6
Author(s):  
Shea B Yonker ◽  
Oleksandr O Korshak ◽  
Timothy Hedstrom ◽  
Alexander Wu ◽  
Siddharth Atre ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation

scholarly journals A VR-based volumetric medical image segmentation and visualization system with natural human interaction

2021 ◽  
Author(s):  
Yi Gao ◽  
Cheng Chang ◽  
Xiaxia Yu ◽  
Pengjin Pang ◽  
Nian Xiong ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Image Segmentation ◽  
User Interface ◽  
Transfer Function ◽  
Volume Rendering ◽  
Human Interaction ◽  
Natural User Interface ◽  
Data Set ◽  
Probability Map ◽  
3D Volume

AbstractVolume rendering produces informative two-dimensional (2D) images from a 3-dimensional (3D) volume. It highlights the region of interest and facilitates a good comprehension of the entire data set. However, volume rendering faces a few challenges. First, a high-dimensional transfer function is usually required to differentiate the target from its neighboring objects with subtle variance. Unfortunately, designing such a transfer function is a strenuously trial-and-error process. Second, manipulating/visualizing a 3D volume with a traditional 2D input/output device suffers dimensional limitations. To address all the challenges, we design NUI-VR$$^2$$ 2 , a natural user interface-enabled volume rendering system in the virtual reality space. NUI-VR$$^2$$ 2 marries volume rendering and interactive image segmentation. It transforms the original volume into a probability map with image segmentation. A simple linear transfer function will highlight the target well in the probability map. More importantly, we set the entire image segmentation and volume rendering pipeline in an immersive virtual reality environment with a natural user interface. NUI-VR$$^2$$ 2 eliminates the dimensional limitations in manipulating and perceiving 3D volumes and dramatically improves the user experience.

scholarly journals Computational and mathematical methods in brain atlasing

2017 ◽  
Vol 30 (6) ◽  
pp. 520-534 ◽  
Author(s):  
Wieslaw L Nowinski
Keyword(s):  
Virtual Reality ◽  
Image Segmentation ◽  
Computational Methods ◽  
Physical Modelling ◽  
Clinical Applications ◽  
Brain Atlas ◽  
Mathematical Methods ◽  
Wide Range ◽  
Scan Registration ◽  
Brain Atlases

Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

scholarly journals Invisibility Cloak using Color Detection and Segmentation with open CV

2020 ◽  
Vol 6 (12) ◽  
pp. 440-444
Author(s):  
Puneet and Vasudha Bahl
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Harry Potter ◽  
Invisibility Cloak ◽  
Invisible Cloak ◽  
The World ◽  
Color Detection ◽  
False Sense ◽  
Collective Imagination ◽  
Science Fantasy

Have you ever thought of making visible things invisible, just like the Harry Potter? Have you ever thought how does one supersede backgrounds and add effects in a movie? The cloak was magical and invisible in Harry Potter, the movie. As we know there is no magic and no invisible cloak which exists in the world. It’s all about the graphicstricks. The concept of an invisibility cloak is a mixture of science, fantasy, and the collective imagination. This paper helps to create one’s own ‘Invisibility Cloak’.It will make use of Python and OpenCV module specifically targeting Image Processing and Image Segmentation to create a false sense of invisibility in the frame. It will explore how an object of a specific color or texture can be manipulated using the OpenCV library of python. To achieve this, initially we’ll be capturing and storing the backdrop frame . Thereafter we’ll be identifying the red coloured fabric by making use of the above mentioned algorithms. Then we’ll segment out the red colored fabric by generating a mask and then finallywe’ll generate the final augmented(magical) output to create Invisibility cloak. These steps are discussed deeper in thepaper.

Top-Down Processing and Visual Reorientation Illusions in a Virtual Reality Environment

2004 ◽  
Vol 63 (3) ◽  
pp. 143-149 ◽  
Author(s):  
Fred W. Mast ◽  
Charles M. Oman
Keyword(s):  
Virtual Reality ◽  
Visual Processing ◽  
Line Length ◽  
Perceptual Cues ◽  
Virtual Reality Environment ◽  
Top Down ◽  
Test Conditions ◽  
The Subject ◽  
Processing Mechanisms

The role of top-down processing on the horizontal-vertical line length illusion was examined by means of an ambiguous room with dual visual verticals. In one of the test conditions, the subjects were cued to one of the two verticals and were instructed to cognitively reassign the apparent vertical to the cued orientation. When they have mentally adjusted their perception, two lines in a plus sign configuration appeared and the subjects had to evaluate which line was longer. The results showed that the line length appeared longer when it was aligned with the direction of the vertical currently perceived by the subject. This study provides a demonstration that top-down processing influences lower level visual processing mechanisms. In another test condition, the subjects had all perceptual cues available and the influence was even stronger.

Laparoscopic Cholecystectomy

Swiss Surgery ◽  
2002 ◽  
Vol 8 (6) ◽  
pp. 250-254
Author(s):  
Vogelbach ◽  
Bogdan ◽  
Rosenthal ◽  
Pfefferkorn ◽  
Triponez
Keyword(s):  
Virtual Reality ◽  
Laparoscopic Cholecystectomy ◽  
Neue Technik

Fragestellung: Die dieser Untersuchung zugrunde liegende Frage war, ob das angewandte Ausbildungskonzept geeignet war, um am Beispiel der Einführung der laparoskopischen Cholezystektomie eine neue Operationsmethode in einer universitären Ausbildungsklinik mit einer grossen Anzahl Chirurgen zu etablieren. Patienten und Methodik: Seit Einführung der ersten laparoskopischen Cholezystektomie wurden alle Cholezystektomien während zwei Jahren (Mai 1990 bis Mai 1992) prospektiv erfasst. Ein Ausbildungskonzept wurde gewählt, bei dem jeweils ein Operateur durch einen Tutor geschult wurde und so 15 konsekutive Eingriffe durchführte, um dann die Technik einem weiteren auszubildenden Chirurgen zu instruieren. Resultate: In zwei Jahren wurden 355 Patienten cholezystektomiert. 60% der Operationen wurden laparoskopisch durchgeführt oder begonnen. 40% der Operationen wurden offen durchgeführt. In den ersten zwei Jahren konnten 13 Operateure (durchschnittlich 16 Operationen / Operateur , range 1 - 60) in die neue Technik eingeführt werden. Es traten keine schweren Komplikationen, insbesondere keine Gallenwegsverletzungen in dieser Einführungsphase auf. Diskussion: In der Literatur wird dieses Vorgehen bei der Einführungsphase seit 1992 wiederholt vorgestellt, diskutiert und empfohlen. Zwischenzeitlich gibt es Richtlinien von Fachgesellschaften und nationalen Institutionen, welche die Ausbildung zur Ausführung neuer chirurgischer Techniken reglementieren. In den letzten Jahren verlagern sich die ersten Ausbildungsschritte in Richtung Trainingskurse an skill-stations und virtual reality Trainer. Schlussfolgerung: Das beschriebene Ausbildungskonzept bewährte sich in der Einführungsphase der laparoskopischen Cholezystektomie zu Beginn der 90er-Jahre.

Virtual reality-enabled treatment of nightmares.

Dreaming ◽  
2018 ◽  
Vol 28 (3) ◽  
pp. 205-224 ◽  
Author(s):  
Patrick McNamara ◽  
Kendra Holt Moore ◽  
Yiannis Papelis ◽  
Saikou Diallo ◽  
Wesley J. Wildman
Keyword(s):  
Virtual Reality
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