An Algorithm for Rendering Generalized Depth of Field Effects Based on Simulated Heat Diffusion
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Colour Relationships Using Traditional, Analogue and Digital Technology
Colour Relationships Using Traditional, Analogue and Digital Technology Peter Burke Skills Victoria (TAFE)/Italy (Veneto) ISS Institute Fellowship Fellowship funded by Skills Victoria, Department of Innovation, Industry and Regional Development, Victorian Government ISS Institute Inc MAY 2011 © ISS Institute T 03 9347 4583 Level 1 F 03 9348 1474 189 Faraday Street [email protected] Carlton Vic E AUSTRALIA 3053 W www.issinstitute.org.au Published by International Specialised Skills Institute, Melbourne Extract published on www.issinstitute.org.au © Copyright ISS Institute May 2011 This publication is copyright. No part may be reproduced by any process except in accordance with the provisions of the Copyright Act 1968. Whilst this report has been accepted by ISS Institute, ISS Institute cannot provide expert peer review of the report, and except as may be required by law no responsibility can be accepted by ISS Institute for the content of the report or any links therein, or omissions, typographical, print or photographic errors, or inaccuracies that may occur after publication or otherwise. ISS Institute do not accept responsibility for the consequences of any action taken or omitted to be taken by any person as a consequence of anything contained in, or omitted from, this report. Executive Summary This Fellowship study explored the use of analogue and digital technologies to create colour surfaces and sound experiences. The research focused on art and design activities that combine traditional analogue techniques (such as drawing or painting) with print and electronic media (from simple LED lighting to large-scale video projections on buildings). The Fellow’s rich and varied self-directed research was centred in Venice, Italy, with visits to France, Sweden, Scotland and the Netherlands to attend large public events such as the Biennale de Venezia and the Edinburgh Festival, and more intimate moments where one-on-one interviews were conducted with renown artists in their studios. -
Depth of Field PDF Only
Depth of Field for Digital Images Robin D. Myers Better Light, Inc. In the days before digital images, before the advent of roll film, photography was accomplished with photosensitive emulsions spread on glass plates. After processing and drying the glass negative, it was contact printed onto photosensitive paper to produce the final print. The size of the final print was the same size as the negative. During this period some of the foundational work into the science of photography was performed. One of the concepts developed was the circle of confusion. Contact prints are usually small enough that they are normally viewed at a distance of approximately 250 millimeters (about 10 inches). At this distance the human eye can resolve a detail that occupies an angle of about 1 arc minute. The eye cannot see a difference between a blurred circle and a sharp edged circle that just fills this small angle at this viewing distance. The diameter of this circle is called the circle of confusion. Converting the diameter of this circle into a size measurement, we get about 0.1 millimeters. If we assume a standard print size of 8 by 10 inches (about 200 mm by 250 mm) and divide this by the circle of confusion then an 8x10 print would represent about 2000x2500 smallest discernible points. If these points are equated to their equivalence in digital pixels, then the resolution of a 8x10 print would be about 2000x2500 pixels or about 250 pixels per inch (100 pixels per centimeter). The circle of confusion used for 4x5 film has traditionally been that of a contact print viewed at the standard 250 mm viewing distance. -
Dof 4.0 – a Depth of Field Calculator
DoF 4.0 – A Depth of Field Calculator Last updated: 8-Mar-2021 Introduction When you focus a camera lens at some distance and take a photograph, the further subjects are from the focus point, the blurrier they look. Depth of field is the range of subject distances that are acceptably sharp. It varies with aperture and focal length, distance at which the lens is focused, and the circle of confusion – a measure of how much blurring is acceptable in a sharp image. The tricky part is defining what acceptable means. Sharpness is not an inherent quality as it depends heavily on the magnification at which an image is viewed. When viewed from the same distance, a smaller version of the same image will look sharper than a larger one. Similarly, an image that looks sharp as a 4x6" print may look decidedly less so at 16x20". All other things being equal, the range of in-focus distances increases with shorter lens focal lengths, smaller apertures, the farther away you focus, and the larger the circle of confusion. Conversely, longer lenses, wider apertures, closer focus, and a smaller circle of confusion make for a narrower depth of field. Sometimes focus blur is undesirable, and sometimes it’s an intentional creative choice. Either way, you need to understand depth of field to achieve predictable results. What is DoF? DoF is an advanced depth of field calculator available for both Windows and Android. What DoF Does Even if your camera has a depth of field preview button, the viewfinder image is just too small to judge critical sharpness. -
Interacting with Autostereograms
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/336204498 Interacting with Autostereograms Conference Paper · October 2019 DOI: 10.1145/3338286.3340141 CITATIONS READS 0 39 5 authors, including: William Delamare Pourang Irani Kochi University of Technology University of Manitoba 14 PUBLICATIONS 55 CITATIONS 184 PUBLICATIONS 2,641 CITATIONS SEE PROFILE SEE PROFILE Xiangshi Ren Kochi University of Technology 182 PUBLICATIONS 1,280 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Color Perception in Augmented Reality HMDs View project Collaboration Meets Interactive Spaces: A Springer Book View project All content following this page was uploaded by William Delamare on 21 October 2019. The user has requested enhancement of the downloaded file. Interacting with Autostereograms William Delamare∗ Junhyeok Kim Kochi University of Technology University of Waterloo Kochi, Japan Ontario, Canada University of Manitoba University of Manitoba Winnipeg, Canada Winnipeg, Canada [email protected] [email protected] Daichi Harada Pourang Irani Xiangshi Ren Kochi University of Technology University of Manitoba Kochi University of Technology Kochi, Japan Winnipeg, Canada Kochi, Japan [email protected] [email protected] [email protected] Figure 1: Illustrative examples using autostereograms. a) Password input. b) Wearable e-mail notification. c) Private space in collaborative conditions. d) 3D video game. e) Bar gamified special menu. Black elements represent the hidden 3D scene content. ABSTRACT practice. This learning effect transfers across display devices Autostereograms are 2D images that can reveal 3D content (smartphone to desktop screen). when viewed with a specific eye convergence, without using CCS CONCEPTS extra-apparatus. -
A Residual Network and FPGA Based Real-Time Depth Map Enhancement System
entropy Article A Residual Network and FPGA Based Real-Time Depth Map Enhancement System Zhenni Li 1, Haoyi Sun 1, Yuliang Gao 2 and Jiao Wang 1,* 1 College of Information Science and Engineering, Northeastern University, Shenyang 110819, China; [email protected] (Z.L.); [email protected] (H.S.) 2 College of Artificial Intelligence, Nankai University, Tianjin 300071, China; [email protected] * Correspondence: [email protected] Abstract: Depth maps obtained through sensors are often unsatisfactory because of their low- resolution and noise interference. In this paper, we propose a real-time depth map enhancement system based on a residual network which uses dual channels to process depth maps and intensity maps respectively and cancels the preprocessing process, and the algorithm proposed can achieve real- time processing speed at more than 30 fps. Furthermore, the FPGA design and implementation for depth sensing is also introduced. In this FPGA design, intensity image and depth image are captured by the dual-camera synchronous acquisition system as the input of neural network. Experiments on various depth map restoration shows our algorithms has better performance than existing LRMC, DE-CNN and DDTF algorithms on standard datasets and has a better depth map super-resolution, and our FPGA completed the test of the system to ensure that the data throughput of the USB 3.0 interface of the acquisition system is stable at 226 Mbps, and support dual-camera to work at full speed, that is, 54 fps@ (1280 × 960 + 328 × 248 × 3). Citation: Li, Z.; Sun, H.; Gao, Y.; Keywords: depth map enhancement; residual network; FPGA; ToF Wang, J. -
Optimo 42 - 420 A2s Distance in Meters
DEPTH-OF-FIELD TABLES OPTIMO 42 - 420 A2S DISTANCE IN METERS REFERENCE : 323468 - A Distance in meters / Confusion circle : 0.025 mm DEPTH-OF-FIELD TABLES ZOOM 35 mm F = 42 - 420 mm The depths of field tables are provided for information purposes only and are estimated with a circle of confusion of 0.025mm (1/1000inch). The width of the sharpness zone grows proportionally with the focus distance and aperture and it is inversely proportional to the focal length. In practice the depth of field limits can only be defined accurately by performing screen tests in true shooting conditions. * : data given for informaiton only. Distance in meters / Confusion circle : 0.025 mm TABLES DE PROFONDEUR DE CHAMP ZOOM 35 mm F = 42 - 420 mm Les tables de profondeur de champ sont fournies à titre indicatif pour un cercle de confusion moyen de 0.025mm (1/1000inch). La profondeur de champ augmente proportionnellement avec la distance de mise au point ainsi qu’avec le diaphragme et est inversement proportionnelle à la focale. Dans la pratique, seuls les essais filmés dans des conditions de tournage vous permettront de définir les bornes de cette profondeur de champ avec un maximum de précision. * : information donnée à titre indicatif Distance in meters / Confusion circle : 0.025 mm APERTURE T4.5 T5.6 T8 T11 T16 T22 T32* HYPERFOCAL / DISTANCE 13,269 10,617 7,61 5,491 3,995 2,938 2,191 40 m Far ∞ ∞ ∞ ∞ ∞ ∞ ∞ Near 10,092 8,503 6,485 4,899 3,687 2,778 2,108 15 m Far ∞ ∞ ∞ ∞ ∞ ∞ ∞ Near 7,212 6,383 5,201 4,153 3,266 2,547 1,984 8 m Far 19,316 31,187 ∞ ∞ ∞ ∞ -
Deep Learning Whole Body Point Cloud Scans from a Single Depth Map
Deep Learning Whole Body Point Cloud Scans from a Single Depth Map Nolan Lunscher John Zelek University of Waterloo University of Waterloo 200 University Ave W. 200 University Ave W. [email protected] [email protected] Abstract ture the complete 3D structure of an object or person with- out the need for the machine or operator to necessarily be Personalized knowledge about body shape has numerous an expert at how to take every measurement. Scanning also applications in fashion and clothing, as well as in health has the benefit that all possible measurements are captured, monitoring. Whole body 3D scanning presents a relatively rather than only measurements at specific points [27]. simple mechanism for individuals to obtain this information Beyond clothing, understanding body measurements and about themselves without needing much knowledge of an- shape can provide details about general health and fitness. thropometry. With current implementations however, scan- In recent years, products such as Naked1 and ShapeScale2 ning devices are large, complex and expensive. In order to have begun to be released with the intention of capturing 3D make such systems as accessible and widespread as pos- information about a person, and calculating various mea- sible, it is necessary to simplify the process and reduce sures such as body fat percentage and muscle gains. The their hardware requirements. Deep learning models have technologies can then be used to monitor how your body emerged as the leading method of tackling visual tasks, in- changes overtime, and offer detailed fitness tracking. cluding various aspects of 3D reconstruction. In this paper Depth map cameras and RGBD cameras, such as the we demonstrate that by leveraging deep learning it is pos- XBox Kinect or Intel Realsense, have become increasingly sible to create very simple whole body scanners that only popular in 3D scanning in recent years, and have even made require a single input depth map to operate. -
Evidences of Dr. Foote's Success
EVIDENCES OF J "'ll * ' 'A* r’ V*. * 1A'-/ COMPILED FROM BOOKS OF BIOGRAPHY, MSS., LETTERS FROM GRATEFUL PATIENTS, AND FROM FAVORABLE NOTICES OF THE PRESS i;y the %)J\l |)utlfs!iCnfl (Kompans 129 East 28tii Street, N. Y. 1885. "A REMARKABLE BOOKf of Edinburgh, Scot- land : a graduate of three universities, and retired after 50 years’ practice, he writes: “The work in priceless in value, and calculated to re- I tenerate aoclety. It la new, startling, and very Instructive.” It is the most popular and comprehensive book treating of MEDICAL, SOCIAL, AND SEXUAL SCIENCE, P roven by the sale of Hair a million to be the most popula R ! R eaaable because written in language plain, chasti, and forcibl E I instructive, practicalpresentation of “MidiciU Commc .'Sense” medi A V aiuable to invalids, showing new means by which they may be cure D A pproved by editors, physicians, clergymen, critics, and literat I T horough treatment of subjects especially important to young me N E veryone who “wants to know, you know,” will find it interestin C I 4 Parts. 35 Chapters, 936 Pages, 200 Illustrations, and AT T7 \\T 17'T7 * rpT T L) t? just introduced, consists of a series A It Ci VV ETjAI C U D, of beautiful colored anatom- ical charts, in fivecolors, guaranteed superior to any before offered in a pop ular physiological book, and rendering it again the most attractive and quick- selling A Arr who have already found a gold mine in it. Mr. 17 PCl “ work for it v JIj/1” I O Koehler writes: I sold the first six hooks in two hours.” Many agents take 50 or 100at once, at special rates. -
Depth of Field in Photography
Instructor: N. David King Page 1 DEPTH OF FIELD IN PHOTOGRAPHY Handout for Photography Students N. David King, Instructor WWWHAT IS DDDEPTH OF FFFIELD ??? Photographers generally have to deal with one of two main optical issues for any given photograph: Motion (relative to the film plane) and Depth of Field. This handout is about Depth of Field. But what is it? Depth of Field is a major compositional tool used by photographers to direct attention to specific areas of a print or, at the other extreme, to allow the viewer’s eye to travel in focus over the entire print’s surface, as it appears to do in reality. Here are two example images. Depth of Field Examples Shallow Depth of Field Deep Depth of Field using wide aperture using small aperture and close focal distance and greater focal distance Depth of Field in PhotogPhotography:raphy: Student Handout © N. DavDavidid King 2004, Rev 2010 Instructor: N. David King Page 2 SSSURPRISE !!! The first image (the garden flowers on the left) was shot IIITTT’’’S AAALL AN ILLUSION with a wide aperture and is focused on the flower closest to the viewer. The second image (on the right) was shot with a smaller aperture and is focused on a yellow flower near the rear of that group of flowers. Though it looks as if we are really increasing the area that is in focus from the first image to the second, that apparent increase is actually an optical illusion. In the second image there is still only one plane where the lens is critically focused. -
Fusing Multimedia Data Into Dynamic Virtual Environments
ABSTRACT Title of dissertation: FUSING MULTIMEDIA DATA INTO DYNAMIC VIRTUAL ENVIRONMENTS Ruofei Du Doctor of Philosophy, 2018 Dissertation directed by: Professor Amitabh Varshney Department of Computer Science In spite of the dramatic growth of virtual and augmented reality (VR and AR) technology, content creation for immersive and dynamic virtual environments remains a signifcant challenge. In this dissertation, we present our research in fusing multimedia data, including text, photos, panoramas, and multi-view videos, to create rich and compelling virtual environments. First, we present Social Street View, which renders geo-tagged social media in its natural geo-spatial context provided by 360° panoramas. Our system takes into account visual saliency and uses maximal Poisson-disc placement with spatiotem- poral flters to render social multimedia in an immersive setting. We also present a novel GPU-driven pipeline for saliency computation in 360° panoramas using spher- ical harmonics (SH). Our spherical residual model can be applied to virtual cine- matography in 360° videos. We further present Geollery, a mixed-reality platform to render an interactive mirrored world in real time with three-dimensional (3D) buildings, user-generated content, and geo-tagged social media. Our user study has identifed several use cases for these systems, including immersive social storytelling, experiencing the culture, and crowd-sourced tourism. We next present Video Fields, a web-based interactive system to create, cal- ibrate, and render dynamic videos overlaid on 3D scenes. Our system renders dynamic entities from multiple videos, using early and deferred texture sampling. Video Fields can be used for immersive surveillance in virtual environments. Fur- thermore, we present VRSurus and ARCrypt projects to explore the applications of gestures recognition, haptic feedback, and visual cryptography for virtual and augmented reality. -
Construction of Autostereograms Taking Into Account Object Colors and Its Applications for Steganography
Construction of Autostereograms Taking into Account Object Colors and its Applications for Steganography Yusuke Tsuda Yonghao Yue Tomoyuki Nishita The University of Tokyo ftsuday,yonghao,[email protected] Abstract Information on appearances of three-dimensional ob- jects are transmitted via the Internet, and displaying objects q plays an important role in a lot of areas such as movies and video games. An autostereogram is one of the ways to represent three- dimensional objects taking advantage of binocular paral- (a) SS-relation (b) OS-relation lax, by which depths of objects are perceived. In previous Figure 1. Relations on an autostereogram. methods, the colors of objects were ignored when construct- E and E indicate left and right eyes, re- ing autostereogram images. In this paper, we propose a L R spectively. (a): A pair of points on the screen method to construct autostereogram images taking into ac- and a single point on the object correspond count color variation of objects, such as shading. with each other. (b): A pair of points on the We also propose a technique to embed color informa- object and a single point on the screen cor- tion in autostereogram images. Using our technique, au- respond with each other. tostereogram images shown on a display change, and view- ers can enjoy perceiving three-dimensional object and its colors in two stages. Color information is embedded in we propose an approach to take into account color variation a monochrome autostereogram image, and colors appear of objects, such as shading. Our approach assign slightly when the correct color table is used. -
Ddrnet: Depth Map Denoising and Refinement for Consumer Depth
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs Shi Yan1, Chenglei Wu2, Lizhen Wang1, Feng Xu1, Liang An1, Kaiwen Guo3, and Yebin Liu1 1 Tsinghua University, Beijing, China 2 Facebook Reality Labs, Pittsburgh, USA 3 Google Inc, Mountain View, CA, USA Abstract. Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X. However, they still suffer from heavy noises which limit their applications. Although plenty of progresses have been made to reduce the noises and boost geometric details, due to the inherent illness and the real-time requirement, the problem is still far from been solved. We pro- pose a cascaded Depth Denoising and Refinement Network (DDRNet) to tackle this problem by leveraging the multi-frame fused geometry and the accompanying high quality color image through a joint training strategy. The rendering equation is exploited in our network in an unsupervised manner. In detail, we impose an unsupervised loss based on the light transport to extract the high-frequency geometry. Experimental results indicate that our network achieves real-time single depth enhancemen- t on various categories of scenes. Thanks to the well decoupling of the low and high frequency information in the cascaded network, we achieve superior performance over the state-of-the-art techniques. Keywords: Depth enhancement · Consumer depth camera · Unsuper- vised learning · Convolutional Neural Networks· DynamicFusion 1 Introduction Consumer depth cameras have enabled lots of new applications in computer vision and graphics, ranging from live 3D scanning to virtual and augmented reality.