Graphics and Multimedia
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Animatsiooni Töövõtted Ja Terminid
Multimeedium, 2D animatsioon Animatsiooni töövõtted ja terminid Nii 2D kui ka 3D animatsioonide juures kasutatakse mitmeid samu võtteid ja termineid. Mõned neist on järgnevas lahtiseletatud. Keyframe Keyframe ehk võtmekaader on selline kaader, millel autor määrab objekti(de) täpse asukoha, suuruse, kuju, värvuse jms. antud ajahetkel. Kaadrid kahe keyframe vahel genereeritakse tavaliselt animatsiooniprogrammi poolt Keyframe 1 Keyframe 2 keerukate arvutuste tulemusena. Traditsioonilise animeerimise korral joonistavad peakunstnikud vajalikud keyframe'id ning nende assistendid kõik ülejäänu. Tracing Tracing on meetod uue kaadri loomiseks eelmise või vahel ka järgmise kopeerimise (trace) teel. Kopeeritava kujutise näitamiseks kasutatakse mitmeid meetodeid, sealhulgas aluseks oleva kujutise näitamine teise värviga, sageli sinisega. Tweening Tweening on tehnika, mis etteantud keyframe'ide vahele genereerib kasutaja poolt etteantud arvu kaadreid, millede jooksul toimub järkjärguline üleminek esimeselt keyframe'ilt teisele. Võttes arvesse kahe keyframe'i erinevusi, kalkuleerib arvuti iga kaadri jaoks proportsionaalsed muudatused objektide asukoha, suuruse, värvuse, kuju jms jaoks. Mida rohkem kaadreid lastakse genereerida, seda väiksemad tulevad järjestikuste kaadrite vahelised erinevused ning seda sujuvam tuleb liikumine. Keyframe 1 genereeritud kaadrid Keyframe 2 Rendering Rendering on protsess, mille käigus objektide traatmudelid (wireframe vahel ka mesh) kaetakse pinnatekstuuridega, neile lisatakse valgusefektid ja värvid, et saada realistlik -
Intel® Desktop Board DG965PZ Technical Product Specification
Intel® Desktop Board DG965PZ Technical Product Specification August 2006 Order Number: D56009-001US The Intel® Desktop Board DG965PZ may contain design defects or errors known as errata that may cause the product to deviate from published specifications. Current characterized errata are documented in the Intel Desktop Board DG965PZ Specification Update. Revision History Revision Revision History Date -001 First release of the Intel® Desktop Board DG965PZ Technical Product August 2006 Specification. This product specification applies to only the standard Intel® Desktop Board DG965PZ with BIOS identifier MQ96510A.86A. Changes to this specification will be published in the Intel Desktop Board DG965PZ Specification Update before being incorporated into a revision of this document. INFORMATION IN THIS DOCUMENT IS PROVIDED IN CONNECTION WITH INTEL® PRODUCTS. NO LICENSE, EXPRESS OR IMPLIED, BY ESTOPPEL OR OTHERWISE, TO ANY INTELLECTUAL PROPERTY RIGHTS IS GRANTED BY THIS DOCUMENT. EXCEPT AS PROVIDED IN INTEL’S TERMS AND CONDITIONS OF SALE FOR SUCH PRODUCTS, INTEL ASSUMES NO LIABILITY WHATSOEVER, AND INTEL DISCLAIMS ANY EXPRESS OR IMPLIED WARRANTY, RELATING TO SALE AND/OR USE OF INTEL PRODUCTS INCLUDING LIABILITY OR WARRANTIES RELATING TO FITNESS FOR A PARTICULAR PURPOSE, MERCHANTABILITY, OR INFRINGEMENT OF ANY PATENT, COPYRIGHT OR OTHER INTELLECTUAL PROPERTY RIGHT. INTEL PRODUCTS ARE NOT INTENDED FOR USE IN MEDICAL, LIFE SAVING, OR LIFE SUSTAINING APPLICATIONS. Intel Corporation may have patents or pending patent applications, trademarks, copyrights, or other intellectual property rights that relate to the presented subject matter. The furnishing of documents and other materials and information does not provide any license, express or implied, by estoppel or otherwise, to any such patents, trademarks, copyrights, or other intellectual property rights. -
The Application of Voxel Size Correction in X-Ray Computed Tomography for Dimensional Metrology
SINCE2013 Singapore International NDT Conference & Exhibition 2013, 19-20 July 2013 The Application of Voxel Size Correction in X-ray Computed Tomography for Dimensional Metrology Joseph J. LIFTON1,2, Andrew A. MALCOLM2, John W. MCBRIDE1,3, Kevin J. CROSS1 1The University of Southampton, United Kingdom; Email: [email protected]. 2Singapore Institute of Manufacturing Technology, Singapore; Email: [email protected]. 3The University of Southampton Malaysia Campus (USMC), Malaysia; Email: [email protected]. Abstract X-ray computed tomography (CT) is a non-destructive, radiographic scanning technique that enables the visualisation and dimensional evaluation of both internal and external features of a workpiece; it is therefore an attractive alternative for measurement tasks that prove problematic for conventional tactile and optical instruments. The data output of a CT measurement is a volume of grey value integers that describe the material distribution of the scanned workpiece; the relative spacing of volume-elements (voxels) is termed voxel size and influences all dimensional information evaluated from a CT data-set. Voxel size is defined by the position of a workpiece relative to the X-ray source and detector, and is therefore prone to axis position errors, errors in the geometric alignment of the CT system’s hardware, and the positional drift of the X-ray focal spot. In this work a method is presented for calculating a voxel scaling factor that corrects for voxel size errors, and this method is then applied to a general X-ray CT measurement task and demonstrated to reduce measurement errors. Keywords: X-ray computed tomography, dimensional metrology, calibration, uncertainty, voxel size. -
Temporal Voxel Cone Tracing with Interleaved Sample Patterns by Sanghyeok Hong
c 2015, SangHyeok Hong. All Rights Reserved. The material presented within this document does not necessarily reflect the opinion of the Committee, the Graduate Study Program, or DigiPen Institute of Technology. TEMPORAL VOXEL CONE TRACING WITH INTERLEAVED SAMPLE PATTERNS BY SangHyeok Hong THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science awarded by DigiPen Institute of Technology Redmond, Washington United States of America March 2015 Thesis Advisor: Gary Herron DIGIPEN INSTITUTE OF TECHNOLOGY GRADUATE STUDIES PROGRAM DEFENSE OF THESIS THE UNDERSIGNED VERIFY THAT THE FINAL ORAL DEFENSE OF THE MASTER OF SCIENCE THESIS TITLED Temporal Voxel Cone Tracing with Interleaved Sample Patterns BY SangHyeok Hong HAS BEEN SUCCESSFULLY COMPLETED ON March 12th, 2015. MAJOR FIELD OF STUDY: COMPUTER SCIENCE. APPROVED: Dmitri Volper date Xin Li date Graduate Program Director Dean of Faculty Dmitri Volper date Claude Comair date Department Chair, Computer Science President DIGIPEN INSTITUTE OF TECHNOLOGY GRADUATE STUDIES PROGRAM THESIS APPROVAL DATE: March 12th, 2015 BASED ON THE CANDIDATE'S SUCCESSFUL ORAL DEFENSE, IT IS RECOMMENDED THAT THE THESIS PREPARED BY SangHyeok Hong ENTITLED Temporal Voxel Cone Tracing with Interleaved Sample Patterns BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER SCIENCE AT DIGIPEN INSTITUTE OF TECHNOLOGY. Gary Herron date Xin Li date Thesis Committee Chair Thesis Committee Member Pushpak Karnick date Matt -
SIMD Extensions
SIMD Extensions PDF generated using the open source mwlib toolkit. See http://code.pediapress.com/ for more information. PDF generated at: Sat, 12 May 2012 17:14:46 UTC Contents Articles SIMD 1 MMX (instruction set) 6 3DNow! 8 Streaming SIMD Extensions 12 SSE2 16 SSE3 18 SSSE3 20 SSE4 22 SSE5 26 Advanced Vector Extensions 28 CVT16 instruction set 31 XOP instruction set 31 References Article Sources and Contributors 33 Image Sources, Licenses and Contributors 34 Article Licenses License 35 SIMD 1 SIMD Single instruction Multiple instruction Single data SISD MISD Multiple data SIMD MIMD Single instruction, multiple data (SIMD), is a class of parallel computers in Flynn's taxonomy. It describes computers with multiple processing elements that perform the same operation on multiple data simultaneously. Thus, such machines exploit data level parallelism. History The first use of SIMD instructions was in vector supercomputers of the early 1970s such as the CDC Star-100 and the Texas Instruments ASC, which could operate on a vector of data with a single instruction. Vector processing was especially popularized by Cray in the 1970s and 1980s. Vector-processing architectures are now considered separate from SIMD machines, based on the fact that vector machines processed the vectors one word at a time through pipelined processors (though still based on a single instruction), whereas modern SIMD machines process all elements of the vector simultaneously.[1] The first era of modern SIMD machines was characterized by massively parallel processing-style supercomputers such as the Thinking Machines CM-1 and CM-2. These machines had many limited-functionality processors that would work in parallel. -
Human Body Model Acquisition and Tracking Using Voxel Data
Submitted to the International Journal of Computer Vision Human Body Model Acquisition and Tracking using Voxel Data Ivana Mikić2, Mohan Trivedi1, Edward Hunter2, Pamela Cosman1 1Department of Electrical and Computer Engineering University of California, San Diego 2Q3DM, Inc. Abstract We present an integrated system for automatic acquisition of the human body model and motion tracking using input from multiple synchronized video streams. The video frames are segmented and the 3D voxel reconstructions of the human body shape in each frame are computed from the foreground silhouettes. These reconstructions are then used as input to the model acquisition and tracking algorithms. The human body model consists of ellipsoids and cylinders and is described using the twists framework resulting in a non-redundant set of model parameters. Model acquisition starts with a simple body part localization procedure based on template fitting and growing, which uses prior knowledge of average body part shapes and dimensions. The initial model is then refined using a Bayesian network that imposes human body proportions onto the body part size estimates. The tracker is an extended Kalman filter that estimates model parameters based on the measurements made on the labeled voxel data. A voxel labeling procedure that handles large frame-to-frame displacements was designed resulting in the very robust tracking performance. Extensive evaluation shows that the system performs very reliably on sequences that include different types of motion such as walking, sitting, dancing, running and jumping and people of very different body sizes, from a nine year old girl to a tall adult male. 1. Introduction Tracking of the human body, also called motion capture or posture estimation, is a problem of estimating the parameters of the human body model (such as joint angles) from the video data as the position and configuration of the tracked body change over time. -
This Is Your Presentation Title
Introduction to GPU/Parallel Computing Ioannis E. Venetis University of Patras 1 Introduction to GPU/Parallel Computing www.prace-ri.eu Introduction to High Performance Systems 2 Introduction to GPU/Parallel Computing www.prace-ri.eu Wait, what? Aren’t we here to talk about GPUs? And how to program them with CUDA? Yes, but we need to understand their place and their purpose in modern High Performance Systems This will make it clear when it is beneficial to use them 3 Introduction to GPU/Parallel Computing www.prace-ri.eu Top 500 (June 2017) CPU Accel. Rmax Rpeak Power Rank Site System Cores Cores (TFlop/s) (TFlop/s) (kW) National Sunway TaihuLight - Sunway MPP, Supercomputing Center Sunway SW26010 260C 1.45GHz, 1 10.649.600 - 93.014,6 125.435,9 15.371 in Wuxi Sunway China NRCPC National Super Tianhe-2 (MilkyWay-2) - TH-IVB-FEP Computer Center in Cluster, Intel Xeon E5-2692 12C 2 Guangzhou 2.200GHz, TH Express-2, Intel Xeon 3.120.000 2.736.000 33.862,7 54.902,4 17.808 China Phi 31S1P NUDT Swiss National Piz Daint - Cray XC50, Xeon E5- Supercomputing Centre 2690v3 12C 2.6GHz, Aries interconnect 3 361.760 297.920 19.590,0 25.326,3 2.272 (CSCS) , NVIDIA Tesla P100 Cray Inc. DOE/SC/Oak Ridge Titan - Cray XK7 , Opteron 6274 16C National Laboratory 2.200GHz, Cray Gemini interconnect, 4 560.640 261.632 17.590,0 27.112,5 8.209 United States NVIDIA K20x Cray Inc. DOE/NNSA/LLNL Sequoia - BlueGene/Q, Power BQC 5 United States 16C 1.60 GHz, Custom 1.572.864 - 17.173,2 20.132,7 7.890 4 Introduction to GPU/ParallelIBM Computing www.prace-ri.eu How do -
Photorealistic Scene Reconstruction by Voxel Coloring
In Proc. Computer Vision and Pattern Recognition Conf., pp. 1067-1073, 1997. Photorealistic Scene Reconstruction by Voxel Coloring Steven M. Seitz Charles R. Dyer Department of Computer Sciences University of Wisconsin, Madison, WI 53706 g E-mail: fseitz,dyer @cs.wisc.edu WWW: http://www.cs.wisc.edu/computer-vision Broad Viewpoint Coverage: Reprojections should be Abstract accurate over a large range of target viewpoints. This A novel scene reconstruction technique is presented, requires that the input images are widely distributed different from previous approaches in its ability to cope about the environment with large changes in visibility and its modeling of in- trinsic scene color and texture information. The method The photorealistic scene reconstruction problem, as avoids image correspondence problems by working in a presently formulated, raises a number of unique challenges discretized scene space whose voxels are traversed in a that push the limits of existing reconstruction techniques. fixed visibility ordering. This strategy takes full account Photo integrity requires that the reconstruction be dense of occlusions and allows the input cameras to be far apart and sufficiently accurate to reproduce the original images. and widely distributed about the environment. The algo- This criterion poses a problem for existing feature- and rithm identifies a special set of invariant voxels which to- contour-based techniques that do not provide dense shape gether form a spatial and photometric reconstruction of the estimates. While these techniques can produce texture- scene, fully consistent with the input images. The approach mapped models [1, 3, 4], accuracy is ensured only in places is evaluated with images from both inward- and outward- where features have been detected. -
Online Detector Response Calculations for High-Resolution PET Image Reconstruction
Home Search Collections Journals About Contact us My IOPscience Online detector response calculations for high-resolution PET image reconstruction This article has been downloaded from IOPscience. Please scroll down to see the full text article. 2011 Phys. Med. Biol. 56 4023 (http://iopscience.iop.org/0031-9155/56/13/018) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 171.65.80.217 The article was downloaded on 15/06/2011 at 20:11 Please note that terms and conditions apply. IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY Phys. Med. Biol. 56 (2011) 4023–4040 doi:10.1088/0031-9155/56/13/018 Online detector response calculations for high-resolution PET image reconstruction Guillem Pratx1 and Craig Levin2 1 Department of Radiation Oncology, Stanford University, Stanford, CA 94305, USA 2 Departments of Radiology, Physics and Electrical Engineering, and Molecular Imaging Program at Stanford, Stanford University, Stanford, CA 94305, USA E-mail: [email protected] Received 3 January 2011, in final form 19 May 2011 Published 15 June 2011 Online at stacks.iop.org/PMB/56/4023 Abstract Positron emission tomography systems are best described by a linear shift- varying model. However, image reconstruction often assumes simplified shift- invariant models to the detriment of image quality and quantitative accuracy. We investigated a shift-varying model of the geometrical system response based on an analytical formulation. The model was incorporated within a list- mode, fully 3D iterative reconstruction process in which the system response coefficients are calculated online on a graphics processing unit (GPU). -
12. Optical Data Storage
12. Optical data storage 12.1 Theory of color RGB additive color model B B Blue Magenta (0.6, 0.2, 1) (0,0,1) (1,0,1) Gray shades Cyan (g,g,g) (0,1,1) White (1,1,1) (0.6, 1, 0.7) R R Black Red (0,0,0) (1,0,0) G Green (0,1,0) (1, 0.7, 0.1) Yellow (1,1,0) G The RGB color model converts the additive color mixing system into a digital system, in which any color is represented by a point in a color tridimensional space and thus by a three coordinates vector. The coordinates are composed of the respective proportions of the primary light colors (Red, Green, Blue). The RGB model is often used by software as it requires no conversion in order to display colors on a computer screen. 169 CMY subtractive color model Y Y Yellow Green (0, 0.3, 0.9) (0,0,1) (1,0,1) Gray shades Red (g,g,g) (0,1,1) Black (1,1,1) C (0.4, 0, 0.3) C White Cyan (0,0,0) (1,0,0) M Magenta (0,1,0) (0.4, 0.8, 0) Blue (1,1,0) M The CMY color model corresponds to the subtractive color mixing system. The coordinates of a point in this color space are composed of the respective proportions of the primary filter colors (Cyan, Magenta, Yellow). The CMY model is used whenever a colored document is printed. The conversion RGB and CMY models can be written as follows: For the orange point, for example: 170 HSV color model V Green Yellow 120° 60° (37°, 0.9, 1) White H 1.0 S Cyan Red 180° 0° V Blue Magenta 240° 300° Hue is given in polar coordinate. -
14. Color Mapping
14. Color Mapping Jacobs University Visualization and Computer Graphics Lab Recall: RGB color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 691 CMY color model • The CMY color model is related to the RGB color model. •Itsbasecolorsare –cyan(C) –magenta(M) –yellow(Y) • They are arranged in a 3D Cartesian coordinate system. • The scheme is subtractive. Jacobs University Visualization and Computer Graphics Lab Data Analytics 692 Subtractive color scheme • CMY color model is subtractive, i.e., adding colors makes the resulting color darker. • Application: color printers. • As it only works perfectly in theory, typically a black cartridge is added in practice (CMYK color model). Jacobs University Visualization and Computer Graphics Lab Data Analytics 693 CMY color cube • All colors c that can be generated are represented by the unit cube in the 3D Cartesian coordinate system. magenta blue red black grey white cyan yellow green Jacobs University Visualization and Computer Graphics Lab Data Analytics 694 CMY color cube Jacobs University Visualization and Computer Graphics Lab Data Analytics 695 CMY color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 696 CMYK color model Jacobs University Visualization and Computer Graphics Lab Data Analytics 697 Conversion • RGB -> CMY: • CMY -> RGB: Jacobs University Visualization and Computer Graphics Lab Data Analytics 698 Conversion • CMY -> CMYK: • CMYK -> CMY: Jacobs University Visualization and Computer Graphics Lab Data Analytics 699 HSV color model • While RGB and CMY color models have their application in hardware implementations, the HSV color model is based on properties of human perception. • Its application is for human interfaces. Jacobs University Visualization and Computer Graphics Lab Data Analytics 700 HSV color model The HSV color model also consists of 3 channels: • H: When perceiving a color, we perceive the dominant wavelength. -
Prepared by Dr.P.Sumathi COLOR MODELS
UNIT V: Colour models and colour applications – properties of light – standard primaries and the chromaticity diagram – xyz colour model – CIE chromaticity diagram – RGB colour model – YIQ, CMY, HSV colour models, conversion between HSV and RGB models, HLS colour model, colour selection and applications. TEXT BOOK 1. Donald Hearn and Pauline Baker, “Computer Graphics”, Prentice Hall of India, 2001. Prepared by Dr.P.Sumathi COLOR MODELS Color Model is a method for explaining the properties or behavior of color within some particular context. No single color model can explain all aspects of color, so we make use of different models to help describe the different perceived characteristics of color. 15-1. PROPERTIES OF LIGHT Light is a narrow frequency band within the electromagnetic system. Other frequency bands within this spectrum are called radio waves, micro waves, infrared waves and x-rays. The below figure shows the frequency ranges for some of the electromagnetic bands. Each frequency value within the visible band corresponds to a distinct color. The electromagnetic spectrum is the range of frequencies (the spectrum) of electromagnetic radiation and their respective wavelengths and photon energies. Spectral colors range from the reds through orange and yellow at the low-frequency end to greens, blues and violet at the high end. Since light is an electromagnetic wave, the various colors are described in terms of either the frequency for the wave length λ of the wave. The wavelength and frequency of the monochromatic wave is inversely proportional to each other, with the proportionality constants as the speed of light C where C = λ f.