An Analysis and Implementation of the HDR+ Burst Denoising Method
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USER MANUAL AKASO Brave 6 Plus Action Camera
USER MANUAL AKASO Brave 6 Plus Action Camera V1.2 CONTENTS What's in the Box 1 Your Brave 6 Plus 2 Getting Started 4 Overview of Modes 5 Customizing Your Brave 6 Plus 8 Playing Back Your Content 15 Deleting Your Content 15 Connecting to the AKASO GO app 15 Offloading Your Content 16 Maintaining Your Camera 16 Maximizing Battery Life 17 Battery Storage and Handling 17 External Microphone 18 Remote 18 Mounting Your Camera 20 Contact Us 22 WHAT'S IN THE BOX Waterproof Handle Bar/ Brave 6 Plus Housing Pole Mount Mount 1 Mount 2 Mount 3 Mount 4 Mount 5 Mount 6 Mount 7 Mount 8 Charger 1 Helmet Mounts Battery Protective Backdoor Clip 1 Clip 2 Tethers Lens Cloth USB Cable Quick Start Guide AKASO Brave 6 Plus Action Camera Remote Bandages Quick Start Guide 1 YOUR BRAVE 6 PLUS 3 1 4 5 2 6 7 8 10 9 2 11 1 Shutter/Select Button 7 USB Type-C Port 2 Power/Mode/Exit Button 8 Micro HDMI Port 3 Up/Wifi Button 9 Touch Screen 4 Down Button 10 Battery Door 5 Speaker 11 MicroSD Slot 6 Lens Note: The camera does not record sound when it is in the waterproof case. 3 GETTING STARTED Welcome to your AKASO Brave 6 Plus. To capture videos and photos, you need a microSD card to start recording (sold separately). MICROSD CARDS Please use brand name microSD cards that meet these requirements: • microSD, microSDHC or microSDXC • UHS-III rating only • Capacity up to 64GB (FAT32) Note: 1. -
Real-Time Burst Photo Selection Using a Light-Head Adversarial Network
Real-time Burst Photo Selection Using a Light-Head Adversarial Network Baoyuan Wang, Noranart Vesdapunt, Utkarsh Sinha, Lei Zhang Microsoft Research Abstract. We present an automatic moment capture system that runs in real-time on mobile cameras. The system is designed to run in the viewfinder mode and capture a burst sequence of frames before and after the shutter is pressed. For each frame, the system predicts in real-time a \goodness" score, based on which the best moment in the burst can be selected immediately after the shutter is released, without any user interference. To solve the problem, we develop a highly efficient deep neural network ranking model, which implicitly learns a \latent relative attribute" space to capture subtle visual differences within a sequence of burst images. Then the overall goodness is computed as a linear aggre- gation of the goodnesses of all the latent attributes. The latent relative attributes and the aggregation function can be seamlessly integrated in one fully convolutional network and trained in an end-to-end fashion. To obtain a compact model which can run on mobile devices in real-time, we have explored and evaluated a wide range of network design choices, tak- ing into account the constraints of model size, computational cost, and accuracy. Extensive studies show that the best frame predicted by our model hit users' top-1 (out of 11 on average) choice for 64:1% cases and top-3 choices for 86:2% cases. Moreover, the model(only 0.47M Bytes) can run in real time on mobile devices, e.g. -
Lecture 26: Domain Specific Architectures Chapter 07, CAQA 6Th Edition
Lecture 26: Domain Specific Architectures Chapter 07, CAQA 6th Edition CSCE 513 Computer Architecture Department of Computer Science and Engineering Yonghong Yan [email protected] https://passlab.github.io/CSCE513 Copyright and Acknowledgements § Copyright © 2019, Elsevier Inc. All rights Reserved – Textbook slides § Machine Learning for Science” in 2018 and A Superfacility Model for Science” in 2017 By Kathy Yelic – https://people.eecs.berkeley.edu/~yelick/talks.html 2 CSE 564 Class Contents § Introduction to Computer Architecture (CA) § Quantitative Analysis, Trend and Performance of CA – Chapter 1 § Instruction Set Principles and Examples – Appendix A § Pipelining and Implementation, RISC-V ISA and Implementation – Appendix C, RISC-V (riscv.org) and UCB RISC-V impl § Memory System (Technology, Cache Organization and Optimization, Virtual Memory) – Appendix B and Chapter 2 – Midterm covered till Memory Tech and Cache Organization § Instruction Level Parallelism (Dynamic Scheduling, Branch Prediction, Hardware Speculation, Superscalar, VLIW and SMT) – Chapter 3 § Data Level Parallelism (Vector, SIMD, and GPU) – Chapter 4 § Thread Level Parallelism – Chapter 5 § Domain-Specific Architecture – Chapter 7 3 The Moore’s Law TrendSuperscalar/Vector/Parallel GPUs 1 PFlop/s (1015) IBM Parallel BG/L ASCI White ASCI Red 1 TFlop/s Pacific 12 (10 ) 2X Transistors/Chip TMC CM-5 Cray T3D Every 1.5 Years Vector TMC CM-2 1 GFlop/s Cray 2 Cray X-MP (109) Super Scalar Cray 1 1941 1 (Floating Point operations / second, Flop/s) 1945 100 CDC 7600 IBM 360/195 -
P1360R0: Towards Machine Learning for C++: Study Group 19
P1360R0: Towards Machine Learning for C++: Study Group 19 Date: 2018-11-26(Post-SAN mailing): 10 AM ET Project: ISO JTC1/SC22/WG21: Programming Language C++ Audience SG19, WG21 Authors : Michael Wong (Codeplay), Vincent Reverdy (University of Illinois at Urbana-Champaign, Paris Observatory), Robert Douglas (Epsilon), Emad Barsoum (Microsoft), Sarthak Pati (University of Pennsylvania) Peter Goldsborough (Facebook) Franke Seide (MS) Contributors Emails: [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] [email protected] Reply to: [email protected] Introduction 2 Motivation 2 Scope 5 Meeting frequency and means 6 Outreach to ML/AI/Data Science community 7 Liaison with other groups 7 Future meetings 7 Conclusion 8 Acknowledgements 8 References 8 Introduction This paper proposes a WG21 SG for Machine Learning with the goal of: ● Making Machine Learning a first-class citizen in ISO C++ It is the collaboration of a number of key industry, academic, and research groups, through several connections in CPPCON BoF[reference], LLVM 2018 discussions, and C++ San Diego meeting. The intention is to support such an SG, and describe the scope of such an SG. This is in terms of potential work resulting in papers submitted for future C++ Standards, or collaboration with other SGs. We will also propose ongoing teleconferences, meeting frequency and locations, as well as outreach to ML data scientists, conferences, and liaison with other Machine Learning groups such as at Khronos, and ISO. As of the SAN meeting, this group has been officially created as SG19, and we will begin teleconferences immediately, after the US thanksgiving, and after NIPS. -
SIMD in Different Processors
SIMD Introduction Application, Hardware & Software Champ Yen (嚴梓鴻) [email protected] http://champyen.blogspot.tw Agenda ● What & Why SIMD ● SIMD in different Processors ● SIMD for Software Optimization ● What are important in SIMD? ● Q & A Link of This Slides https://goo.gl/Rc8xPE 2 What is SIMD (Single Instruction Multiple Data) one lane for(y = 0; y < height; y++){ for(x = 0; x < width; x+=8){ //process 8 point simutaneously for(y = 0; y < height; y++){ uin16x8_t va, vb, vout; for(x = 0; x < width; x++){ va = vld1q_u16(a+x); //process 1 point vb = vld1q_u16(b+x); out[x] = a[x]+b[x]; vout = vaddq_u16(va, vb); } vst1q_u16(out, vout); a+=width; b+=width; out+=width; } a+=width; b+=width; out+=width; } } 3 Why do we need to use SIMD? 4 Why & How do we use SIMD? Image Processing Gaming Scientific Computing Deep Neural Network 5 SIMD in different Processor - CPU ● x86 − MMX − SSE − AVX − AVX-512 ● ARM - Application − v5 DSP Extension − v6 SIMD − v7 NEON − v8 Advanced SIMD (NEON) − SVE https://software.intel.com/sites/landingpage/IntrinsicsGuide/ http://infocenter.arm.com/help/topic/com.arm.doc.ihi0073a/IHI0073A_arm_neon_intrinsics_ref.pdf6 SIMD in different Processor - GPU ● SIMD − AMD GCN − ARM Mali ● WaveFront − Nvidia − Imagination PowerVR Rogue 7 SIMD in different Processor - DSP ● Qualcomm Hexagon 600 HVX ● Cadence IVP P5 ● Synopsys EV6x ● CEVA XM4 8 SIMD Optimization SIMD SIMD Framework / Software Hardware Programming Design Model 9 SIMD Optimization ● Auto/Semi-Auto Method ● Compiler Intrinsics ● Specific Framework/Infrastructure -
Intro to Digital Photography.Pdf
ABSTRACT Learn and master the basic features of your camera to gain better control of your photos. Individualized chapters on each of the cameras basic functions as well as cheat sheets you can download and print for use while shooting. Neuberger, Lawrence INTRO TO DGMA 3303 Digital Photography DIGITAL PHOTOGRAPHY Mastering the Basics Table of Contents Camera Controls ............................................................................................................................. 7 Camera Controls ......................................................................................................................... 7 Image Sensor .............................................................................................................................. 8 Camera Lens .............................................................................................................................. 8 Camera Modes ............................................................................................................................ 9 Built-in Flash ............................................................................................................................. 11 Viewing System ........................................................................................................................ 11 Image File Formats ....................................................................................................................... 13 File Compression ...................................................................................................................... -
Impact with Smartphone Photography
A Smartphone for Serious Photography? DSLR technically superior … but photo quality depends on technical skill, creative vision Smartphone cameras can produce remarkable pictures … always at ready … After all … The Best Camera is the One You Have With You Impact With Smartphone Photography 1 2 Smartphone Camera Handling To further avoid camera shake, release shutter with volume buttons …or use Apple headphones Cradle phone with both hands in “C” shape … keeps fingers away from lens … reduces camera shake 3 4 Keep Your Lens Clean Phone spends lot of time in pocket Master your or purse …grimy lens = poor image camera’s interface quality Explore …experiment with every control… Clean often with microfiber cloth … (breathe on lens to add moisture — every shooting mode no liquids) in built-in camera app Make small circles with soft pressure Set Phone Down With Camera Facing Up 5 6 Key Features: iPhone Standard Camera App AE/AF Lock Auto Burst Touch and hold finger on “Point and shoot”… automatic Take 10 photos in one second … screen … AE/AF Lock and focus and exposure perfect to capture action sun indicators appear Manual Focus High Dynamic Range (HDR) Tap a spot on screen to set focus Combines 3 different exposures to create one image with detail in both Exposure Adjust Exposure Adjust highlights and shadows Slide sun indicator up or Swipe up or down to make image down to make image brighter or darker lighter or darker Grid Grid Horizontal and vertical lines divide Helps keep horizons level, architectural lines Lock Focus & Exp (AE Lock) screen -
Nikon D5100: from Snapshots to Great Shots
Nikon D5100: From Snapshots to Great Shots Rob Sylvan Nikon D5100: From Snapshots to Great Shots Rob Sylvan Peachpit Press 1249 Eighth Street Berkeley, CA 94710 510/524-2178 510/524-2221 (fax) Find us on the Web at www.peachpit.com To report errors, please send a note to [email protected] Peachpit Press is a division of Pearson Education Copyright © 2012 by Peachpit Press Senior Acquisitions Editor: Nikki McDonald Associate Editor: Valerie Witte Production Editor: Lisa Brazieal Copyeditor: Scout Festa Proofreader: Patricia Pane Composition: WolfsonDesign Indexer: Valerie Haynes Perry Cover Image: Rob Sylvan Cover Design: Aren Straiger Back Cover Author Photo: Rob Sylvan Notice of Rights All rights reserved. No part of this book may be reproduced or transmitted in any form by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. For information on getting permission for reprints and excerpts, contact permissions@ peachpit.com. Notice of Liability The information in this book is distributed on an “As Is” basis, without warranty. While every precaution has been taken in the preparation of the book, neither the author nor Peachpit shall have any liability to any person or entity with respect to any loss or damage caused or alleged to be caused directly or indirectly by the instructions contained in this book or by the computer software and hardware products described in it. Trademarks All Nikon products are trademarks or registered trademarks of Nikon and/or Nikon Corporation. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. -
Computational Photography Research at Stanford
Computational photography research at Stanford January 2012 Marc Levoy Computer Science Department Stanford University Outline ! Light field photography ! FCam and the Stanford Frankencamera ! User interfaces for computational photography ! Ongoing projects • FrankenSLR • burst-mode photography • languages and architectures for programmable cameras • computational videography and cinematography • multi-bucket pixels 2 ! Marc Levoy Light field photography using a handheld plenoptic camera Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz and Pat Hanrahan (Proc. SIGGRAPH 2005 and TR 2005-02) Light field photography [Ng SIGGRAPH 2005] ! Marc Levoy Prototype camera Contax medium format camera Kodak 16-megapixel sensor Adaptive Optics microlens array 125µ square-sided microlenses 4000 ! 4000 pixels ÷ 292 ! 292 lenses = 14 ! 14 pixels per lens Digital refocusing ! ! ! 2010 Marc Levoy Example of digital refocusing ! 2010 Marc Levoy Refocusing portraits ! 2010 Marc Levoy Light field photography (Flash demo) ! 2010 Marc Levoy Commercialization • trades off (excess) spatial resolution for ability to refocus and adjust the perspective • sensor pixels should be made even smaller, subject to the diffraction limit, for example: 36mm ! 24mm ÷ 2.5µ pixels = 266 Mpix 20K ! 13K pixels 4000 ! 2666 pixels ! 20 ! 20 rays per pixel or 2000 ! 1500 pixels ! 3 ! 3 rays per pixel = 27 Mpix ! Marc Levoy Camera 2.0 Marc Levoy Computer Science Department Stanford University The Stanford Frankencameras Frankencamera F2 Nokia N900 “F” ! facilitate research in experimental computational photography ! for students in computational photography courses worldwide ! proving ground for plugins and apps for future cameras 14 ! 2010 Marc Levoy CS 448A - Computational Photography 15 ! 2010 Marc Levoy CS 448 projects M. Culbreth, A. Davis!! ! ! A phone-to-phone 4D light field fax machine J. -
Gables: a Roofline Model for Mobile Socs
2019 IEEE International Symposium on High Performance Computer Architecture (HPCA) Gables: A Roofline Model for Mobile SoCs Mark D. Hill∗ Vijay Janapa Reddi∗ Computer Sciences Department School Of Engineering And Applied Sciences University of Wisconsin—Madison Harvard University [email protected] [email protected] Abstract—Over a billion mobile consumer system-on-chip systems with multiple cores, GPUs, and many accelerators— (SoC) chipsets ship each year. Of these, the mobile consumer often called intellectual property (IP) blocks, driven by the market undoubtedly involving smartphones has a significant need for performance. These cores and IPs interact via rich market share. Most modern smartphones comprise of advanced interconnection networks, caches, coherence, 64-bit address SoC architectures that are made up of multiple cores, GPS, and many different programmable and fixed-function accelerators spaces, virtual memory, and virtualization. Therefore, con- connected via a complex hierarchy of interconnects with the goal sumer SoCs deserve the architecture community’s attention. of running a dozen or more critical software usecases under strict Consumer SoCs have long thrived on tight integration and power, thermal and energy constraints. The steadily growing extreme heterogeneity, driven by the need for high perfor- complexity of a modern SoC challenges hardware computer mance in severely constrained battery and thermal power architects on how best to do early stage ideation. Late SoC design typically relies on detailed full-system simulation once envelopes, and all-day battery life. A typical mobile SoC in- the hardware is specified and accelerator software is written cludes a camera image signal processor (ISP) for high-frame- or ported. -
Computational Photography & Cinematography
Computational photography & cinematography (for a lecture specifically devoted to the Stanford Frankencamera, see http://graphics.stanford.edu/talks/camera20-public-may10-150dpi.pdf) Marc Levoy Computer Science Department Stanford University The future of digital photography ! the megapixel wars are over (and it’s about time) ! computational photography is the next battleground in the camera industry (it’s already starting) 2 ! Marc Levoy Premise: available computing power in cameras is rising faster than megapixels 12 70 9 52.5 6 35 megapixels 3 17.5 billions of pixels / second of pixels billions 0 0 2005 2006 2007 2008 2009 2010 Avg megapixels in new cameras, CAGR = 1.2 NVIDIA GTX texture fill rate, CAGR = 1.8 (CAGR for Moore’s law = 1.5) ! this “headroom” permits more computation per pixel, 3 or more frames per second, or less custom hardware ! Marc Levoy The future of digital photography ! the megapixel wars are over (long overdue) ! computational photography is the next battleground in the camera industry (it’s already starting) ! how will these features appear to consumers? • standard and invisible • standard and visible (and disable-able) • aftermarket plugins and apps for your camera 4 ! Marc Levoy The future of digital photography ! the megapixel wars are over (long overdue) ! computational photography is the next battleground in the camera industry (it’s already starting) ! how will these features appear to consumers? • standard and invisible • standard and visible (and disable-able) • aftermarket plugins and apps for your camera -
Euphrates: Algorithm-Soc Co-Design for Low-Power Mobile Continuous Vision
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision Yuhao Zhu1 Anand Samajdar2 Matthew Mattina3 Paul Whatmough3 1University of Rochester 2Georgia Institute of Technology 3ARM Research Abstract Haar HOG Tiny YOLO 2 SSD YOLO Faster R-CNN 10 Continuous computer vision (CV) tasks increasingly rely on SOTA CNNs 1 convolutional neural networks (CNN). However, CNNs have 10 Compute Capability massive compute demands that far exceed the performance 0 @ 1W Power Budget and energy constraints of mobile devices. In this paper, we 10 propose and develop an algorithm-architecture co-designed -1 Scaled-down 10 CNN Better system, Euphrates, that simultaneously improves the energy- -2 10 efficiency and performance of continuous vision tasks. Hand-crafted Approaches -3 Our key observation is that changes in pixel data between 10 Tera Ops Per Second (TOPS) Tera 0 20 40 60 80 100 consecutive frames represents visual motion. We first propose Accuracy (%) an algorithm that leverages this motion information to relax the number of expensive CNN inferences required by contin- Fig. 1: Accuracy and compute requirement (TOPS) comparison be- uous vision applications. We co-design a mobile System-on- tween object detection techniques. Accuracies are measured against the widely-used PASCAL VOC 2007 dataset [32], and TOPS is based a-Chip (SoC) architecture to maximize the efficiency of the on the 480p (640×480) resolution common in smartphone cameras. new algorithm. The key to our architectural augmentation is to co-optimize different SoC IP blocks in the vision pipeline col- requirements measured in Tera Operations Per Second (TOPS) lectively. Specifically, we propose to expose the motion data as well as accuracies between different detectors under 60 that is naturally generated by the Image Signal Processor (ISP) frames per second (FPS).