CS A485 Computer and Machine Vision

Transcription

1 CS A485 Computer and Machine Vision Lecture 1 Introduction Part-2 January 14, 2014 Sam Siewert

2 Biological Vision vs. Machine Vision (Why A Honey Bee is Better than HPC for CV) Humans million Photoreceptors 10 billion Neurons (Cerebral Cortex) Brain with 100 billion Neurons Millisecond Transfer Massively Parallel Analog + Digital Computation Synapse Match is a Challenge 7000 Connections from 10 Billion Neurons 3 Year Olds Have Synapses CPU to Digital Camera/HDD Connects 10 s of millions of pixels to Several Billion transistors Through Sequential Logic and I/O Bus Brain plasticity for learning, connectedness, concurrency, integrated sensing, power efficiency, and resiliency billion? 960K Neurons in flight: Learns locations, complex odors, colors, and shapes; with high efficiency (500 Watt/Kg), 0.218g NVIDIA GK110 28nm, (7.1 billion) Intel MICA 22nm (5 billion) Sam Siewert 2

3 How We ll Do It Assessment of Theoretical Learning Two Mid-term Exams (1/2 way, 7/8 way) FINAL Practice 5 Labs Application 1 Extended Lab with your Own Design Sam Siewert 3

4 Linux Lab and Desktop Options Native Linux Installation Ubuntu Logitech C200 or C270 Camera(s) OpenCV ffmpeg GIMP Transformer.uaa.alaska.edu available to all remotely and in A219 Virtual-Box Ubuntu Installation Beagle xm Ubuntu, Intel Terasic Atom Yocto Linux Sam Siewert 4

5 Administrivia Lectures PowerPoint with Camtasia Recorded on Wednesdays in ENGR 227C, Distributed via Blackboard by Thurs Morning Introductions Instructor (Office Hours) - Students (Introductions) Let s all join Google+ Circle (I will create and invite you) UAA Blackboard Personal Lab You MUST Have Native Linux and I recommend VB- Linux Too Either using your own Laptop Or Using A219 Lab at UAA UAA Beagle xm Linux Lab A219, Sam Siewert 5

6 Linux Digital Video and CV Processing Skills Introduction Session January 14, 2014 Sam Siewert

7 Basic Lab Observations CV is Compute Intensive Lower Resolution and Frame Rates (e.g. 640x480 or 320x240 at 30Hz) High-End is Really Intense (HPC) E.g Hz 4K Cameras like or Humans Seem to Saturate at 60Hz (Current Theory) 60Hz Stereo in HD is still a Massive Data Rate (1920x1080 x 3 bytes x 60 x 2), or about 720 MB/sec!! We will work at Low Resolution and 30Hz, but with Both 2D and 3D Both Binocular 3D, and RGB-Depth Sam Siewert 7

8 Tutorial CV Papers IBM DeveloperWorks Build a compute node or small cluster application and scale with HPC - Explore video analytics in the cloud - Machine data analytics - Sam Siewert 8

9 Labs I will POST to BB and External Website on Thursdays Read, Review, Start and Question that Weekend Bring Questions to Office Hours Mon, Tues, Wed the Following Week Lab Due one Week Later This Works Great if YOU Keep Up I will POST Lab #1 on 1/15/2014, Due on 1/26 for Full Credit, Accepted Late Until 1/30 (10% Penalty) Sam Siewert 9

10 OpenCV Demos Overview Session Passive Computer Vision Methods January 14, 2014 Sam Siewert

11 2D & 3D Passive Computer Vision 3D Disparity & Depth Map Canny Edge Finding 2D Skeletal Transform Analog Camera #1 LEFT (NIR, Visible) USB 2.0, PCIe Host Channels Linux with OpenCV (x86, TI OMAP, Atom) Storage Analog Camera #2 RIGHT (NIR, Visible) Linear Hough Transform Face Detection/Recognition Sam Siewert 11

12 OpenNI Overview Session Active Computer Vision Methods January 14, 2014 Sam Siewert

13 3D Active Computational Photometry TI DLP Light-crafter Kit IR Pattern Projection Photo credits and reference: Dr. Daniel Aliaga, Purdue University Analog Camera #2 (Near Infrared) Analog Camera #1 RGB (Visible) Altera FPGA CVPU (Computer Vision Processing Unit) Depth Map HD Digital Camera Port (Snapshot) USB 2.0, PCIe Host Channels Flash SD Card Networked Video Analytics Mobile Sensor Network Processor (TI OMAP, Atom) Sam Siewert 13

14 3D Computer Vision Transforms Long Range ( > 5 meters) Using Passive Binocular Methods Impractical to Project from a UAV or Long Range Observer Requires Image Registration Accurate Camera Intrinsic (Camera Characteristics) & Extrinsic (e.g. Baseline) Short Range ( < 5 meters), Structured IR Light Projection for RGB-D Compare to ASUS Xtion and PrimeSense Off-the-Shelf Robust Depth Maps with Less Noise Showing Significant Promise to Improve CV Scene Segmentation and Object Recognition Compared to 2D Change Their Perception, By Xiaofeng Ren, Dieter Fox, and Kurt Konolige, IEEE RAS, December Noise in Passive Depth Maps Robust Active Depth Map Change Their Perception, By Xiaofeng Ren, Dieter Fox, and Kurt Konolige, IEEE RAS, Sam Siewert December

15 Off-The-Shelf RGB-Depth Mappers Intel Creative Camera Windows Perceptual SDK ASUS Xtion Short and Long Range OpenNI PrimeSense (Kinect Old and New) MS SDK, ROS Sam Siewert 15

16 Summary Numerous MV and CV Applications Inspection and Process Automation MV Domain Interactive Systems and Augmented Reality CV Domain Robotics MV and CV Study of Human Vision and Vision Prosthetics CV 2D Image Processing (Machine Vision) Capture, Enhancement, Segmentation, Recognition Passive 3D Computer Vision Stereo Capture, Calibration, Enhancement, Registration, Depth Mapping, Segmentation, Recognition Active 3D Machine Vision (It s Cheating!) Structured Light Illumination and IR/Visible Capture, IR Analysis and Depth Mapping, Visible Image Registration Works Between 0 and 5 Meters Well Sam Siewert 16