Computer and Machine Vision

Transcription

1 Computer and Machine Vision Lecture Week 12 Part-2 Additional 3D Scene Considerations March 29, 2014 Sam Siewert

2 Outline of Week 12 Computer Vision APIs and Languages Alternatives to C++ and OpenCV API for Top- Down Halide MATLAB with CV Toolbox Mathematica Additional 3D Scene Considerations Camera Depth of Field Scene Complexities Introduction to Structure from Motion Methods 3D Scene Capture to Point-Cloud-Models Receiver Operator Curves for Recognition Performance Sam Siewert 2

3 Quick Review We Need Camera Intrinsics (Optical and Detector Characteristics and Aberrations) and Extrinsics (Camera Mount Details) Calibration Provides Physical Scene Coordinate to Camera Pixel Coordinate Mapping, Correcting for Extrinsic and Intrinsic Aberrations and Errors Chessboard at Give Distance is a Good Calibration Target (Feature Based Alignment Summary in CV on Pages ) No Two Cameras (Even Same Make and Model, Have Identical Intrinsics) Sam Siewert 3

4 3D Review Read or Re-read Chapter 12 in OpenCV Once Calibrated, SIFT, RANSAC or Hough Can Be Used to Find Common Features (Keypoints) for Left/Right Correspondence and Disparity Map Alternatives to 2-Camera Stereopsis are Structure from Motion (Like a Mosaic, Viewpoints from One Camera Over Time) or Active Depth Mappers Sam Siewert 4

5 Directed Reading Passive Depth Maps - Learning OpenCV, Pages Structure from Motion Learning OpenCV, Pages D Feature Alignment, CV Pages CV Chapter 7 Structure from Motion Sam Siewert 5

6 Passive 3D Confusion Specular Reflection Mirrored Images and Orientation Confusion M.C. Escher Camera Depth of Field Lightfield Camera and 3D Many Viewing Angles at Once Lytro Raytrix Refraction Bending of Light Through Materials Sam Siewert 6

7 Specular Reflections Has Caused Pilot Controlled Flight into Ground Multiple Correspondence and Confusion Problem for Passive Depth Mapping Using Features Sun Angle and Shadows Can Help (3D Depth Cue for Photogrammetry) Sam Siewert 7

8 Camera Lens is Not Really a Point Depth of Field Objects Too Near are Out of Focus Objects Too Far are out of Focus Sam Siewert 8

9 Scene Complexity - Refraction Refraction: Snell s Law Glass, Water and Other Translucent Materials Ration of Indices of Refraction and Velocity of Light in Each Medium Consider Ray Entry and Exit from Medium E.g. Air to Glass to Air Sam Siewert 9

10 Vanishing Points CV Pages , For Computation CV Pages , For Use in Calibration A Feature of Perspective Projection as We Saw Earlier Sam Siewert 10

11 Active Depth Mapper Alternative Project Visible or Infrared Light Onto Scene (Even X-ray in Case of CT) Measure Deformation of Known Pattern Pattern is Perfect and Scales on Flat Surface On Textured Surfaces (With Depth) Line Curvature Laser Dot Size and Shape Changes Much Like Intrinsic Calibration, But Now Surface Depth Causes Abberations Computational Expensive, But ASIC Can Accelerate (E.g. PrimeSense) Most Methods are Proprietary API for Active Depth Mapper Applications - OpenNI Sam Siewert 11

12 Building an Active Depth Mapper 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 12

13 Point Clouds 3D Scanners 3D Scanner Active 3D Depth Mapper Creates a CAD Model of an Object Most Often Close Range With Observation of Projected Light (Energy Up to X-ray) Camera Automatically Rotates or Tilts/Pans 360 Degrees in Increments to Photo document a Space Or Object is Rotated on a Turntable With Active Depth Mapper We Have Depth Map from Many Viewpoints E.g. CT Scan Computer Tomography (Typically X-ray for Medical) Spiraling X-Ray of Human Body Provides Point Cloud Model of Skeleton By Stacking 2D Images (Thin slices) Built on Principals Studied, But More of Machine Vision Automation Process PCL Software 3D Scanner and 3 Printer = 3D Replicator Sam Siewert 13

14 Receiver Operator Curves Feature Detection and Matching is Not Perfect (Approximate) CV Pages , Learning OpenCV Early Radar Operation Lead to Term Receiver Operator Curve False Positives True Positive Rate = (Total False)/Total False Negatives Detection Performance True Positive False Positive False Negative True Negative Captures Concepts of False Alarm (Cost to Clear) and Detection Failures (E.g. Cancerous Cell is Missed) For Non-Perfect Detectors, True Positive Rate Performance Increases Require Trade-off with Higher False Positive Rates Alternate Machine Learning Measure (Related Directly) is a Confusion Matrix (True-True, False-True, True-False, False-False) Sam Siewert 14

15 True Positive Rate RoC and 2 Class Confusion As Discussed in Learning OpenCV on Page % 75% RoC Confusion Matrix (E.g. 75% predicted T, actually T. etc) Actual Predicted T F T 75% 25% F 25% 75% 25% False Positive Rate 100% Sam Siewert 15