Robot Vision SS 2009 Matthias Rüther 1 ROBOT VISION Lesson 5: Camera Hardware and Technology Matthias Rüther

Robot Vision SS 2009 Matthias Rüther 2 Content  Camera Hardware –Sensors –Video Data Transfer –Mechanics  Optics –Lenses –Macroscopic –Telecentric –Microscopic  Illumination –Illumination systems –Mechanical Arrays

Robot Vision SS 2009 Matthias Rüther 3 Sensors  Goal: convert light intensity to electrical signal –Mostly visible light spectrum (~700nm to ~400nm) provides color information, light intensity, like human eye –Near infrared (~700nm to 5  m) Similar properties as visible light, NO heat information; black sky, plants are white, used for vegetation inspection, remote sensing, to detect reflective markers

Robot Vision SS 2009 Matthias Rüther 4 Sensors –Ultraviolet (~400nm to ~240nm) Used with special illumination, UV microscopy (resolution up to 100nm) surface inspection (detecting cracks, fluid leaks etc.) flame inspection (alcohol flames are barely visible to human eye) Forensics (finger print, blood, etc.)

Robot Vision SS 2009 Matthias Rüther 5 Sensors  2 Basic Technologies: Charge Coupled Device (CCD) CMOS Sensor (CMOS)  Both are pixelated metal oxide semiconducters  Accumulate in each pixel signal charge proportional to local illumination intensity => spatial sampling function

Robot Vision SS 2009 Matthias Rüther 6 Photon Sensing

Robot Vision SS 2009 Matthias Rüther 7 Charge Transport

Robot Vision SS 2009 Matthias Rüther 8 Read Out

Robot Vision SS 2009 Matthias Rüther 9 Full-Frame CCD

Robot Vision SS 2009 Matthias Rüther 10 Frame Transfer CCD

Robot Vision SS 2009 Matthias Rüther 11 Interline Transfer CCD

Robot Vision SS 2009 Matthias Rüther 12 CMOS vs CCD

Robot Vision SS 2009 Matthias Rüther 13 CMOS: Passive Pixel Sensor (PPS)

Robot Vision SS 2009 Matthias Rüther 14 CMOS: PPS with Column Amplifiers

Robot Vision SS 2009 Matthias Rüther 15 CMOS: PPS with Column Amplifiers

Robot Vision SS 2009 Matthias Rüther 16 CMOS: Active Pixel Sensor (APS)

Robot Vision SS 2009 Matthias Rüther 17 CMOS: APS Variations  On-Chip A/D  Column A/D

Robot Vision SS 2009 Matthias Rüther 18 CMOS: APS Variations  Pixel A/D

Robot Vision SS 2009 Matthias Rüther 19 CMOS Pixels  Passive Pixel –1T, 2 lines –high fill factor, high noise  Photodiode APS –3T, 4 lines –low fill factor, medium noise

Robot Vision SS 2009 Matthias Rüther 20 CMOS Pixels  Pinned Photodiode APS –4T, 5lines –Low fill, low noise, low full well –Correlated Double Sampling (CDS)  Pinned Photodiode (5T) –5T, 5lines –Low fill, low noise, low column FPN, low full well

Robot Vision SS 2009 Matthias Rüther 21 APS Pixel

Robot Vision SS 2009 Matthias Rüther 22 CCD vs CMOS

Robot Vision SS 2009 Matthias Rüther 23 CCD vs CMOS

Robot Vision SS 2009 Matthias Rüther 24 CCD vs CMOS

Robot Vision SS 2009 Matthias Rüther 25 Line Sensor

Robot Vision SS 2009 Matthias Rüther 26 Line Sensor

Robot Vision SS 2009 Matthias Rüther 27 Video Data Transfer  Transfer of image data from Camera to System Memory  Properties: –Transfer distance –Bandwidth / Framerate –Analog / Digital –Environment –Cost  Popular Digital Transfer Protocols: –USB 2.0 (480 Mbps) –IEEE1394 a/b (400 / 800 Mbps) –Gigabit Ethernet (1 / 10 Gbps) –Cameralink (2 / 4 / 5.5 Gbps)

Robot Vision SS 2009 Matthias Rüther 28 CameraLink  Serial Interface for digital image transfer.  Standardized!!!!!  Fast (up to 2.04 Gbps)  Not a High Volume Product -> expensive  Max 10m cable, no power provided  Physical Layer: Low Voltage Differential Signaling (LVDS); high- speed, low-power general purpose interface standard; known as ANSI/TIA/EIA-644, approved in March –350 mV nominal signal swing  Connection Channellink: developed by National Semiconducturs for flat panel displays, –28bit I/O, serialized 7:1 and transferred –Up to 2.04 Gbps  Cameralink specializes Channellink for video data transfer.

Robot Vision SS 2009 Matthias Rüther 29 CameraLink  Mode A: 2.04 Gbps, 1 ChannelLink (blue)  Mode B: 4.08 Gbps, 2 ChannelLink (blue). Requires 2 Connectors  Mode C: 5.44 Gbps, 3 ChannelLink (blue). Requires 2 Connectors

Robot Vision SS 2009 Matthias Rüther 30 IEEE 1394 (Firewire)  De-facto industrial standard, being replaced by GigE –Moderate volume product (Industrial cameras, Video Cameras, Webcams) –Consists of both hardware and software specification –Completely digital--no conversion to analog –Data rates of 100, 200, or 400 Mb per second (800Mbps by 1394b) –Flexible--supports daisy-chain and branching cable configurations –Inexpensive –Max 4.5m cable length –1394b may run over GOF (Glass Optical Fiber), hundreds of meters of cable length –Power provided by bus –Invented by Apple in mid 90‘s as LAN bus (100Mbps) –Development hampered by license fees in 1998 ($1 per port) –Since 1999 owned by 1394LA ($0.25 per unit) –Firewire remains trademark of apple.

Robot Vision SS 2009 Matthias Rüther 31 USB 2.0  Upcoming rival for IEEE1394 –Fast (480Mbps) –High volume (available on every PC) –Plug and Play –Emerged from USB 1.1 (1995) –Provides Power –5m cable length –Master-Slave Architecture (IEEE1394: Peer to Peer) –IEEE1394a is faster (10-70%), due to protocol architecture!

Robot Vision SS 2009 Matthias Rüther 32 GigE  Gigabit Ethernet –Fast (1 Gbps full duplex, 10 Gbps available soon) –Max Cable length: 100m –Carrier: copper, fiber optics, microwave –High volume (available on every PC) –Plug and Play –May be integrated in standard LANs –No power over cable (except PoE devices). –High power consumption of devices –No Quality of Service –No Isochronous transfer –Packet overhead

Robot Vision SS 2009 Matthias Rüther 33 Mechanics  Industrial cameras need to be ruggedized –Up to 90% humidity –-5 to +50 degrees Celsius –Harder requirements for outdoor/surveillance cameras  Common Sensor dimensions: –¼“ –1/3“ –½“ –2/3“ –1“  Mounting usually by ¼“ screws  Lens mount standards: C-mount and CS-mount; 1“ thread; differing by flange focal distance

Robot Vision SS 2009 Matthias Rüther 34 Optics … or how to calculate the focal length.  Lenses (or lens systems, a „compound“ lens) are used to project light rays on an image sensor.  If all rays originating from a distinct point of light intersect in one point on the image plane, a sharp image of this point is acquired.

Robot Vision SS 2009 Matthias Rüther 35 Lens Parameters  Magnification = size of image / size of object –E.g. size of object = 5cm; size of image = 5mm -> magnification = 0.1 –Depends on working distance (lens – object distance) -> impractical for standard lenses  Focal length = working distance * size of image / (size of object + size of image) –E.g. to capture a 1000m wide object from 500m on a CCD chip measuring 4.8x6.4mm, you need 3.2mm of focal length

Robot Vision SS 2009 Matthias Rüther 36 Lens Iris  The Iris limits the amount of light getting through the lens.  -> the image appears darker (avoids overexposure in bright scenes)  -> less lens area is used -> fewer lens errors are incorporated  -> sharpness is increased  Sharpness: theoretically impossible to focus 3D object, but: –Blurred points of some size appear sharp to human eye (e.g. on 35mm film, 1/30mm spots appear sharp) –-> „Depth of field“ –In practice: max. blurred spot is 1 pixel

Robot Vision SS 2009 Matthias Rüther 37 Lens Iris  Depth of field limits: –Wd = working distance –Bs = size of blur spot –I = amount of iris aperture –F = focal length e.g.: a 10mm wide object is imaged on a 1/3“ Megapixel CCD from a distance of 100mm, the blurred spot size is max. 5μm -> best f is 26.5mm, choose 25mm standard lens -> DOF=  0.08mm at full aperture -> DOF=  0.24mm at aperture = 4

Robot Vision SS 2009 Matthias Rüther 38 Lens types  Standard lenses: focal length from 5mm to 75mm –Adjustable/fixed focus –Adjustable/fixed Iris –Adjustable/fixed zoom (focal length)  Macro lenses –Near field imaging (wd ~75mm-90mm, dof ±0.06mm… ±5mm, magnification 0.14…8)  Telecentric lenses –Parallel projection, moving object towards lens does not change the image

Robot Vision SS 2009 Matthias Rüther 39 Lighting  Illumination is the most critical part in a machine vision system.  Small illumination changes may severely affect performance of vision algorithms.  If possible, adjust lighting conditions and keep them fixed!  Properties: –Intensity –Spectrum –Frequency (amplitude change: flicker, strobe) –Direction  Hazards: –Object: reflection, specularity, color, stray light, transparency, motion –Lamp: heat, flicker, stability, lifetime, size, power, speed

Robot Vision SS 2009 Matthias Rüther 40 Regulated Halogen Lamp Systems  Illumination by Quartz-Halogen lamps  High power output  Power control by Voltage regulation and adjustable shutter  Fiber optic light guidance to avoid heating  High power consumption (150W lamp)  Heavy DC power source necessary to avoid flicker  Lamp life: hrs

Robot Vision SS 2009 Matthias Rüther 41 Light Emitting Diodes  Possible to generate all primary colors  Bright White LED‘s possible (up to 5W per piece) -> Cooling  Life time: hrs  Low power consumption -> Small DC current source  Small/light housing  Fast strobe (time limited by driver circuit, down to 1μs pulses)  Packed in LED arrays

Robot Vision SS 2009 Matthias Rüther 42 Types of Illumination  Directional  Glancing  Diffuse

Robot Vision SS 2009 Matthias Rüther 43 Types of Illumination  Ring Light  Diffuse Axial  Brightfield/Backlight

Robot Vision SS 2009 Matthias Rüther 44 Types of Illumination  Darkfield  Structured Light (Line Generators)