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Nov 11 2011

GoPro HERO 2 in hand, now I just need time!

OK, so now I have a new GoPro HERO2 camera shooting 11MP stills at 2fps, I just need the time to go out and test it at our study sites.

First things first, this camera is shooting stills with relatively wide field of view (FOV) and we don't know what that is going to do to structure from motion computation.  The camera shoots in full 170º FOV in 11MP and full or medium 127º FOV at 8MP and 5MP.  Narrow, 90º FOV, options most similar (although still wider) than the other cameras used in our research, are only available in video mode.

Some initial tests with ground subjects on campus have produced somewhat positive results, I think it is too early to tell for sure.

More to follow, when I can get to it.

Oct 25 2011

A new contender: GoPro launches HD Hero2

This camera looks like it might be a great new camera to try in aerial and ground ecosynth work (note: 2 11 MP photos per second):

GoPro launches HD Hero2 helmet cam, announces video streaming Wi-Fi pack for winter

List of HD HERO2 Feature Enhancements:
• Professional 11MP Sensor
• 2x Faster Image Processor
• 2X Sharper Glass Lens
• Professional Low Light Performance
• Full 170º, Medium 127º, Narrow 90º FOV in 1080p and 720p Video
• 120 fps WVGA, 60 fps 720p, 48 fps 960p, 30 fps 1080p Video
• Full 170º and Medium 127º FOV Photos
• 10 11MP Photos Per Second Burst
• 1 11MP Photo Every 0.5 Sec Timelapse Mode
• 3.5mm External Stereo Microphone Input
• Simple Language-based User Interface
• Compatible with Wi-Fi BacPac™ and Wi-Fi Remote™
- Long Range Remote Control of up to 50 GoPro Cameras per Wifi Remote
- Wi-Fi Video/Photo Preview, Playback and Control via GoPro App
- Live Streaming Video and Photos to the Web

Jan 31 2011

Indoor / Outdoor "GPS" Tracking Camera

"A camera that remembers where you've been...even when you don't." That is the catch phrase for the newest in geo-aware digital cameras on the market, and the ploy / technology behind the advertisement has me very intrigued for the possibilities of computer vision enhanced ecology and remote sensing that it may enable.

While working on the ooo-gobs of other aspects of my current work (in other words, all that dissertation proposal and exam stuff), I wondered about the latest progress in GPS enabled digital cameras.  Generally, GPS positions tagged to images could be used to improve the computer vision, structure from motion process. Bundler is not enabled for this, but Noah Snavely suggests in his articles about Bundler that this would be possible.  I was thinking about how useful camera GPS positions would be as I was trying to subset out a set of photos to perform a pilot study of my phenology - color based analysis.  

After a brief web search, I came up with this puppy, the new Casio EXILIM EX-H20G shown here (image and source docs at http://exilim.casio.com/products_exh20g.shtml).  At first blush, it looks like a newer version of the EX-FS150 that we bought over the summer, but never used for much.

The kicker about the EX-H20G is the Hybrid GPS system that uses built-in accelerometers to track position when GPS signal is lost, for example when you go in a building ... or under a forest canopy...?  This a pretty new device and it is still relatively expensive (about $350) but the ability to track and geo-tag position when GPS signal is lost could prove to be very valuable for linking aerial and ground based 3D point clouds.  Unfortunately, a review of the manual indicates that continuous shooting mode is not available on this model, but it may be worth picking one up to see how it works.

Next then will be to soup up Bundler to use camera GPS positions to initialize that computationally dreadful bundle adjustment stage!

 

Sep 25 2010

Camera Exposure Calibration

Even though we have had great success with the Canon and Casio continuous shooting**, high speed cameras for getting high image overlap, we are still having issues with image exposure.  I purchased a Lastolite EzyBalance camera calibration card from Service Photo in Baltimore as way to systematically deal with these issues.

When in continuous shooting mode, the cameras make calculations for focus and exposure based on the first photo taken when the button is pressed (Canon SD4000 Camera Manual).  This means that all photos in the scene will have an exposure (under-, over-, or “correct”) based on the lighting conditions of the first photo.  We discovered this when first using these cameras on the Slow Sticks.  When attaching the camera to the underside of the plane frame, the camera is pointed up at the sky and sun.  When the continuous shooting mode is activated the camera records the light conditions as if it were receiving direct light from the sky/sun.  When the plane is turned right side-up less light is entering the lens and so the photos are underexposed, too dark.  If I mount the camera and activate continuous shooting mode with the camera pointed at the ground it will record lower lighting conditions than what will be observed at altitude above the canopy, so the photos are overexposed, too light.

I am still learning about how SIFT and computer vision work and we are just now at the point where we can start to test changes in camera settings, but based on some preliminary research I think it will be important to strive for consistent illumination among images.  SIFT is largely invariant to changes in illumination between images, so it should still be possible to match photos of the same place under slightly varying illumination conditions (Lowe 1999).***  Since the camera settings are consistent between photos, there should not be changes in feature illumination between photos for the same image collection, unless clouds move into the scene during the flight.  However, under- or over- exposed images may result in a reduction in the detection of image features.  Many things to be tested for sure, but I want to start with trying to achieve consistent image illumination.

Here are some simple examples of my backyard for illustration.  I used the open-source GIMP image editor to generate the image intensity histograms for each image.  The interpretation of image intensity histograms is somewhat subjective or scene based, but the examples below merely serve as illustration of the value of the cal panel. 

The image on the left is over-exposed and the image on the right is under-exposed.  Prior to setting the camera into continuous shooting mode I pointed the lens down at the shadow of my body at my feet and then out across my lawn, resulting in the over-exposed image at left.  For the right image I started with the camera pointed up at the sun and then down to my lawn, resulting in under-exposure.  The left histogram is so white that almost all values are at the far right end of the chart and are hard to see.  The right histogram has values clumped at the left side, representing darker values throughout the whole scene.  In either image it is difficult to make out features, for example the grass in shadow at the right side of the right image, and of course nothing is visible in the left image.

By photographing a fully illuminated grey calibration panel first I get a resulting image with much more natural looking and distributed color intensity, as can be seen in the image at right.  This more spread out histogram is interpreted as having more tonal variation.  While we still have lots to test about camera settings, the goal is that by using this cal panel prior to flight we will be able to achieve consistent photo illumination and exposure.  There are other panels with black, grey and white that can be used to deliberately cause images to be under or over exposed, e.g., the Lastolite XpoBalanceused by some to calibrate digital photos for portraits and also for calibrating the intensity of LiDAR beams (Vain et al. 2009).

OK, that’s enough.  It is too beautiful out to drag this post along any more!

 

** I just discovered a ‘low-light’ 2.5MP resolution camera setting that makes it possible to achieve 5 photos per second with the Canon SD4000, wow!  This has the effect of increasing the camera ISO which may result in grainy photos under high illumination and it is not possible to change that resolution setting.

*** Thanks to my Computational Photography course that I am taking this semester, my review of the Lowe SIFT paper for this post finally made sense! 

refs:

Lowe, D.G. 1999. Object recognition from local scale-invariant features. In International Conference
on Computer Vision
, Corfu, Greece, pp. 1150-1157.

Vain A., Kaasalainen S., Pyysalo U., Krooks A., Litkey P. Use of Naturally Available Reference Targets to Calibrate Airborne Laser Scanning Intensity Data. Sensors. 2009; 9(4):2780-2796.