Here is a short run-down of what happened: On Monday, December 29th, 2014, I went down to the Frankfurt central train station (Hauptbahnhof) with my wife and friends to go ice skating at the Eissporthalle Frankfurt. We took a Deutsche Bahn (DB) train from Giessen, Germany to Frankfurt, Germany, departing at 3:22 PM and
 arriving at 4:02 PM. At the beginning of the trip, I placed my backpack on the 
luggage rack above the seat. About 10 minutes after exiting the train, after having taken an S-Bahn train to Hauptwache, I realized I did not have my bag. I immediately took another S-Bahn back to Frankfurt Hbf, ran up to the train platform, and found my train still waiting at the track (total time from departing the train to returning was approximately 20 minutes). I quickly searched the train because it was about to leave to head northward, but the backpack was gone.

I immediately notified the DB lost property office (Fundbüro) and made a 
claim. I also notified the police but will need to wait 1 week before I can officially file a police report (which I plan on doing on Monday if the bag has not been found).

Unfortunately, I don’t know how much identifying information was in the backpack (it did not contain any IDs), but in the future, I will make sure to include contact information on the camera to help the person who finds my things to reach out to me, if this were to ever happen again.

Here is an incomplete inventory:

• The North Face Hot Shot backpack in red and black
• Case Logic SLRC-202 SLR Camera Bag
• Canon T3i body with memory cards (serial #162037009896)
• Canon EF 50mm f/1.4 USM lens
• Sigma 18-250mm f3.5-6.3 DC MACRO OS HSM lens
• Quite a few other personal effects (that I am omitting in the hope that the bag is found so that I can provide some form of identification)

Here are some representative images of the items lost (the backpack at the top of the page the actual bag stolen):

To capture it, I setup a tripod inside, facing out an (admittedly) dirty window, disabled the len’s optical stabilization, turned on the T3i’s 3x digital crop zoom, and composed the shot. Only post-processing was to speed up the captured clip because the original was ~10 minutes long, shifting the movie to vertically center the moon’s precession, and adding an ambient background track. I’m impressed with what I was able to capture with such an inexpensive lens.

The FoodieTV app, from Glam Media, took advantage of the open nature of the licensing and just included my video in their latest episode (S2E12):

If our kitchen had time-lapse, we'd be eating “Currant Puff Pastries” now! S2E12 http://t.co/XDBmTddpNM cc @semifluid pic.twitter.com/LYNM7G7wqt

— FoodieTV (@foodietv) June 19, 2014

Cool! FoodieTV is a free app on the Apple iTunes Store (unsolicited shout-out and thank you for the free exposure).

There are a number of GoPro editions, but the newest one are the Hero3+ Silver and Black. The Silver Edition is very similar to the GoPro Hero3+ Black Edition (Amazon), but omits a few features, including some very high-resolution video recording modes and a feature that GoPro calls “Superview”. This post talks about a way that I attempted to emulate their Superview mode in MATLAB and put together an adaptive aspect ratio function that allows one to change the image’s aspect ratio while maintaining “safe regions” with minimal distortion.

This function allowed me to resize 4:3 images and video, like this one:

To a wider aspect ratio, for example 16:9:

Click through for info on how I implemented the code.

Let’s say we have an original, 4:3 image or frame from a camera (GoPro in this instance):

If we want to convert it to 4:3 format, we have a couple of options. The simplest is cropping out the top and bottom of the image:

The issue with cropping is that it cuts down on the vertical field of view. We could alternatively scale the image in the horizontal direction (or compress in the vertical, the effect is the same):

As you can see, scaling causes distortion in the image that can be distracting, especially when there are known points of reference (like people) in the frame.

An alternative to cropping or linear scaling, is the GoPro Hero3+ Black’s Superview mode, which is intended to non-linearly scale the image to retain as much vertical image information while attempting to minimize the perceived scaling distortion, especially in the center of the image frame.

Here is a description of the GoPro Superview feature, according to GoPro:

SuperView is a new feature introduced with HERO3+ Black Edition which allows you to capture an immersive wide angle perspective. What this mode does is it takes a 4:3 aspect ratio and dynamically stretches it to a 16:9 aspect ratio. This can be a great choice because it uses the height of the camera’s sensor that you get with 4:3 meaning that you will see more of the sky and ground assuming you are pointed at the horizon. … The way it works is that the camera automatically stretches out the sides of the video to fit into the 16:9 frame. The center of the frame is unchanged, only the edges are adjusted.

This sounds as though there is 1:1 mapping of pixels in the center of the frame with gradual distortion/interpolation towards the right and left edges. This would maintain the total field of view and information content in the image, while attempting to conform to a widescreen aspect ratio.

Abe Kislevitz has a nice discussion of 4:3 to 16:9 footage conversion with the GoPro and specifically discusses a plugin called Elastic Aspect. I chose to do a similar transformation in MATLAB to see if I could reproduce the Superview effect in “post production” (after an image or video has been captured).

To do this, I created a 1-Dimensional mapping of the input pixels to the output pixels along the y-axis and remapped the locations of pixels that were not linearly mapped to “Safe Zones’ using cubic spline interpolation.

Let’s say I want to stretch GoPro image of my wife and I from 4:3 to 16:9 widescreen aspect ratio. Here’s the original 4:3 image:

Using linear interpolation, this would be the expected mapping (and resulting image). Note the distortion in our faces:


Created using the following code (from the below function):

[code lang=matlab]
outImage = bSplineResizeWidth([1080,1920], inImage, [50 50]);
[/code]

With cubic spline mapping with a very small “Safe Zone” that is linearly mapped (5% of the image width), we would have this mapping and resulting image:


Created using the following code (from the below function):

[code lang=matlab]
outImage = bSplineResizeWidth([1080,1920], inImage, [47.5 52.5]);
[/code]

Notice the difference in perceived quality already? Now, let’s protect the center 25% of the image:


Created using the following code (from the below function):

[code lang=matlab]
outImage = bSplineResizeWidth([1080,1920], inImage, [37.5,62.5]);
[/code]

Looking good! Let’s say our subject is off to the side of the image, we can explicitly choose those regions to “protect” by ensuring a 1:1 mapping to avoid any distortion for our subject. For example, here’s a picture of my cousin’s husband, located slightly off-center:

I can explicitly select a “Safe Zone” around his body to avoid distorting him further. Here’s the remapping and resulting image:


Created using the following code (from the below function):

[code lang=matlab]
outImage = bSplineResizeWidth([1080,1920], inImage, [], 1);
[/code]

So, although there is still inevitable distortion around the outside of the image, it seems as this may be an effective way to emulate GoPro’s Superview mode and perform an adaptive aspect ratio adjustment.

For the sake of discussion, there is another method called seam carving (commonly known as content-aware scaling with Adobe Photoshop), but this can introduce some artifacts, depending on the scene. It is also quite slow. Here is an example implementation in MATLAB by Danny Luong: Seam Carving for content aware image resizing: GUI implementation demo. In general, it appears as though seam carving is better suited for images rather that videos (note that the original authors of the algorithm developed a similar method for processing video):

Here is the code for both procedures:

In addition, I tried recompressing the image to see the effect of repeated JPEG compression on the output. An initial image is loaded, it is compressed, then that image is loaded and compressed at a lower quality, then that image is loaded and compressed at an even lower quality, and so on and so forth. Here’s the output at 10 frames per second when we do this from the least amount of JPEG compression (‘Quality’,100) to the most amount of compression (‘Quality’,0):

Just to be artistic, we can then repeat the cycle 10x and observe the effect of repeated compression. Here is an example at 30 frames per second with 10 loops of the repeated recompression:

Click through for the MATLAB code and a couple more example videos.

Here is the MATLAB code for illustrating increasing compression:

And two more examples:

Here is the MATLAB code for illustrating repeated compression:

And two more examples:

And finally, here is the MATLAB code for illustrating repeated compression (repeated 10 times):

And two final examples:

The driver boards utilize 3 AMC7135 constant current ICs, which supply 350 ma each, wired in parallel. I picked the driver boards from DealExtreme, but judging from the forum comments on the page, I’m not going to expect these to have very tight tolerances. I could have picked up 1400 ma boards, but the increase in brightness is negligble for the 33% increase in power drawn. Once I pull together MOSFETs to drive them I should be good-to-go.

I also finally mounted the K2 emitters on aluminum breakout boards/heatsinks that I purchased from SparkFun, although admittedly the SparkFun boards are way too expensive at $2.95 each. DealExtreme sells similar boards for $1.91 for a 5 pack, but they appear to be too small for Luxeon K2s… I purchased the SparkFun breakout boards using a gift certificate that I won from Hacked Gadgets, so I’m happy (thanks Alan!).

Continue to the post for more pictures.

Continue to the post for more pictures.

The next destination was Meads Bay.