anchor.diff:  
   [.text-center]
   .System overview for a stereo vision setup
  -image::{imagesdir}/streaming/stereocam-matlab.svg[width=100%]
  +image::{imagesdir}/streaming/stereocam-matlab.svg[width=80%]
   
   Starting background threads and getting the video stream into Java was relatively straightforward. We used the link:https://github.com/bytedeco/javacv[JavaCV] library, which is a Java wrapper around link:https://opencv.org/[OpenCV] and link:https://www.ffmpeg.org/[FFMpeg] that includes pre-compiled native binaries for all major platforms. However, passing the acquired image data from Java into MATLAB turned out to be more challenging.
   
  
positive.code: Red |  >100% | >33.3 ms  |
  |====================
  
  //{set:cellbgcolor!}
  
  The two tables below show the results for converting color (RGB) images as well as grayscale images. All measurements are in milliseconds.
  
  
negative.code: 
  All benchmarks were run in MATLAB 2017b on an link:https://www.intel.com/content/www/us/en/products/boards-kits/nuc/kits/nuc6i7kyk.html[Intel NUC6I7KYK]. The performance was measured using MATLAB's `link:https://mathworks.com/help/matlab/ref/timeit.html[timeit]` function. The background color of each cell in the result tables represents a rough classification of the overhead on the main MATLAB thread.
  
  {set:cellbgcolor:white}
  
  [.text-center]
  .Color classification
  [width="50%",frame="topbot",options="header",cols="a,a,a"]
  |====================
  | Color |  Overhead | At 30 FPS |
  Green |  <10% | <3.3 ms | 
  