anchor.diff:  
   *3. Java Layout as 1D Pixel Array*
   
  -The most common approach is to copy the backing array behind Java's `BufferedImage` class and to reshape the memory using MATLAB (compare this link:https://mathworks.com/matlabcentral/answers/100155-how-can-i-convert-a-java-image-object-into-a-matlab-image-matrix#answer_109503[answer]).
  +The most common approach for getting image data is to copy the backing array of Java's `BufferedImage` class and to reshape the memory using MATLAB (compare this link:https://mathworks.com/matlabcentral/answers/100155-how-can-i-convert-a-java-image-object-into-a-matlab-image-matrix#answer_109503[answer]).
   
   [source,matlab]
   ----
  
positive.code: 
  == Data Translation
  
  We compared five different ways to get image data from Java into MATLAB. Note that the benchmarks in this section measure only the overhead on the MATLAB main thread. The reason for this is that while additional overhead in a background thread may increase overall latency, it doesn't impact the available time budget for actual processing. The full benchmark code is available link:https://github.com/HebiRobotics/HebiCam/tree/benchmark[here].
  
  *1. Default 3D Array*
  
  
negative.code: * a standard cast calling `uint8(matrix)` doesn't work because it sets all negative values to zero
  * casting via `typecast(matrix, 'uint8')` only works on single dimension vectors
  
  Thus, in order to get a valid MATLAB image, we still need to do several operations.
  
  [source,matlab]
  ----
  