Because of the internal use of 1D Edge Detection filters and Geometry 2D Fitting filters, all parameters known from them are also present in Shape Fitting filters interfaces.
More advanced methods can be used that consider not three, but four consecutive points and which employ additional techniques to assure the highest precision in presence of noise and other practical edge distortions.
It should be kept in mind that in the context of computer vision only the second point is important. Preparing images for human perception is not part of computer vision; it is only part of information visualization. In typical machine vision applications this comes only at the end of the program and usually does not pose any problem.
RegionOfInterest can be used where Region would be used. It provides easier defining and editing of regions. For example, you can define a rectangle inside a RegionOfInterest editor and it will be automatically converted to region on output.
The operation performs skeletonization presenting result as an array of paths.
Smallest axis-aligned rectangle containing the region (Box)
Smallest any-orientation rectangle containing the region (Rectangle2D)
Extraction - as the lightning in the image is uniform, the objects are consistently dark and the background is consistently bright, the extraction of the region corresponding to the objects is a simple task. A basic ThresholdToRegion filter does the job, and does it so well that no Refinement phase is needed in this example.
In this, idealized, example we analyze a picture of an electronic device wrapped in a rubber band. The aim here is to compute the area of the visible part of the band (e.g. to decide whether it was assembled correctly).
In the Modules tab there is a list of project modules which can participate in code generation. It is possible to choose any subset of modules existing in an Adaptive Vision Studio project, as long as this doesn't cause breaking the dependencies existing in them.
These filters return Nil after the time is out and no image has been acquired.
Another typical example is when one is trying to select an object having the maximum value of some feature (e.g. the biggest blob)
An example of a possibly erroneous situation is a use of the ThresholdToRegion filter followed by RegionMassCenter.
Domain Errors are reported in the Console window. You can highlight the filter instance that produced a Domain Error by clicking on the link that appears there
Programs created in Adaptive Vision Studio are fully backward compatible within the same release number.
So, how should you approach constructing a program, when you are accustomed to such programming constructs as loops, conditions and variables?
It turned out that there is a group of parameters, which is used more often than others.
Adaptive Vision Studio enforces that all connections between filters are clearly visualized, even if making them implicit would make programming easier in typical machine vision applications.
Labeling connections is only a way to visualize the program in a more convenient way and does not make its structure any simpler. It is the user's responsibility to keep it well organized anyway.
Additionally, there are several usability enhancements.
In bigger projects you will also find it useful to switch between three different layouts, which can be created to visualize different stages of the algorithm, as well as to the automatic layout mode, which is very useful for interactive analysis of individual filters
When a project is loaded from a file or when new filters are added, the filter instances are displayed subdued. They become highlighted after they are invoked (executed).
Another way to create connections between filters is by using only the Properties window.
Specifies the name of the category in which to group the property or event when displayed in a PropertyGrid control set to Categorized mode.
Analogous to the disposable collections is case of nullable objects (atl::Conditional
Even then, however, it is important to be aware what happens with the allocated data and when it actually is going to be freed. In case of memory allocated on the managed heap (or stack), being aware of how the memory is managed is mainly a matter of application performance. When it comes to dealing with unmanaged resources, memory management awareness is a matter of preventing memory leaks, which apart from performance impact may lead to the application abnormal behavior.
Most of the native AVL types have their counterpart in the AvlNet.dll assembly, which are native AVL type wrappers. Internally, a managed type contains a native pointer to the AVL type and because of those native pointers (unmanaged resources), objects of those types need to be disposed properly when not used any more to avoid memory leaks.
The library is compatible with Debian-like system, including - but not limited to - Ubuntu distributions.
The most convenient way to make programs with Adaptive Vision Library for Linux is to develop vision algorithm using Adaptive Vision Studio on Windows and then generating C++ code.