It so happened that from time to time during the past two years I was working on several research projects in the areas of Computer Vision and Artificial Intelligence. As a result of this work, a lot of code was produced and several articles on Code Project were published that describe some of these areas. Publishing these articles, I discovered that these areas are interesting not only to me, but to a wide range of developers as well. From the time of my first publication on Code Project, I received many different e-mails from many interesting people all over the world, who were applying some of my code to a great number of applications.
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Keyword Index. GetMessageString Method. ConnectionFailedException Constructor. ConnectionLostException Constructor. DeviceBusyException Constructor. DeviceErrorException Constructor. DoublePoint Constructor. DoublePoint Fields X Field. Y Field. Add Method DoublePoint, Double. Addition Method Addition Operator. Addition Operator. DistanceTo Method. Divide Method. Division Operator. Equality Operator.
Equals Method. EuclideanNorm Method. Explicit Method Explicit Operator. Explicit Operator. GetHashCode Method. Inequality Operator. Multiply Method. Multiply Operator. Round Method. SquaredDistanceTo Method. Subtract Method DoublePoint, Double. Subtraction Method Subtraction Operator.
Subtraction Operator. ToString Method. DoubleRange Constructor. DoubleRange Methods Equality Operator. IsInside Method Double. IsOverlapping Method. ToIntRange Method. DoubleRange Properties Length Property. Max Property. Min Property. IntPoint Constructor. IntPoint Fields X Field. Add Method IntPoint, Int Implicit Method Implicit Operator. Implicit Operator. Subtract Method IntPoint, Int IntRange Constructor. IntRange Methods Equality Operator. IsInside Method Int IntRange Properties Length Property.
MessageTransferHandler Delegate. NotConnectedException Constructor. Parallel Class Parallel Members. Parallel Methods For Method. Parallel Properties ThreadsCount Property. ForLoopBody Delegate. Point Structure Point Members. Point Constructor.
Point Fields X Field. Add Method Point, Single. Subtract Method Point, Single. PolishExpression Methods Evaluate Method. Range Structure Range Members. Range Constructor. Range Methods Equality Operator. IsInside Method Single. Range Properties Length Property. ThreadSafeRandom Constructor Int Next Method Int Next Method Int32, Int NextBytes Method. NextDouble Method. Chart Constructor. SeriesType, Int SeriesType, Int32, Boolean.
Dispose Method. RemoveAllDataSeries Method. RemoveDataSeries Method. UpdateDataSeries Method. Chart Properties RangeX Property. RangeY Property. SeriesType Enumeration. ColorSlider Constructor. ColorSlider Methods Dispose Method. EndColor Property.
FillColor Property. StartColor Property. Type Property. ColorSliderType Enumeration. Histogram Class Histogram Members. Histogram Constructor. Histogram Methods Dispose Method. OnPaint Method. Histogram Properties AllowSelection Property.
Color Property. IsLogarithmicView Property. IsVertical Property. Values Property. Histogram Events PositionChanged Event. SelectionChanged Event.
Position Property.
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With the advances we've made in computing power today, we now have more choices than ever before on how we input data into our PCs. Much of this drive is coming about as a result of the ever growing maker culture that small home automation kits such as the Arduino and Raspberry PI have brought about. Many IoT tinkerers are now days playing with amateur robotics and home automation, creating some very interesting projects. NET is a complete Artificial Intelligence framework for. NET developers, allowing software creators to create neural networks, computer vision, and semi-autonomous statistical analysis. One of the more fascinating aspects of the tool kit are the classes and assemblies dedicated to computer vision. In this post, we're going to create a simple Windows Forms application that will use your computer's web cam to turn your PC into a motion detection system.
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NET framework provides neural networks library, which contains set of classes aimed for creating different type of artificial neural networks and training them to solve certain tasks, like recognition, approximation, prediction, etc. The library mainly allows to create two categories of artificial neural networks: Feed forward neural networks with activation function. This type of networks is represented as by one layer, as by multi layer networks, which don't have recurrent connections - information flaws in these networks from inputs to outputs passing all layers of neural network only one time without doing loops. Neurons of such networks calculate their output by calculating weighted sum of their inputs and passing it to activation function, which value becomes an output of neuron.
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