http://www.emgu.com
Assembly: Emgu.CV (in Emgu.CV.dll) Version: 2.4.2.1777 (2.4.2.1777)
Perfroms object detection with increasing detection window.
Namespace: Emgu.CVAssembly: Emgu.CV (in Emgu.CV.dll) Version: 2.4.2.1777 (2.4.2.1777)
Syntax
C# |
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public Rectangle[] DetectMultiScale( Image<Bgr, byte> image, double hitThreshold, Size winStride, Size padding, double scale, int finalThreshold, bool useMeanshiftGrouping ) |
Visual Basic |
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Public Function DetectMultiScale ( _ image As Image(Of Bgr, Byte), _ hitThreshold As Double, _ winStride As Size, _ padding As Size, _ scale As Double, _ finalThreshold As Integer, _ useMeanshiftGrouping As Boolean _ ) As Rectangle() |
Visual C++ |
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public: array<Rectangle>^ DetectMultiScale( Image<Bgr, unsigned char>^ image, double hitThreshold, Size winStride, Size padding, double scale, int finalThreshold, bool useMeanshiftGrouping ) |
Parameters
- image
- Type: Emgu.CV..::..Image<(Of <(<'Bgr, Byte>)>)>
The image to search in
- hitThreshold
- Type: System..::..Double
Threshold for the distance between features and SVM classifying plane. Usually it is 0 and should be specfied in the detector coefficients (as the last free coefficient). But if the free coefficient is omitted (which is allowed), you can specify it manually here.
- winStride
- Type: System.Drawing..::..Size
Window stride. Must be a multiple of block stride.
- padding
- Type: System.Drawing..::..Size
[Missing <param name="padding"/> documentation for "M:Emgu.CV.HOGDescriptor.DetectMultiScale(Emgu.CV.Image{Emgu.CV.Structure.Bgr,System.Byte},System.Double,System.Drawing.Size,System.Drawing.Size,System.Double,System.Int32,System.Boolean)"]
- scale
- Type: System..::..Double
Coefficient of the detection window increase.
- finalThreshold
- Type: System..::..Int32
After detection some objects could be covered by many rectangles. This coefficient regulates similarity threshold. 0 means don't perform grouping. Should be an integer if not using meanshift grouping. Use 2.0 for default
- useMeanshiftGrouping
- Type: System..::..Boolean
If true, it will use meanshift grouping.