Statistical operations, and blob anaylsis

`vision.Autocorrelator` |
Compute 2-D autocorrelation of input matrix |

`vision.BlobAnalysis` |
Properties of connected regions |

`vision.Crosscorrelator` |
2-D cross-correlation of two input matrices |

`vision.LocalMaximaFinder` |
Find local maxima in matrices |

`vision.Maximum` |
Find maximum values in input or sequence of inputs |

`vision.Mean` |
Find mean value of input or sequence of inputs |

`vision.Median` |
Find median values in an input |

`vision.Minimum` |
Find minimum values in input or sequence of inputs |

`vision.StandardDeviation` |
Find standard deviation of input or sequence of inputs |

`vision.Variance` |
Find variance values in an input or sequence of inputs |

2-D Autocorrelation | Compute 2-D autocorrelation of input matrix |

2-D Correlation | Compute 2-D cross-correlation of two input matrices |

2-D Histogram | Generate histogram of input or sequence of inputs |

2-D Maximum | Find maximum values in input or sequence of inputs |

2-D Mean | Find mean value of each input matrix |

2-D Median | Find 2-D Median value of each input matrix |

2-D Minimum | Find minimum values in input or sequence of inputs |

2-D Standard Deviation | Find standard deviation of each input matrix |

2-D Variance | Compute variance of input or sequence of inputs |

Blob Analysis | Compute statistics for labeled regions |

Find Local Maxima | Find local maxima in matrices |

PSNR | Compute peak signal-to-noise ratio (PSNR) between images |

This example shows how to use a combination of basic morphological operators and blob analysis to extract information from a video stream.

This example shows how to use morphological operations to count objects in a video stream.

**Find the Histogram of an Image**

The Histogram block computes the frequency distribution of the elements in each input image by sorting the elements into a specified number of discrete bins.

**Correct Nonuniform Illumination**

Use the Opening block to correct for uneven lighting in an image.

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