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Compute 2-D autocorrelation of input matrix

Statistics

`visionstatistics`

The 2-D Autocorrelation block computes the two-dimensional autocorrelation
of the input matrix. Assume that input matrix A has dimensions (*Ma*, *Na*).
The equation for the two-dimensional discrete autocorrelation is

$$C(i,j)={\displaystyle \sum _{m=0}^{(Ma-1)}{\displaystyle \sum _{n=0}^{(Na-1)}A(m,n)\cdot conj(A(m+i,n+j))}}$$

where $$0\le i<2Ma-1$$ and $$0\le j<2Na-1$$.

The output of this block has dimensions $$(2Ma-1,2Na-1)$$.

Port | Input/Output | Supported Data Types | Complex Values Supported |
---|---|---|---|

Input | Vector or matrix of intensity values or a scalar, vector, or matrix that represents one plane of the RGB video stream | Double-precision floating point Single-precision floating point Fixed point 8-, 16-, 32-bit signed integer 8-, 16-, 32-bit unsigned integer
| Yes – |

Output | Autocorrelation of the input matrix | Same as Input port | Yes |

If the data type of the input is floating point, the output of the block has the same data type.

The following diagram shows the data types used in the 2-D Autocorrelation block for fixed-point signals.

You can set the product output, accumulator, and output data types in the block mask as discussed in Parameters.

The output of the multiplier is in the product output data type if at least one of the inputs to the multiplier is real. If both of the inputs to the multiplier are complex, the result of the multiplication is in the accumulator data type. For details on the complex multiplication performed, refer to Multiplication Data Types.

**Rounding mode**Select the Rounding Modes for fixed-point operations.

**Saturate on integer overflow**Select the overflow mode for fixed-point operations. See Precision and Range.

**Product output**Specify the product output data type. See Fixed-Point Data Types and Multiplication Data Types for illustrations depicting the use of the product output data type in this block:

When you select

`Same as input`

, these characteristics match those of the input to the block.When you select

`Binary point scaling`

, you can enter the word length and the fraction length of the product output, in bits.When you select

`Slope and bias scaling`

, you can enter the word length, in bits, and the slope of the product output. The bias of all signals in the Computer Vision Toolbox™ software is 0.

**Accumulator**Use this parameter to specify how to designate the accumulator word and fraction lengths. Refer to Fixed-Point Data Types and Multiplication Data Types for illustrations depicting the use of the accumulator data type in this block. The accumulator data type is only used when both inputs to the multiplier are complex.

When you select

`Same as product output`

, these characteristics match those of the product output.When you select

`Same as input`

, these characteristics match those of the input to the block.When you select

`Binary point scaling`

, you can enter the word length and the fraction length of the accumulator, in bits.When you select

`Slope and bias scaling`

, you can enter the word length, in bits, and the slope of the accumulator. The bias of all signals in the Computer Vision Toolbox software is`0`

.

**Output**Choose how to specify the output word length and fraction length.

When you select

`Same as input`

, these characteristics match those of the input to the block.When you select

`Binary point scaling`

, you can enter the word length and the fraction length of the output, in bits.When you select

`Slope and bias scaling`

, you can enter the word length, in bits, and the slope of the output. The bias of all signals in the Computer Vision Toolbox software is`0`

.

**Lock data type settings against change by the fixed-point tools**Select this parameter to prevent the fixed-point tools from overriding the data types you specify on the block mask. For more information, see

`fxptdlg`

, a reference page on the Fixed-Point Tool in the Simulink^{®}documentation.

Computer Vision Toolbox | |

Computer Vision Toolbox | |

Computer Vision Toolbox | |

Computer Vision Toolbox | |

Computer Vision Toolbox | |

Computer Vision Toolbox | |

2-D Maximum | Computer Vision Toolbox |

2-D Minimum | Computer Vision Toolbox |