Local image display

Open or close the local image display window. An Image Processing One Image tool supports the following image buffers:

• The Current.InputImage buffer contains the current image with any enclosing region graphics.
• The LastRun.InputImage buffer contains the result from the last execution of the tool.
• The LastRun.OutputImage buffer contains the results of the tool after it performs the image processing operations.

Open

Open a VisionPro persistence (.vpp) file that contains a set of saved properties for this vision tool object type. VisionPro reports an error if you try to open a .vpp file for another object type.

For more information about VisionPro persistence features, see the topic Persistence in VisionPro.

Add/Subtract Constant

Add a positive or negative value to the grey value of each pixel in the greyscale image, generating an image that is artificially lighter or darker than the original. Adding a value artificially brightens an image:

For input images of type CogImage24PlanarColor, values are added to Plane 0 (red), Plane 1 (green), and Plane 2 (blue). The following figure shows an example where the red component has been reduced by a high constant value:

You must also choose whether pixel values that fall below 0 or exceed 255 after the operation are allowed to wrap or be clamped to these limits. For example, in the case of a greyscale image, if you allow the values to wrap then a pixel with a grey value of 200 that has the value 100 added to it has the new value of 45 (200 + 100 - 255). If you choose to clamp the values then the same pixel does not exceed the value 255 after the operation.

Convolve 3x3

Convolve the input image with a 3x3 kernel of floating-point values. You can use this function, in conjunction with other image processing operators, to implement custom image processing operations, as shown in the following example:

Note that this operator always wraps output values rather than clamping them.

ConvolveNxM

Processing the image with a kernel of floating-point values of a size you specify. Use the NxM filter to perform a variety of image processing operations such as edge sharpening, edge detection, and edge softening, as shown in the following example:

Expand

Enlarge the entire image, or a portion of the entire image, by a magnification factor that you specify. The operation accepts separate parameters for enlarging the image along the x-axis and the y-axis, so you can use the operation to magnify the input image along one direction only. For example, the following figure shows an input image and the image after it has been magnified by a factor of 5:

Flip/Rotate

Perform a horizontal flip or a clockwise rotation on all or some portion of the input image. The following figure shows how a portion of an input image has been rotated 180 degrees:

You might need to flip or rotate an image so a vision tool analyzes the correct features each time the application executes, or to use the previously trained font characters of an OCV tool.

Gauss Sampler

Take a subsample of the input image so that the output image contains only a fraction of the original pixels, and smooth the input image by reducing the amount of contrast caused by frequent changes of light and dark pixels. For example, the following figure shows an image that has undergone a subsampling and smoothing operation:

Use a sampling operation when the vision tools you use work just as effectively on the reduced image and you want to increase the speed of your application. Use a smoothing operation to lessen the effect of liabilities such as texture, signal noise, or fine print in your images.

In addition to sampling and smoothing, this operation allows you to include a magnitude shift factor for the output image, with a range of -7 through 7. Using negative values for the shift factor produces darker output images, while using positive values produces lighter output images.

Grey Morphology

Perform grey scale morphology on the input image, selectively enhancing or reducing image features based on their size and orientation. See the section Grey-Scale Morphology for background information on this extensive image-processing operation. The following figure shows an image that has undergone a morphological operation:

High Pass Filter

You can perform a Gaussian, Mean or Median smoothing operation and then subtract the resulting image from the input image. The following figure shows an image after a High Pass filter has been applied:

Use a High Pass filter to accentuate fine features in the input image.

Median 3x3

Reduce the effect of image noise in the input image by examining the 3x3 matrix of pixels around each original pixel, ranking them by order of grey values, and then taking the fifth, or middle grey value, for the output image. The following figure shows the effect of the median 3x3 operation:

The 3x3 Median operation takes no parameters. Be aware, however, that this operation reduces the size of the input image by 2 rows and 2 columns, or what is effectively a one-pixel strip around the border of the input image. If you use multiple 3x3 Median operations on the same image, the effect of this reduction multiplies. For example, if you use five 3x3 Median operations on an image, the output image will be 10 rows and 10 columns smaller than the original image.

NxM Median

Reduce the effect of image noise in the input image by examining the matrix of pixels around each original pixel using a kernel of custom size. Larger kernels have a greater effect at reducing image noise but can reduce the quality of features in the image.

The NxM Median filter supports a masking kernel of Care and Don't Care pixels, allowing you to specify which elements of the matrix are not considered when generating the new grey value for the output image. Care pixels are enabled as shown in the following figure:

Missing Pixel

Substitute pixels of unknown value with pixels of a fixed value or pixels whose values are based on the analysis of the surrounding pixels. Pixel values can be unknown based on how they are acquired. For example, range images from a DS1000 series sensor can contain missing pixels.

Multiply Constant

Multiply the grey value of each pixel in the greyscale image by a constant value. For input images of type CogImage24PlanarColor, the values of Plane 0 (red), Plane 1 (green), and Plane 2 (blue) are multiplied by the multipliers you specify. If you specify a value between 0.0 and 1.0, it has the effect of dividing each pixel by a constant value, as shown in the following figure:

The following figure shows an example where the red component has been multiplied by a high value:

You must also choose whether resulting pixel values that fall below 0 or exceed 255 after the operation are allowed to wrap or be clamped to these limits. For example, if you allow the values to wrap then a pixel with a grey value of 200 that is multiplied by 2 has the new value of 145 (2 * 200 - 255). If you choose to clamp the values then the same pixel does not exceed the value 255 after the operation.

Pixel Map

Apply a pixel map to the input image. For greyscale input images, each pixel in the input image is replaced by a pixel with the value from the pixel map at the index that is equal to the input image pixel value. For images of type CogImage24PlanarColor, you can specify the individual pixel maps for Plane 0 (red), Plane 1 (green), and Plane 2 (blue). For example, if a greyscale input image pixel has a value of 73, it would be replaced by the value of the 73rd element of the pixel map. The following figure shows the effect of applying an inverted pixel map, where the pixel map contains values from 255 to 0:

The following figure shows the effect of applying an inverted Plane 0 (red) pixel map, where the Plane 0 pixel map contains values from 255 to 0:

Common applications for a pixel map include constructing mask images and performing customized image equalization.

Pixel Set

Selectively copy input image pixels to the output image or set output image pixel values to a user-defined value, based on which pixels in the input image have been classified as Care or Don't Care pixel values. In many applications the operator uses an image mask to define which pixels you want to pass/set between the input image and the output image.

Quantize

Reduce the number of discrete grey values in the input image. Performing a quantize operation can help separate individual features that have similar grey values, or reduce desired features to a single grey value, which can make them easier to analyze with other vision tools. The following figure shows the effect of the quantize operation:

When you select the quantize operation you must choose the number of discrete grey values the output image will contain.

Sample Convolve

Perform simultaneous separable convolution and sampling. A common use of this is downsampling with Gaussian smoothing. The following figure shows an image that has undergone a smoothing operation:

Subsampler

Generate an output image where the input image has been reduced in resolution and size. Subsampling can allow other vision tools to operate faster on the reduced image, although reducing the image size can result in less accuracy. The Subsampler operation provides two types of algorithms for generating the output image. The first algorithm divides the input image into blocks of pixels and copies the pixel at the center of the block into the output image. If the block contains an even number of rows or columns, the operation copies the upper-left pixel closest to the center of the block. The following figure demonstrates subsampling with a 3x3 block:

The second algorithm the Subsampler operation offers is spatial averaging, which divides the input image into blocks of pixels, determines the average grey value of the pixels in each block, and places that average into the output image. The following figure demonstrates spatial averaging with a 2x2 block:

Between the two algorithms, the subsampling operation executes faster, but the spatial averaging algorithm offers a higher degree of accuracy.

Note: If you specify an even number for the subsampling rate and you do not use spatial averaging, the tool selects the pixel above and to the left of the center of the sampling area. This introduces a one-half pixel shift in the locations of features in the sampled image. The tool automatically adjusts the output image coordinate space tree by shifting the coordinate space by one-half pixel. Because spatial averaging averages pixel values evenly across the sampling area regardless of its size, no such adjustment is performed when spatial averaging is enabled.

Add a new image processing operation. A menu appears with the list of image-processing operations. Highlight the operation you want and a new line appears in the list of image-processing operations at the bottom of the list.

You can add more than one operator of the same type. An Image Processing One Image tool with multiple operators exhibits the following behavior:

• The tool applies the operations to the input image in the order in which they appear in the edit control.
• The tool applies the input region to the input image prior to the first operation only.
• The entire rectangular output image from each operation is fed into any subsequent operation.
• The same operation can appear repeatedly in the list.

Value Computation

Choose the method for determining the value of each missing pixel:

• NeighborMinimum: Use the minimum greyscale value of the neighboring pixels, either vertically or horizontally, based on the setting for Direction.
• NeighborMaximum: Use the maximum greyscale value of the neighboring pixels, either vertically or horizontally, based on the setting for Direction.
• NeighborInterpolation: Calculate and use an average greyscale value of neighboring pixels in both the vertical and horizontal direction.
• Global: Use the method specified by Global Value Mode.

Direction

The direction in which missing pixel analysis is to be performed:

• Horizontal: By rows
• Vertical: By columns
• Both: Both vertically and horizontally, with the result derived from the results of both stages

Global Value Mode

Choose the manner in which the missing is determined when Value Computation is set to Global

• Fixed: Use the Fixed Global Value
• ImageMinimum: Use the minimum greyscale value of all valid pixels in the input image.
• ImageMaximum: Use the maximum greyscale value of all valid pixels in the input image.
• ImageAverage: Use the average greyscale value of all valid pixels in the input image.

Region Mode

Defines how the tool interprets the training region you specify. Choose either of the following options:

• Affine Transform is enabled only if you specify a CogRectangleAffine input region. If you select this option, the pixels are affine-transformed to create a new image before the operators are applied.

  All operators support this mode except:

  • High-Pass filter when processing CogImage16Range images.
  • Missing Pixel
  • Pixel Set

• Bounding Box specifies that pixels that lie within a pixel-aligned bounding box that encloses the region are processed.

  All operators support this mode.

• Masked Region specifies that pixels outside of the region are masked as "Don't Care" pixels

  Only the following operators support this mode:

  • Gauss Sampling
  • Grey Morphology NxM
  • Median NxM
  • Pixel Set

Region

Select the shape of the input region. Selecting "None=Use entire image" means that the tool uses the entire input image. An Image Processing One Image tool supports the following input region shapes:

• CogCircle
• CogEllipse
• CogPolygon
• CogRectangle
• CogRectangleAffine

The set of region-defining parameters that appear depend on the region shape you use. For more information on using a polygon as an input region, see the topic Using Polygon Input Regions.

The tool applies the input region to the input image prior to the first operation only.