Index

General

  1. Matching threshold
  2. A Matching threshold allows you to manipulate the recognition results. The similarity score is a comparative score which shows how likely the test image contains the object. A high similarity score suggests that test image contains the object. On the other hand, a low similarity score implies that test image displays noise.

    The matching threshold is selected experiment way because objects are very different. Default is 40000 and for concrete case it should be determined. Matching threshold can be in range 0 to 500 000.

  3. Enable mask enhancement
  4. Enables mask enhancement. The given mask can be extended in order to eliminate noise and small halls inside the object.

  5. Recognition speed
  6. Use tracking
  7. This option used during object recognition with test images If the option is marked the tracking of the object in a sequence of test images is enabled. Otherwise, if the option is unmarked the tracking is disabled and object recognition uses the other algorithm for comparison.

    This options should be marked only with a sequence of test images where the neighbouring images differ only slightly. Also, constant light conditions and constant background usually improve tracking results.

  8. Transform type
  9. If auto, selects best possible transform, if particular transform is selected but current condition do not allow to perform it simple transform is selected. Transform complexity: Similarity->Affine->Perspective.

Learning/Recognition

  1. Learning mode
  2. Feature type
  3. Recognition or learning features type.

  4. Shape scaling level
  5. Identifier specifying how many times to reduce image size to capture difference appearance of the shape duo to visual differences on smaller scales. This parameter can be applied only for shape mode. When using this parameter model size will be bigger and recognition speed slower.

    Range: 0 - all possible scaling; N (natural number) - how many reduced images to process (no error condition, just does not extract more than possible in case).

  6. Image rescale factor
  7. Identifier specifying how to rescale input image before processing. The image can be downsampled or increased. Features are stored in rescaled size.

  8. Use all CPU cores
  9. Identifier specifying to use all CPU cores to make parallel recognition.

  10. Number of CPU cores to use
  11. Specifies how many CPU cores to use for recognition.

Colors

  1. Active shape color
  2. Active shape color and model name, score in recognition mode.

  3. Inactive shape color
  4. Inactive shape color and matched object in recognition mode.