Class TldTemplateMatching<T extends ImageGray<T>>


public class TldTemplateMatching<T extends ImageGray<T>> extends Object
Created NCC templates to describe the target region. Each template is composed of a 15x15 area. The descriptor is computed by sampling evenly spaced points through out the rectangular region. Confidence values are computed based in the distance a point is from the closest positive and negative template.
  • Field Details

  • Constructor Details

    • TldTemplateMatching

      public TldTemplateMatching(InterpolatePixelS<T> interpolate)
    • TldTemplateMatching

      protected TldTemplateMatching()
  • Method Details

    • reset

      public void reset()
      Discard previous results and puts it back into its initial state
    • setImage

      public void setImage(T gray)
      Must call this function before any of the others which process descriptions
      gray - Input image
    • addDescriptor

      public void addDescriptor(boolean positive, ImageRectangle rect)
      Creates a new descriptor for the specified region
      positive - if it is a positive or negative example
    • addDescriptor

      public void addDescriptor(boolean positive, float x0, float y0, float x1, float y1)
    • computeNccDescriptor

      public void computeNccDescriptor(NccFeature f, float x0, float y0, float x1, float y1)
      Computes the NCC descriptor by sample points at evenly spaced distances inside the rectangle
    • createDescriptor

      public NccFeature createDescriptor()
      Creates a new descriptor or recycles an old one
    • computeConfidence

      public double computeConfidence(int x0, int y0, int x1, int y1)
      Compute a value which indicates how confident the specified region is to be a member of the positive set. The confidence value is from 0 to 1. 1 indicates 100% confidence. Positive and negative templates are used to compute the confidence value. Only the point in each set which is closest to the specified region are used in the calculation.
      value from 0 to 1, where higher values are more confident
    • computeConfidence

      public double computeConfidence(ImageRectangle r)
      see the other function with the same name
    • distance

      public double distance(NccFeature observed, List<NccFeature> candidates)
      Computes the best distance to 'observed' from the candidate list.
      observed - Feature being matched
      candidates - Set of candidate matches
      score from 0 to 1, where lower is closer
    • getTemplatePositive

      public List<NccFeature> getTemplatePositive()
    • getTemplateNegative

      public List<NccFeature> getTemplateNegative()