code: protected void learnAmbiguousNegative(Rectangle2D_F64 targetRegion) {
  
  		TldHelperFunctions.convertRegion(targetRegion, targetRegion_I32);
  
  		if( detection.isSuccess() ) {
  			TldRegion best = detection.getBest();
  
  			// see if it found the correct solution
  			double overlap = helper.computeOverlap(best.rect,targetRegion_I32);
  			if( overlap <= config.overlapLower ) {
  				template.addDescriptor(false,best.rect);
  //				fern.learnFernNoise(false, best.rect );
  			}
  
  			// mark all ambiguous regions as bad
  			List<ImageRectangle> ambiguous = detection.getAmbiguousRegions();
  			for( ImageRectangle r : ambiguous ) {
  				overlap = helper.computeOverlap(r,targetRegion_I32);
  				if( overlap <= config.overlapLower ) {
  					fern.learnFernNoise(false, r );
  					template.addDescriptor(false,r);
  				}
  			}
  		}
  	}
docs: Mark regions which were local maximums and had high confidence as negative.  These regions were
  candidates for the tracker but were not selected