Finds the best match for each descriptor from a query set

matches = matcher.match(queryDescriptors, trainDescriptors)
matches = matcher.match(queryDescriptors)
[...] = matcher.match(..., 'OptionName', optionValue, ...)

Input

• queryDescriptors Query set of descriptors.
• trainDescriptors Train set of descriptors. This set is not added to the train descriptors collection stored in the class object.

Output

• matches Matches. If a query descriptor is masked out in Mask, no match is added for this descriptor. So, matches size may be smaller than the query descriptors count. A 1-by-N structure array with the following fields:
  • queryIdx query descriptor index (zero-based index)
  • trainIdx train descriptor index (zero-based index)
  • imgIdx train image index (zero-based index)
  • distance distance between descriptors (scalar)

Options

• Mask default empty
  • In the first form, mask specifying permissible matches between an input query and train matrices of descriptors. Matrix of size [size(queryDescriptors,1),size(trainDescriptors,1)].
  • In the second form, set of masks. Each masks{i} specifies permissible matches between the input query descriptors and stored train descriptors from the i-th image trainDescCollection{i}. Cell array of length length(trainDescriptors), each a matrix of size [size(queryDescriptors,1),size(trainDescriptors{i},1)].

In the first variant of this method, the train descriptors are passed as an input argument. In the second variant of the method, train descriptors collection that was set by cv.DescriptorMatcher.add() is used. Optional mask (or masks) can be passed to specify which query and training descriptors can be matched. Namely, queryDescriptors(i,:) can be matched with trainDescriptors(j,:) only if mask(i,j) is non-zero.