Local Features: Detection and Description

Matching two images is one of the subjects that we want to learn for implementing our project. Hereby we have worked on the Local Features subject. Rotation, scaling, viewpoint changes makes harder matching process. Image matching process contains five steps:
 1. Find a set of distinctive keypoints: For better results, points which changes in both directions should be chosen. These points are generally corners.
 2. Define a region around each keypoint in a scale- or affine-invariant manner: Similarity of signature functions helps matching process. In affine-invariant region detection, strong viewpoint changes can be handled too.
 3. Extract and normalize the region content: In this step rotation invariances are fixed.
 4. Compute a descriptor from the normalized region: Finally a descriptor is encoded for content discriminative matching process. SIFT and SURF are the example implementations of local descriptors. 5. Match the local descriptors.

 In conclusion we have learned the algorithm of matching process with local descriptors. This functionality can be used in stereo matching, image retrieval, object recognition and categorization.