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Features Matching
Features Matching is the task of establishing correspondences between two images of the same scene or object. It is an effective method to detect a specified target in a cluttered scene.
Methods of Features Matching
Dense Registration through Optical Flow
Wide Baseline Spatial Matching
Random Sample Consensus (RANSAC)
Hough Transform
The Hough Transform is a feature extraction technique used in image analysis, computer vision, and digital image processing. The purpose of the technique is to find imperfect instances of objects within a certain class of shapes by a voting procedure.
Bag of words and VLAD
The general idea of a Bag of Visual words (BOVW) is to represent an image as a set of features. Features consist of keypoints and descriptors. Keypoints are the “stand out” points in an image and the descriptor is the description of the keypoint. It’s a way of extracting features from text for use in modeling, such as with machine learning algorithms.
Vector or Locally Aggregated Descriptors (VLAD) is an extension of Bag of words which accumulates the residual of each descriptor with respect to its assigned cluster. In simpler terms, a descriptor is matched to its closest cluster, then for each cluster, the sum of the differences of the descriptors assigned to the cluster and the centroid of the cluster is stored.
Image Matching
Image Matching is an important concept in computer vision and object recognition. Images of the same item can be taken from any angle, with any lighting and scale. This as well as occlusion may cause problems for recognition. But ultimately, they still show the same item and should be categorized that way.
Aggregated Selective Match Kernel
Aggregated Selective Match Kernel is a model that is incorporated by sharing the best properties of Hamming Embedding (HE) and Vector or Locally Aggregated Descriptors (VLAD). ASMK exploits the use of a selectivity function and shows how aggregation per visual word can deal with burstiness.