LSH-RANSAC: Incremental Matching of Large-Size Maps

Balanced Large Scale Knowledge Matching Using LSH Forest

Evolving Knowledge Ecosystems were proposed recently to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the enviro...

Incremental Optimization of Large Robot-Acquired Maps

Many robotics applications require the robot to build a map of its environment. We consider the problem of Simultaneous Localization and Mapping (SLAM), i.e., building a map of an environment while exploring it for the first time. SLAM algorithms approach this by identifying features in the environment (e.g., the corner of a desk) and determining the relative positions of features. A robot’s se...

Efficient matching of large-size histograms

As we know, histogram matching is a commonly-adopted technique in the applications of pattern recognition. The matching of two patterns can be accomplished by matching their corresponding histograms. In general, the number of features and the resolution of each feature will determine the size of histogram. The more the number of features and the higher the resolution of each feature, the strong...

DT-RANSAC: A Delaunay Triangulation Based Scheme for Improved RANSAC Feature Matching

Image Matching for Individual Recognition with SIFT, RANSAC and MCL

Monitoring the whale individuals in the ocean is a current problem among conservationists. Biologists often use photos of whale caudal for this problem as it is the most discriminant pattern for distinguishing an individual whale from another, but it often requires laborious visual analysis. There was a challenge announced in the SeaCLEF of LifeCLEF campaign for automatic whale individual recog...