Extract Topological Features Research Articles

A Quantitative Visualization Method for Wind Tunnel Experiments Based on 3D Particle Tracking Velocimetry (3D-PTV)

The development of a measurement system to visualize, classify (based on topological features) and quantify complex flows in wind tunnel experiments is described. The basic idea is to extract topological features directly instead of postprocessing large sets of raw data (as generated by conventional PIV or PTV systems). As a particular aspect, the seeding methods in industrial wind tunnels are discussed. While helium filled soap bubbles are well suited because of their neutral buoyancy, there is a problem with their visibility, especially when reflecting model surfaces are being imaged at the same time. Various methods were evaluated in order to enhance the visual contrast of the bubbles.

Recognition of Handwritten Characters by Topological Feature Extraction and Multilevel Categorization

A handwritten character recognition system has been designed by making use of topological feature extraction and multilevel decision making. By properly specifying a set of easily detectable topological features, it is possible to convert automatically the handwritten characters into stylized forms and to classify them into primary classes with similar topological configurations. Final recognition is accomplished by a secondary stage that performs local analysis on the characters in each primary category. The recognition system consists of two stages: global recognition, followed by local recognition. Automatic character stylization results in pattern clustering which simplifies the classification tasks considerably, while allowing a high degree of generality in the acceptable writing format. Simulation of this scheme on a digital computer has shown only 6 percent misrecognition.

Automatic recognition of handwritten characters via feature extraction and multi-level decision

Automatic recognition of handwritten alphanumeric characters is designed by making use of topological feature extraction and multi-level decision making. By properly specifying a set of easily detectable topological features, it is possible to convert automatically the handwritten characters into stylized forms and to classify them into primary categories. Each category contains one or several character pattern classes with similar topological configurations. Final recognition is accomplished by a secondary stage which performs local analysis on the characters in each primary category. The recognition system consists of two stages, global recognition followed by local recognition. Automatic character stylization results in pattern clustering which simplifies the classification tasks considerably, while allowing a high degree of generality in the acceptable writing format. Simulation of this scheme on a digital computer has shown only 2% misrecognition.