This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self- Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed.
Dense Two-Frame Stereo Correspondence bySelf-Organizing Neural Network
GALLO, IGNAZIO;BINAGHI, ELISABETTA
2009-01-01
Abstract
This work aims at defining an extension of a competitive method for matching correspondences in stereoscopic image analysis. The method we extended was proposed by Venkatesh, Y.V. et al where the authors extend a Self- Organizing Map by changing the neural weights updating phase in order to solve the correspondence problem within a two-frame area matching approach and producing dense disparity maps. In the present paper we have extended the method mentioned by adding some details that lead to better results. Experimental studies were conducted to evaluate and compare the solution proposed.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.