TY - CHAP
T1 - A dense stereo correspondence algorithm for hardware implementation with enhanced disparity selection
AU - Nalpantidis, Lazaros
AU - Gasteratos, A.
AU - Sirakoulis, G.Ch.
PY - 2008/1/1
Y1 - 2008/1/1
N2 - In this paper an effective, hardware oriented stereo correspondence algorithm, able to produce dense disparity maps of improved fidelity is presented. The proposed algorithm combines rapid execution, simple and straight-forward structure as well as comparably high quality of results. These features render it as an ideal candidate for hardware implementation and for real-time applications. The proposed algorithm utilizes the Absolute Differences (AD) as matching cost and aggregates the results inside support windows, assigning Gaussian distributed weights to the support pixels, based on their Euclidean distance. The resulting Disparity Space Image (DSI) is furthered refined by Cellular Automata (CA) acting in all of the three dimensions of the DSI. The algorithm is applied to typical as well as to self-recorded real-life image sets. The disparity maps obtained are presented and quantitatively examined.
AB - In this paper an effective, hardware oriented stereo correspondence algorithm, able to produce dense disparity maps of improved fidelity is presented. The proposed algorithm combines rapid execution, simple and straight-forward structure as well as comparably high quality of results. These features render it as an ideal candidate for hardware implementation and for real-time applications. The proposed algorithm utilizes the Absolute Differences (AD) as matching cost and aggregates the results inside support windows, assigning Gaussian distributed weights to the support pixels, based on their Euclidean distance. The resulting Disparity Space Image (DSI) is furthered refined by Cellular Automata (CA) acting in all of the three dimensions of the DSI. The algorithm is applied to typical as well as to self-recorded real-life image sets. The disparity maps obtained are presented and quantitatively examined.
UR - http://www.scopus.com/inward/record.url?scp=55349105845&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-87881-0_34
DO - 10.1007/978-3-540-87881-0_34
M3 - Book chapter
AN - SCOPUS:55349105845
VL - 5138 LNAI
SP - 365
EP - 370
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER -