The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Combining Focus Measures for Three Dimensional Shape Estimation Using Genetic Programming
Abstract
Three-dimensional (3D) shape reconstruction is a fundamental problem in machine vision applications. Shape from focus (SFF) is one of the passive optical methods for 3D shape recovery, which uses degree of focus as a cue to estimate 3D shape. In this approach, usually a single focus measure operator is applied to measure the focus quality of each pixel in image sequence. However, the applicability of a single focus measure is limited to estimate accurately the depth map for diverse type of real objects. To address this problem, we introduce the development of optimal composite depth (OCD) function through genetic programming (GP) for accurate depth estimation. The OCD function is developed through optimally combining the primary information extracted using one (homogeneous features) or more focus measures (heterogeneous features). The genetically developed composite function is then used to compute the optimal depth map of objects. The performance of this function is investigated using both synthetic and real world image sequences. Experimental results demonstrate that the proposed estimator is more accurate than existing SFF methods. Further, it is found that heterogeneous function is more effective than homogeneous function.
Related Content
|
Fahim Anzum, Ashratuz Zavin Asha, Lily Dey, Artemy Gavrilov, Fariha Iffath, Abu Quwsar Ohi, Liam Pond, Md. Shopon, Marina L. Gavrilova.
© 2024.
46 pages.
|
|
Naomi Dassi Tchomte, Franklin Tchakounte, Ismael Abbo.
© 2024.
42 pages.
|
|
Wyclife Ong'eta.
© 2024.
13 pages.
|
|
Gabbi Evrard Tchoukouegno De Mofo, Ali Joan Beri Wacka, Franklin Tchakounte, Jean Marie Kuate Fotso.
© 2024.
22 pages.
|
|
Cecile Simo Tala.
© 2024.
31 pages.
|
|
Ismael Abbo, Naomi Dassi Tchomte.
© 2024.
20 pages.
|
|
Stones Dalitso Chindipha.
© 2024.
22 pages.
|
|
|