Abstract
We propose a robust method for registering overlapping range images of a Lambertian object under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, the albedo is promising to range image registration of Lambertian objects lacking in discriminative geometric features under variable illumination. We use adaptive regions in our method to model the local distribution of albedo, which enables us to stably extract the reliable attributes of each point against illumination estimates. We use a level-set method to grow robust and adaptive regions to define these attributes. A similarity metric between two attributes is also defined to match points in the overlapping area. Moreover, remaining mismatches are efficiently removed using the rigidity constraint of surfaces. Our experiments using synthetic and real data demonstrate the robustness and effectiveness of our proposed method.
| Original language | English |
|---|---|
| Pages (from-to) | 649-667 |
| Number of pages | 19 |
| Journal | Computer Vision and Image Understanding |
| Volume | 115 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - May 1 2011 |
| Externally published | Yes |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
Cite this
Robustly registering range images using local distribution of albedo. / Thomas, Diego; Sugimoto, Akihiro.
In: Computer Vision and Image Understanding, Vol. 115, No. 5, 01.05.2011, p. 649-667.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Robustly registering range images using local distribution of albedo
AU - Thomas, Diego
AU - Sugimoto, Akihiro
PY - 2011/5/1
Y1 - 2011/5/1
N2 - We propose a robust method for registering overlapping range images of a Lambertian object under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, the albedo is promising to range image registration of Lambertian objects lacking in discriminative geometric features under variable illumination. We use adaptive regions in our method to model the local distribution of albedo, which enables us to stably extract the reliable attributes of each point against illumination estimates. We use a level-set method to grow robust and adaptive regions to define these attributes. A similarity metric between two attributes is also defined to match points in the overlapping area. Moreover, remaining mismatches are efficiently removed using the rigidity constraint of surfaces. Our experiments using synthetic and real data demonstrate the robustness and effectiveness of our proposed method.
AB - We propose a robust method for registering overlapping range images of a Lambertian object under a rough estimate of illumination. Because reflectance properties are invariant to changes in illumination, the albedo is promising to range image registration of Lambertian objects lacking in discriminative geometric features under variable illumination. We use adaptive regions in our method to model the local distribution of albedo, which enables us to stably extract the reliable attributes of each point against illumination estimates. We use a level-set method to grow robust and adaptive regions to define these attributes. A similarity metric between two attributes is also defined to match points in the overlapping area. Moreover, remaining mismatches are efficiently removed using the rigidity constraint of surfaces. Our experiments using synthetic and real data demonstrate the robustness and effectiveness of our proposed method.
UR - http://www.scopus.com/inward/record.url?scp=79952280579&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952280579&partnerID=8YFLogxK
U2 - 10.1016/j.cviu.2010.11.016
DO - 10.1016/j.cviu.2010.11.016
M3 - Article
AN - SCOPUS:79952280579
VL - 115
SP - 649
EP - 667
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
SN - 1077-3142
IS - 5
ER -