Abstract
In real world, a scene is composed by many characteristics. Intrinsic images represent these characteristics by two components, reflectance (the albedo of each point) and shading (the illumination of each point). Because reflectance images are invariant under different illumination conditions, they are more appropriate for some vision applications, such as recognition, detection. We develop the system to separate them from a single image. Firstly, a presented method, called Weighted-Map Method, is used to separate reflectance and shading. A weighted map is created by first transforming original color domain into new color domain and then extracting some useful property. Secondly, we build Markov Random Fields and use Belief Propagation to propagate local information in order to help us correct misclassifications from neighbors. According to our experimental results, our system can apply to not only real images but also synthesized images.
| Original language | English |
|---|---|
| Title of host publication | Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers |
| Pages | 85-95 |
| Number of pages | 11 |
| Edition | PART 3 |
| DOIs | |
| Publication status | Published - 2010 Dec 29 |
| Event | 9th Asian Conference on Computer Vision, ACCV 2009 - Xi'an, China Duration: 2009 Sep 23 → 2009 Sep 27 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Number | PART 3 |
| Volume | 5996 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Other
| Other | 9th Asian Conference on Computer Vision, ACCV 2009 |
|---|---|
| Country | China |
| City | Xi'an |
| Period | 09-09-23 → 09-09-27 |
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All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)
Cite this
}
Weighted map for reflectance and shading separation using a single image. / Hsieh, Sung Hsien; Fang, Chih Wei; Wang, Te Hsun; Chu, Chien Hung; Lien, Jenn Jier James.
Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers. PART 3. ed. 2010. p. 85-95 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5996 LNCS, No. PART 3).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Weighted map for reflectance and shading separation using a single image
AU - Hsieh, Sung Hsien
AU - Fang, Chih Wei
AU - Wang, Te Hsun
AU - Chu, Chien Hung
AU - Lien, Jenn Jier James
PY - 2010/12/29
Y1 - 2010/12/29
N2 - In real world, a scene is composed by many characteristics. Intrinsic images represent these characteristics by two components, reflectance (the albedo of each point) and shading (the illumination of each point). Because reflectance images are invariant under different illumination conditions, they are more appropriate for some vision applications, such as recognition, detection. We develop the system to separate them from a single image. Firstly, a presented method, called Weighted-Map Method, is used to separate reflectance and shading. A weighted map is created by first transforming original color domain into new color domain and then extracting some useful property. Secondly, we build Markov Random Fields and use Belief Propagation to propagate local information in order to help us correct misclassifications from neighbors. According to our experimental results, our system can apply to not only real images but also synthesized images.
AB - In real world, a scene is composed by many characteristics. Intrinsic images represent these characteristics by two components, reflectance (the albedo of each point) and shading (the illumination of each point). Because reflectance images are invariant under different illumination conditions, they are more appropriate for some vision applications, such as recognition, detection. We develop the system to separate them from a single image. Firstly, a presented method, called Weighted-Map Method, is used to separate reflectance and shading. A weighted map is created by first transforming original color domain into new color domain and then extracting some useful property. Secondly, we build Markov Random Fields and use Belief Propagation to propagate local information in order to help us correct misclassifications from neighbors. According to our experimental results, our system can apply to not only real images but also synthesized images.
UR - http://www.scopus.com/inward/record.url?scp=78650490657&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650490657&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-12297-2_9
DO - 10.1007/978-3-642-12297-2_9
M3 - Conference contribution
AN - SCOPUS:78650490657
SN - 3642122965
SN - 9783642122965
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 95
BT - Computer Vision, ACCV 2009 - 9th Asian Conference on Computer Vision, Revised Selected Papers
ER -