IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a scholarly archival journal published monthly. This journal covers traditional areas of computer vision and image understanding, all traditional areas of pattern analysis and recognition, and selected areas of machine intelligence. Read the full scope of TPAMI.
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From the February 2016 issue
Shape and Reflectance Estimation in the Wild
By Geoffrey Oxholm, and Ko Nishino
Our world is full of objects with complex reflectances situated in rich illumination environments. Though stunning, the diversity of appearance that arises from this complexity is also daunting. For this reason, past work on geometry recovery has tried to frame the problem into simplistic models of reflectance (such as Lambertian, mirrored, or dichromatic) or illumination (one or more distant point light sources). In this work, we directly tackle the problem of joint reflectance and geometry estimation under known but uncontrolled natural illumination by fully exploiting the surface orientation cues that become embedded in the appearance of the object. Intuitively, salient scene features (such as the sun or stained glass windows) act analogously to the point light sources of traditional geometry estimation frameworks by strongly constraining the possible orientations of the surface patches reflecting them. By jointly estimating the reflectance of the object, which modulates the illumination, the appearance of a surface patch can be used to derive a nonparametric distribution of its possible orientations. If only a single image exists, these strongly constrained surface patches may then be used to anchor the geometry estimation and give context to the less-descriptive regions. When multiple images exist, the distribution of possible surface orientations becomes tighter as additional context is given, though integrating the separate views poses additional challenges. In this paper we introduce two methods, one for the single image case, and another for the case of multiple images. The effectiveness of our methods is evaluated extensively on synthetic and real-world data sets that span the wide range of real-world environments and reflectances that lies between the extremes that have been the focus of past work.
Editorials and Announcements
- According to Thomson Reuters' 2013 Journal Citation Report, TPAMI has an impact factor of 5.694.
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- We are pleased to announce that David Forsyth, a professor at the University of Illinois at Urbana-Champaign, is the new Editor in Chief of IEEE Transactions on Pattern and Machine Intelligence starting in 2013. He was previously a member of the advisory board of TPAMI.
- TPAMI Essential Set now available
- State of the Journal (Feb 2016)
- State of the Journal (Jan 2015)
- Editor's Note (June 2013)
- Farewall State of the Journal (Jan 2013)
- Editor's Note (Jan 2013)
- Editor's Note (May 2012)
- Editor's Note (February 2012)
- State of the Journal (January 2012)
- Special Issue on Higher Order Graphical Models in Computer Vision (July 2015)
- Special Issue on Bayesian Nonparametrics (Feb 2015)
- TPAMI CVPR Special Section (Dec 2013)
- Special Section on Learning Deep Architectures (Aug 2013)
- In Memoriam: Mark Everingham (Nov 2012)
- Introduction to the Special Section on IEEE Conference on Computer Vision and Pattern Recognition (September 2012)
Call for Papers
- Special Issue on Learning with Shared Information for Computer Vision and Multimedia Analysis (PDF)
Extended Submission deadline: March 15, 2016.
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