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Displaying 1-5 out of 5 total
Who killed the directed model?
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Justin Domke, Alap Karapurkar, Yiannis Aloimonos
Issue Date:June 2008
pp. 1-8
Prior distributions are useful for robust low-level vision, and undirected models (e.g. Markov Random Fields) have become a central tool for this purpose. Though sometimes these priors can be specified by hand, this becomes difficult in large models, which...
 
Image Transformations and Blurring
Found in: IEEE Transactions on Pattern Analysis and Machine Intelligence
By Justin Domke, Yiannis Aloimonos
Issue Date:May 2009
pp. 811-823
Since cameras blur the incoming light during measurement, different images of the same surface do not contain the same information about that surface. Thus, in general, corresponding points in multiple views of a scene have different image intensities. Whi...
 
Signals on Pencils of Lines
Found in: Computer Vision, IEEE International Conference on
By Justin Domke, Yiannis Aloimonos
Issue Date:October 2007
pp. 1-7
This paper proposes the
 
Multiple View Image Reconstruction: A Harmonic Approach
Found in: Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
By Justin Domke, Yiannis Aloimonos
Issue Date:June 2007
pp. 1-8
This paper presents a new constraint connecting the signals in multiple views of a surface. The constraint arises from a harmonic analysis of the geometry of the imaging process and it gives rise to a new technique for multiple view image reconstruction. G...
 
A Probabilistic Notion of Correspondence and the Epipolar Constraint
Found in: 3D Data Processing Visualization and Transmission, International Symposium on
By Justin Domke, Yiannis Aloimonos
Issue Date:June 2006
pp. 41-48
We present a probabilistic framework for correspondence and egomotion. First, we suggest computing probability distributions of correspondence. This has the advantage of being robust to points subject to the aperture effect and repetitive structure, while ...
 
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