Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
ACM SIGGRAPH 2005 Papers
2D to 3d image conversion based on classification of background depth profiles
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
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It is generally understood that interpreting depth from monoscopic images inherently induces the problem of object segmentation and context recognition. A common way to deal with this cimplexity is to focus on one aspect (e.g. segmentation) and largely constrain or skip the other (e.g. scene classification). In our approach, we practically reconcile the two paths. We start by defining several depth profiles, providing rough estimates for both background and foreground, while reflecting common photographic and cinematic practices and rules. The idea is to assign one or more of these profiles to an image-based on the output of multiple classifiers, and apply an image-adaptive bilateral filter to align the depth with object edges. From extensive trails, we conclude that robust classification and visual performance can be achieved with this approach on a variety of content.