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IEEE Transactions on Pattern Analysis and Machine Intelligence
Junctions: Detection, Classification, and Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topographic Maps and Local Contrast Changes in Natural Images
International Journal of Computer Vision
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Digital Picture Processing
Filtering, Segmentation, and Depth
Filtering, Segmentation, and Depth
Cooperative Robust Estimation Using Layers of Support
IEEE Transactions on Pattern Analysis and Machine Intelligence
Depth Estimation from Image Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scales in Natural Images and a Consequence on their Bounded Variation Norm
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
Bilateral Filtering for Gray and Color Images
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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ACM SIGGRAPH 2004 Papers
Flash photography enhancement via intrinsic relighting
ACM SIGGRAPH 2004 Papers
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CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
ACM SIGGRAPH 2007 papers
3-D Depth Reconstruction from a Single Still Image
International Journal of Computer Vision
Shape from Defocus via Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation and Depth Ordering Using an Occlusion Detector
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach
International Journal of Computer Vision
Monocular Depth by Nonlinear Diffusion
ICVGIP '08 Proceedings of the 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing
Exploiting T-junctions for depth segregation in single images
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
LSD: A Fast Line Segment Detector with a False Detection Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian inference for layer representation with mixed Markov random field
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Hierarchical region-based representation for segmentation and filtering with depth in single images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Geometric Description of Images as Topographic Maps
Geometric Description of Images as Topographic Maps
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
Fast cartoon + texture image filters
IEEE Transactions on Image Processing
Simultaneous segmentation and figure/ground organization using angular embedding
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Recovering Occlusion Boundaries from an Image
International Journal of Computer Vision
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representing moving images with layers
IEEE Transactions on Image Processing
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This work presents a novel computational model for relative depth order estimation from a single image based on low-level local features that encode perceptual depth cues such as convexity/concavity, inclusion, and T-junctions in a quantitative manner, considering information at different scales. These multi-scale features are based on a measure of how likely is a pixel to belong simultaneously to different objects (interpreted as connected components of level sets) and, hence, to be occluded in some of them, providing a hint on the local depth order relationships. They are directly computed on the discrete image data in an efficient manner, without requiring the detection and interpretation of edges or junctions. Its behavior is clarified and illustrated for some simple images. Then the recovery of the relative depth order on the image is achieved by global integration of these local features applying a non-linear diffusion filtering of bilateral type. The validity of the proposed features and the integration approach is demonstrated by experiments on real images and comparison with state-of-the-art monocular depth estimation techniques.