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SIFT Flow: Dense Correspondence across Scenes and Its Applications
TLDR
SIFT flow is proposed, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. Expand
SIFT Flow: Dense Correspondence across Different Scenes
TLDR
A method to align an image to its neighbors in a large image collection consisting of a variety of scenes, and applies the SIFT flow algorithm to two applications: motion field prediction from a single static image and motion synthesis via transfer of moving objects. Expand
Unsupervised Joint Object Discovery and Segmentation in Internet Images
TLDR
This work proposes to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. Expand
Exploring new representations and applications for motion analysis
TLDR
This thesis builds a human-assisted motion annotation system to obtain ground-truth motion, missing in the literature, for natural video sequences, and proposes SIFT flow, a new framework for image parsing by transferring the metadata information from the images in a large database to an unknown query image. Expand
Nonparametric Scene Parsing via Label Transfer
TLDR
This paper proposes a novel, nonparametric approach for object recognition and scene parsing using a new technology the authors name label transfer, which is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure. Expand
Nonparametric scene parsing: Label transfer via dense scene alignment
TLDR
Compared to existing object recognition approaches that require training for each object category, the proposed nonparametric scene parsing system is easy to implement, has few parameters, and embeds contextual information naturally in the retrieval/alignment procedure. Expand
Deformable Spatial Pyramid Matching for Fast Dense Correspondences
TLDR
This work introduces a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences that simultaneously regularizes match consistency at multiple spatial extents-ranging from an entire image, to coarse grid cells, to every single pixel. Expand
Real-time texture synthesis by patch-based sampling
TLDR
An algorithm for synthesizing textures from an input sample by sampling patches according to a nonparametric estimation of the local conditional MRF density function, to avoid mismatching features across patch boundaries. Expand
Automatic Estimation and Removal of Noise from a Single Image
TLDR
A unified framework for automatic estimation and removal of color noise from a single image using piecewise smooth image models is proposed and an upper bound of the real NLF is estimated by fitting a lower envelope to the standard deviations of per-segment image variances. Expand
Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling
TLDR
The technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and is demonstrated through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade. Expand
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