Titlebook: Computer Vision - ACCV 2010; 10th Asian Conferenc Ron Kimmel,Reinhard Klette,Akihiro Sugimoto Conference proceedings 2011 Springer Berlin H

最高點(diǎn)

Deformable Object Modelling and Matchingve an overview of such models and of two efficient algorithms for matching such models to new images (Active Shape Models and Active Appearance Models). We also describe recent work on automatically constructing such models from minimally labelled training images.

冒號(hào)

New Efficient Solution to the Absolute Pose Problem for Camera with Unknown Focal Length and Radial 2D-to-3D point correspondences. We propose to solve the problem separately for non-planar and for planar scenes. By decomposing the problem into these two situations we obtain simpler and more efficient solver than the previously known general solver. We demonstrate in synthetic and real experiments

館長(zhǎng)

Efficient Large-Scale Stereo Matchingies by forming a triangulation on a set of support points which can be robustly matched, reducing the matching ambiguities of the remaining points. This allows for efficient exploitation of the disparity search space, yielding accurate dense reconstruction without the need for global optimization. M

陰郁

Mendicant

懶鬼才會(huì)衰弱

Culpable

Shuttle

Video Temporal Super-Resolution Based on Self-similarityl domain of videos. Temporal super-resolution is inherently ill-posed problem because there are an infinite number of high temporal resolution frames that can produce the same low temporal resolution frame. The key idea in this work to solve this ambiguity is exploiting self-similarity, ., a self-si

VEST

Temporal Super Resolution from a Single Quasi-periodic Image Sequence Based on Phase Registrationplied to construct a one period image sequence with high frame-rate based on phase registration data in sub-frame order among multiple periods of the image sequence. First, the periodic image sequence to be reconstructed is expressed as a manifold in the parametric eigenspace of the phase. Given an

adhesive

Solving MRFs with Higher-Order Smoothness Priors Using Hierarchical Gradient Nodes graph conversion which decomposes higher-order cliques as hierarchical auxiliary nodes. For a special class of smoothness priors which can be formulated as gradient-based potentials, we introduce an efficient representation of an auxiliary node called a gradient node. We denote a graph converted us