Titlebook: Computer Vision -- ECCV 2014; 13th European Confer David Fleet,Tomas Pajdla,Tinne Tuytelaars Conference proceedings 2014 Springer Internati

custody

galley

Joint Unsupervised Face Alignment and Behaviour Analysisusually trained on thousands of carefully annotated examples, is applied to track the landmark points, and then analysis is performed using mostly the shape and more rarely the facial texture. This paper challenges the above framework by showing that it is feasible to perform joint landmarks localiz

vascular

Learning a Deep Convolutional Network for Image Super-Resolutiontion images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the low-resolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unli

endoscopy

Discriminative Indexing for Probabilistic Image Patch Priorssks, especially deconvolution, due to its rich expressiveness. However, its applicability is limited by the heavy computation involved in the associated optimization process. Inspired by the recent advances on using regression trees to index priors defined on a Conditional Random Field, we propose a

BUDGE

Modeling Video Dynamics with Deep Dynencodernamic system can model dynamic textures but have limited capacity of representing sophisticated nonlinear dynamics. Inspired by the nonlinear expression power of deep autoencoders, we propose a novel model named dynencoder which has an autoencoder at the bottom and a variant of it at the top (named

MORPH

Good Image Priors for Non-blind Deconvolutionat if we have more specific training examples, .sharp images of similar scenes? Surprisingly, state-of-the-art image priors don’t seem to benefit from from context-specific training examples. Re-training generic image priors using ideal sharp example images provides minimal improvement in non-blind

讓你明白

Image Deconvolution Ringing Artifact Detection and Removal via PSF Frequency Analysisinto account non-invertible frequency components of the blur kernel used in the deconvolution. Efficient Gabor wavelets are produced for each non-invertible frequency and applied on the deblurred image to generate a set of filter responses that reveal existing ringing artifacts. The set of Gabor fil

arsenal

Overthrow

https://doi.org/10.1007/978-3-319-10593-23D; activity recognition and understanding; artificial intelligence; computational photography; computer

Fretful

978-3-319-10592-5Springer International Publishing Switzerland 2014