Titlebook: Computer Vision – ACCV 2016 Workshops; ACCV 2016 Internatio Chu-Song Chen,Jiwen Lu,Kai-Kuang Ma Conference proceedings 2017 Springer Intern

neolith

flaunt

Patch Group Based Bayesian Learning for Blind Image Denoising distribution is usually unknown and is more complex, making image denoising still a challenging problem. In this paper, we propose a novel blind image denoising method under the Bayesian learning framework, which automatically performs noise inference and reconstructs the latent clean image. By uti

開始從未

BOLT

共同確定為確

Visual Smoke Detectionrly fire detection. Proposed algorithm uses an unique combination of features to detect smoke efficiently. These features use appearance, energy and motion properties of the smoke. Further analysis of past history of smoke increases the accuracy of the algorithm. These features are less complex and

Anticoagulants

Local Feature-Based Photo Album Compression by Eliminating Redundancy of Human Partition save the storage spaces. Recently, an advance technique of photo album compression via video compression is proposed which utilizes the similarity between photos to improve the compression performance. In this paper, we modify the original scheme to improve the compression performance when photos c

異常

UNT

harbinger

padding

A Dual Adaptive Regularization Method to Remove Mixed Gaussian-Poisson Noise the ill-posed image denoising problems with an approximate well-posed one. However, the sole constraint in non-adaptive regularization methods is harmful to a good balance between the noise-removing and detail-preserving. Meanwhile, most existing adaptive regularization methods were aimed at unitar