A robust data hiding scheme that embeds the watermark bits by quantizing the gradient directions of an image is proposed. By embedding the watermark in the angle, the watermark be...
Matrix optimization with orthogonal constraints appear in a variety of application fields including signal and image processing. Several researchers have developed algorithms for...
This paper proposes a new HDR imaging method in the gradient domain based on the fusion of two images with different exposure. We first formulate an energy function for the binar...
A novel local image descriptor is proposed in this paper, which combines intensity orders and gradient distributions in multiple support regions. The novelty lies in three aspects...
Diffusion Kurtosis Imaging (DKI) is a new magnetic resonance imaging model that describes the non-Gaussian diffusion behavior in tissues. It has recently been shown that DKI param...
Dirk H. J. Poot, Arnold Jan den Dekker, Eric Achte...
The problems of dimension reduction and inference of statistical dependence are addressed by the modeling framework of learning gradients. The models we propose hold for Euclidean...
The asymptotic behavior of stochastic gradient algorithms is studied. Relying on some results of differential geometry (Lojasiewicz gradient inequality), the almost sure pointconve...
Many statistical translation models can be regarded as weighted logical deduction. Under this paradigm, we use weights from the expectation semiring (Eisner, 2002), to compute fir...
Abstract-- Policy Gradients with Parameter-based Exploration (PGPE) is a novel model-free reinforcement learning method that alleviates the problem of high-variance gradient estima...
Frank Sehnke, Alex Graves, Christian Osendorfer, J...
This paper presents the combined use of gradient and mutual information for infrared and intensity templates matching. We propose to joint: (i) feature matching in a multiresoluti...
Fernando Barrera, Felipe Lumbreras, Angel Domingo ...