Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Primal Sketch Based Adaptive Perceptual JND Model for Digital Watermarking

Abstract-Watermarking algorithms based on the Just Noticeable Distortion (JND) model show great superiority over other methods, in terms of the watermark imperceptibility and robustness. However, the existing wavelet-based JND models are based on global coefficients, without consideration of image content characteristics. Following the pixel-wise masking idea, a primal sketch based adaptive perceptual JND model (PSAPM) is proposed in this paper, in which an improved watermarking algorithm is designed. It better describes the behavior of the watermark embedder. Experiments show that our algorithm is robust against attacks, including cropping, noise addition and JPEG compression