[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a description of how to understand, use, and interpret principal component analysis. The paper focuses on the use of principal component analysis in typical chemometric areas but the results are generally applicable.

/pdf/principal-component-analysis-1sba76ou47.pdf

Principal component analysis

Photoplethysmography and its application in clinical physiological measurement

In this paper, a non-causal median filtering method is proposed to predict the motion vectors across the wavelet subbands of a video frame for multiresolution motion estimation. This median filtering method effectively overcomes the problem of propagation of false motion vectors that exists in the conventional multiresolution motion estimation schemes. A significant feature of the proposed technique is that it imposes no demand for additional bandwidth. Simulation studies show that this median filtering-based multiresolution motion; estimation technique effectively improves the motion prediction performance. It is further shown that this performance improvement is achieved with little increase in the computational complexity.

Wavelet-based multiresolution motion estimation through median filtering

Motion estimation plays a very crucial role in many video applications. Hence, an accurate and fast algorithm is required in real-time applications. Although exhaustive block matching (EBM) algorithm is the best algorithm used for motion estimation in terms of performance, it is computationally very expensive. Several algorithms have been developed in the literature to reduce the computational time required by the EBM algorithm; however, their performance is not comparable to that of EBM. In this paper, we propose an extremely fast technique for motion estimation based on the edge map of only a fraction of the frame. Extensive experiments are carried out, and the results show that the proposed technique provides a performance comparable to that of the EBM algorithm, with a very low computational complexity.

A very fast edge map-based algorithm for accurate motion estimation

In this paper, a new contourlet-based method for denoising of images corrupted by additive white Gaussian noise is proposed. The alpha-stable distribution is used to model the contourlet coefficients of noise-free images. This model is then exploited to develop a Bayesian minimum mean absolute error estimator. A modified empirical characteristic function-based method is employed for estimating the parameters of the assumed alpha-stable prior. The performance of the proposed denoising method is evaluated by using standard noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods. The results show that the proposed method provides values of the peak signal-to-noise ratio higher than that provided by some of the existing techniques along with superior visual quality images.

http://users.encs.concordia.ca/~h_sadrea/paper/06908372.pdf

Contourlet domain image denoising using the alpha-stable distribution

In this paper, a two-stage scheme is proposed to deal with the difficult problem of acoustic echo cancellation (AEC) in single-channel scenario in the presence of noise. In order to overcome the major challenge of getting a separate reference signal in adaptive filter-based AEC problem, the delayed version of the echo and noise suppressed signal is proposed to use as reference. A modified objective function is thereby derived for a gradient-based adaptive filter algorithm, and proof of its convergence to the optimum Wiener-Hopf solution is established. The output of the AEC block is fed to an acoustic noise cancellation (ANC) block where a spectral subtraction-based algorithm with an adaptive spectral floor estimation is employed. In order to obtain fast but smooth convergence with maximum possible echo and noise suppression, a set of updating constraints is proposed based on various speech characteristics (e.g., energy and correlation) of reference and current frames considering whether they are voiced, unvoiced, or pause. Extensive experimentation is carried out on several echo and noise corrupted natural utterances taken from the TIMIT database, and it is found that the proposed scheme can significantly reduce the effect of both echo and noise in terms of objective and subjective quality measures.

/pdf/single-channel-acoustic-echo-cancellation-in-noise-based-on-147r306sdw.pdf

Single-channel acoustic echo cancellation in noise based on gradient-based adaptive filtering

Histogram of oriented gradients (HOG) is often used for object detection in images These HOG features of images can be referred to as 2DHOG when represented in a 2D matrix format instead of a 1D vector In this paper, we propose a new vehicle detection algorithm by using 2DHOG in the discrete cosine transform (DCT) domain The proposed technique consists of extracting 2DHOG from the input image and applying on it 2DDCT This is followed by a low pass filtering in order to obtain novel features called as transform-domain 2DHOG (TD2DHOG) TD2DHOG is used with a classifier pyramid in order to reduce the multi-scale scanning cost Experimental results show that the proposed algorithm when applied on two public vehicle detection datasets reduces the storage requirement of the classifier pyramid, while providing about the same performance as that provided by the state-of-the-art techniques

M. Omair Ahmad

Papers

Wavelet-based multiresolution motion estimation through median filtering

A very fast edge map-based algorithm for accurate motion estimation

Contourlet domain image denoising using the alpha-stable distribution

Single-channel acoustic echo cancellation in noise based on gradient-based adaptive filtering

Vehicle detection using TD2DHOG features