There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

The 11th edition of Harrison's Principles of Internal Medicine welcomes Anthony Fauci to its editorial staff, in addition to more than 85 new contributors. While the organization of the book is similar to previous editions, major emphasis has been placed on disorders that affect multiple organ systems. Important advances in genetics, immunology, and oncology are emphasized. Many chapters of the book have been rewritten and describe major advances in internal medicine. Subjects that received only a paragraph or two of attention in previous editions are now covered in entire chapters. Among the chapters that have been extensively revised are the chapters on infections in the compromised host, on skin rashes in infections, on many of the viral infections, including cytomegalovirus and Epstein-Barr virus, on sexually transmitted diseases, on diabetes mellitus, on disorders of bone and mineral metabolism, and on lymphadenopathy and splenomegaly. The major revisions in these chapters and many

Harrison's Principles of Internal Medicine

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

http://ieeexplore.ieee.org/iel2/4524/12820/00592460.pdf

Image Processing

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

In this paper, new methods are addressed for impulse and speckle noise removal in images. The approach is based on the
fusion of noise detection and image inpainting techniques. To avoid destroying the real structures of the image, the noise
areas are first recognized to be repaired by an inpainting algorithm, subsequently. To distinguish the impulse noise areas
in the image, a Neuro-Fuzzy model is employed and, to extract the speckled regions an algorithm is proposed based on
Frost filtering and image resizing. The advantage of inpainting technique over the regular filtering methods is that it will
be easier to generalize to all types of noise. Once we detect the damaged pixels in the image, the inpainting algorithm
will be able to repair them. Various types of images under three levels of noise are tested using PSNR and SSIM
measures. The experimental results demonstrate the great ability of the new approaches to suppress the noise properly,
while preserving critical details of the image.

Image noise removal using image inpainting

Image denoising is a vital and indispensable pre-process for most applied image processing systems. Having prior knowledge about the noise level is essential for optimizing denoising algorithms. However, this information most likely does not exist for real applications and is much harder to extract from a single noisy image than from multiple noisy images. For Gaussian noise, there are many accurate state-of-the-art level estimations, whereas there are only limited random valued impulse noise level estimations proposed. Moreover, the existing proposed impulse noise estimators are limited in accuracy, especially in the presence of high noise levels. This paper presents a new random-valued impulse noise level estimation (RVI-E) algorithm using only a single image. The presented RVI-E algorithm is based on distribution property of impulse noise pixels, on correlation among the image, and on a new linear relationship between the percentage of big-distorted noise and one of all noise. The mathematical study, computer simulations, and analysis on 347 different images using five online grayscale image databases shows that (a) the presented method is efficient, robust and reliable, (b) the presented methods show stably accurate performance across images with different contents and different levels of noise (lower than 60%), and (c) the speed performance of the proposed RVI can be boosted by the parallel computing strategy, since the estimation utilizes a parallel framework.

Single Image Based Random-Value Impulse Noise Level Estimation Algorithm

 The color image from one of the color models, for instance the RGB model, can be transformed into the quaternion algebra and be represented as one quaternion image which allows to process simultaneously of all color components of the image. The color image can be also considered in different models with transformation to the octonion space with following processing in the 8-D frequency domain.  In this work, we describe the algorithm for the 2-D two-side octonion DFT (ODFT), by using two-side 2-D quaternion DFTs (QDFT).  The calculation of the transform is reduced to calculation of two 2-D QDFT which has fast algorithms. The octonion algebra with the Fourier transform can be used in color imaging as the 2-D ODFT, which found effective applications in color imaging, medical imaging, in image filtration, image enhancement.  The octonion 2-D DFT can be used not only in color imaging, but in gray-scale imaging as well, and for that there are many models of transferring one or a few gray-scale images into the octonion space.

2-D Octonion Discrete Fourier Transform: Fast Algorithms

A new weighted thresholding concept is presented, which is used for the set-theoretical representation of signals,
the producing new signals containing a large number of key features that are in the original signals and the
design new morphological filters. Such representation maps many operations of non binary signal and image
processing to the union of the simple operations over the binary signals and images. The weighted thresholding
is invariant under the morphological transformations, including the basic ones, erosion and dilation. The main
idea of using the weighted thresholding is in the choice of the special level of thresholding on which we can
concentrate all our attention for the future processing. Together with arithmetical thresholding the so-called
Fibonacci levels are chosen because of many interesting properties; one of them is the effective decomposition of
the median filter. Experimental results show that the Fibonacci thresholding is much promised and can be used
for many applications, including the image enhancement, segmentation, and edge detection.

Sos S. Agaian

Papers

Image noise removal using image inpainting

Single Image Based Random-Value Impulse Noise Level Estimation Algorithm

2-D Octonion Discrete Fourier Transform: Fast Algorithms

Fibonacci thresholding: Signal representation and morphological filters

An examination of decomposition sparsity