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

Because a signal can often be easily corrupted during its transmission, registration, or storage, de-noising is an important field in the areas of communications systems and of signal and image processing, especially where defense and security applications are of concern. Techniques employing transform-based methods such as the Fourier transform, the cosine transform, and wavelets have already been applied successfully to this field when dealing with an image corrupted by noise having a Gaussian or uniform distribution. However, images where impulse or salt and pepper noise are introduced are typically treated using median or switched-median algorithms because the sudden discontinuities of impulse noise often present problems for conventional transform-based noise reduction approaches. Additionally, binary images cannot easily be de-noised by fast orthogonal transforms or wavelets.
A novel noise detection and reduction scheme using a fast logical (binary) transform-based Boolean minimization algorithm is presented. The presented approach is capable of de-noising both binary and multivalued images corrupted by impulse noise. A comparison with well-known methods is offered. Particularly, the algorithm reliably detects noise more effectively than existing switched-median methods, and de-noising results comparable to or better than those attainable with median filtering are possible. The technique performs especially well when operating on images of high complexity. The new technique does not require the use of a multiplication nor a sorting operation. In addition, we show that the presented de-noising procedure could be easily performed on an already compressed file or during the compression step. Furthermore, the simplicity of the transform makes a gate-level hardware realization practical for use with distributed sensors and inexpensive or high-speed imaging systems.

Denoising of images using logical (binary) transforms

As traditional RGB cameras cannot perform well under weak light in the darkness and poor weather conditions, thermal cameras have become an essential component of edge systems. This paper proposes a lightweight, faster binarized R-CNN network (a state-of-the-art instance segmentation model), called BiThermalNet, for thermal object detection with high detection capabilities and lower memory usage. It designs a new Region Proposal Network(RPN) structure with a binary neural network (BNN) to lower model size by 16%, having higher accuracy performance. BiThermalNet adds novel-designed residual gates to maximum information entropy and offers channel-wise weight and bias to reduce errors from binarization. The extensive experiments on different thermal datasets (such as Dogs&People Thermal Dataset, UNIRI-TID) confirm that BiThermalNet can outperform traditional faster R-CNN by sizable margins with smaller models size. Moreover, a comparative analysis of the proposed methods on thermal images will also be presented.

BiThermalNet: a lightweight network with BNN RPN for thermal object detection

Methods and apparatus for restoration of a digital image. In one embodiment, a method for repairing a defect in a digital image to provide a restored image comprises determining a plurality of pixel locations to form a neighborhood relating to the defect and whether or not the neighborhood has a well-defined, dark border along its edge. Should the neighborhood not have dark border, one embodiment of the method entails processing the neighborhood to bring the neighborhood approximately to uniform darkness, processing the neighborhood to match surroundings in the digital image, copying an edge of a neighborhood in the digital image into the processed neighborhood, processing pixels of the edge to repair the copied edge pixels, and outputting the restored image for display to a user. Should the neighborhood have a dark, well-defined border, one embodiment of the method entails processing the neighborhood as to locally enhance the neighborhood and match its surroundings in the digital image; processing the neighborhood's edge such that the edge also matches its surroundings in both the defect and the digital image; processing the neighborhood to invert its pixel values and then perform the last two steps once again; processing the neighborhood to increase its contrast and then perform the last three steps once again; processing the neighborhood to bring the neighborhood to a more uniform darkness; processing the uniform darkness neighborhood to match surroundings in the digital image; and outputting the restored image for display to a user.

Digital image restoration for eliminating water blotches from old texts, photos or documents

The proposed method is a new approach for enhancing grayscale images, when the images are map to quaternion space, and then, the quaternion based enhancement technique is used. Namely, the quaternion alpha-rooting method to enhance the so generated “quaternion” image. Currently, there are only very limited techniques to convert a grayscale image to color image, and in this article we propose a novel conversion technique which helps in easily converting a grayscale image to a color or quaternion image. In addition to that, we describe the quaternion alpha-rooting method of quaternion image enhancement. Quaternion approach of enhancement allows for processing the multi-signaled image as a single unit. The fast algorithm of quaternion discrete Fourier transforms makes the implementation of the enhancement method practically possible and effective. The results of image enhancement by the proposed method and comparison with the traditional alpha-rooting of grayscale images are described. The metric used to assess the quality of enhancement shows good values for the results of the proposed enhancement. One of the enhancement metrics is the contrast-based metric referred to as the enhancement measure estimation (EME). Other metrics used to assess the quality of the enhanced images are signal-to-noise ratio (SNR), mean-square-root error (MSRE).

Sos S. Agaian

Papers

Denoising of images using logical (binary) transforms

BiThermalNet: a lightweight network with BNN RPN for thermal object detection

Digital image restoration for eliminating water blotches from old texts, photos or documents

Quaternion alpha-rooting image enhancement of grayscale images

Gaze depth estimation for eye-tracking systems