15149884
Inverse halftoning by decision tree learning
Inverse halftoning is the process to retrieve a (gray) continuous-tone image from a halftone. Recently, machine-learning-based inverse halftoning techniques have been proposed. Decision-tree learning has been applied with success to various machine-learning applications for quite some time. In this paper, we propose to use decision-tree learning to solve the inverse halftoning problem. This allows us to reuse a number of algorithms already developed. Especially, the maximization of entropy gain is a powerful idea that makes the learning algorithm to automatically select the ideal window as the decision-tree is constructed. The new technique has generated gray images with PSNR numbers, which are several dB above those previously reported in the literature. Moreover, it possesses very fast implementation, lending itself useful for real time applications.
13935279
Robust anisotropic diffusion to produce clear statistical parametric map from noisy fMRI
Functional magnetic resonance imaging (fMRI) uses MRI to noninvasively map areas of increased neuronal activity in human brain without the use of an exogenous contrast agent. Low signal-to-noise ratio of fMRI images makes it necessary to use sophisticated image processing techniques, such as statistical parametric map (SPM), to detect activated brain areas. This paper presents a new technique to obtain clear SPM from noisy fMRI data. It is based on the robust anisotropic diffusion. A direct application of the anisotropic diffusion to fMRI does not work, mainly due to the lack of sharp boundaries between activated and non-activated regions. To overcome this difficulty, we propose to calculate SPM from noisy fMRI, compute diffusion coefficients in the SPM space, and then perform the diffusion in fMRI images using the coefficients previously computed. These steps are iterated until the convergence. Experimental results using the new technique yielded surprisingly sharp and noiseless SPMs.
15509328
New features for affine-invariant shape classification
An object seen from different viewpoints results in differently deformed images. Affine-invariant shape classification must classify correctly the object, regardless its viewpoint. In this paper, we propose new local and global features invariant under affine transformation. These features can be used for supervised or unsupervised shape classification, and for shape-based image indexing and retrieval. One of the proposed features is related to the convex deficiency and the others are extracted from the area matrix. Area matrix was used by Shen for the similarity matching in image retrieval. However, differently from the Shen's work, we parameterize the shape contour using the affine-length parameter. This makes our features robust to affine parameterization, while Shen's work does not have this property. Experimental results indicate that our method can classify correctly even highly deformed and noisy shapes using small training sets.
3147895
Pitfalls in public key watermarking
The cryptographic concept of digital signatures is well suited to image watermarking for authentication and integrity verification. However, the peculiar nature of watermarks demands special care in the application of digital signatures for this purpose. We cryptanalyze a recently proposed watermarking scheme and show how it can be strengthened to thwart our attacks, exploring the use of hashing function contexts and signature algorithms based on elliptic curve cryptography.
4790058
A public-key authentication watermarking for binary images
Authentication watermarking is the process that inserts hidden data into an object (image) in order to detect any fraudulent alteration perpetrated by a malicious hacker. In the literature, quite a small number of secure authentication methods are available for binary images. This paper proposes a new secure authentication watermarking method for binary images. It can detect any visually significant alteration while maintaining good visual quality. As usual, the security of the algorithm lies on the secrecy of a private-key. Only its owner can insert the correct watermark while anyone may verify the authenticity through the corresponding public-key. A possible application of the proposed technique is in Internet fax transmission, i.e. for legal authentication of documents routed outside the phone network.
6853667
Efficient Algorithms for Pairing-Based Cryptosystems
We describe fast new algorithms to implement recent cryptosystems based on the Tate pairing. In particular, our techniques improve pairing evaluation speed by a factor of about 55 compared to previously known methods in characteristic 3, and attain performance comparable to that of RSA in larger characteristics. We also propose faster algorithms for scalar multiplication in characteristic 3 and square root extraction over Fpm, the latter technique being also useful in contexts other than that of pairing-based cryptography.
6362737
Further progress in watermark evaluation testbed (WET)
While Digital Watermarking has received much attention in recent years, it is still a relatively young technology. There are few accepted tools/metrics that can be used to evaluate the suitability of a watermarking technique for a specific application. This lack of a universally adopted set of metrics/methods has motivated us to develop a web-based digital watermark evaluation system called the Watermark Evaluation Testbed or WET. There have been more improvements over the first version of WET. We implemented batch mode with a queue that allows for user submitted jobs. In addition to StirMark 3.1 as an attack module, we added attack modules based on StirMark 4.0. For a new image fidelity measure, we evaluate conditional entropy as an image fidelity measure for different watermarking algorithms and different attacks. Also, we show the results of curve fitting the Receiver Operating Characteristic (ROC) analysis data using the Parzen window density estimation. The curve fits the data closely while having only two parameters to estimate.
32223299
Security Techniques Based on EPC Gen2 Tag for Secure Mobile RFID Network Services
RFID (Radio Frequency Identification) technology will be a ubiquitous reality in daily life in the near future. The R&D groups in global now have paid attention to integrate RFID with mobile phone devices as well as to associate with the existing mobile telecommunication network. Such a converged technology and services would lead to make new markets and research challenges. However, privacy threats on RFID tags embedded into products has become stumbling block. Therefore, this paper propose a new security technique for mobile RFID service, which can be support to secure RFID service and uses the privacy protection in mobile RFID network services.
558514
Missing value estimation for DNA microarray gene expression data: local least squares imputation
MOTIVATION
Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local similarity structures in the data as well as least squares optimization process.


RESULTS
The proposed local least squares imputation method (LLSimpute) represents a target gene that has missing values as a linear combination of similar genes. The similar genes are chosen by k-nearest neighbors or k coherent genes that have large absolute values of Pearson correlation coefficients. Non-parametric missing values estimation method of LLSimpute are designed by introducing an automatic k-value estimator. In our experiments, the proposed LLSimpute method shows competitive results when compared with other imputation methods for missing value estimation on various datasets and percentages of missing values in the data.


AVAILABILITY
The software is available at http://www.cs.umn.edu/~hskim/tools.html


CONTACT
hpark@cs.umn.edu
38600553
WIPI Mobile Platform with Secure Service for Mobile RFID Network Environment
Recently, RFID (Radio Frequency Identification) technology is practically applied to a number of logistics processes as well as asset management, and RFID is also expected to be permeated in our daily life with the name of ’Ubiquitous Computing’ or ‘Ubiquitous Network’ within the near future. The R&D groups in global now have paid attention to integrate RFID with mobile devices as well as to associate with the existing mobile telecommunication network. Such a converged technology and services would lead to make new markets and research challenges. However, the privacy violation on tagged products has become stumbling block. We propose light-weight security mechanism which is constructed by mobile RFID security mechanism based on WIPI (Wireless Internet Platform for Interoperability). WIPI-based light-weight mobile RFID security platform can be applicable to various mobile RFID services that required secure business applications in mobile environment.
8295029
Cache filtering techniques to reduce the negative impact of useless speculative memory references on processor performance
High-performance processors employ aggressive speculation and prefetching techniques to increase performance. Speculative memory references caused by these techniques sometimes bring data into the caches that are not needed by correct execution. This paper proposes the use of the first-level caches as filters that predict the usefulness of speculative memory references. With the proposed technique, speculative memory references bring data only into the first-level caches rather than all levels in the cache hierarchy. The processor monitors the use of the cache blocks in the first-level caches and decides which blocks to keep in the cache hierarchy based on the usefulness of cache blocks. It is shown that a simple implementation of this technique usually outperforms inclusive and exclusive baseline cache hierarchies commonly used by today's processors and results in IPC performance improvements of up to 9.2% on the SPEC2000 integer benchmarks.