Cancelable biometrics realization with multispace random projections.

Biometric characteristics cannot be changed; therefore, the loss of privacy is permanent if they are ever compromised. This paper presents a two-factor cancelable formulation, where the biometric data are distorted in a revocable but non-reversible manner by first transforming the raw biometric data into a fixed-length feature vector and then projecting the feature vector onto a sequence of random subspaces that were derived from a user-specific pseudorandom number (PRN). This process is revocable and makes replacing biometrics as easy as replacing PRNs. The formulation has been verified under a number of scenarios (normal, stolen PRN, and compromised biometrics scenarios) using 2400 Facial Recognition Technology face images. The diversity property is also examined.     

Safe projections of binary data sets

Selectivity estimation of a boolean query based on frequent itemsets can be solved by describing the problem by a linear program. However, the number of variables in the equations is exponential, rendering the approach tractable only for small-dimensional cases. One natural approach would be to project the data to the variables occurring in the query. This can, however, change the outcome of the linear program.We introduce the concept of safe sets: projecting the data to a safe set does not change the outcome of the linear program. We characterise safe sets using graph theoretic concepts and give an algorithm for finding minimal safe sets containing given attributes. We describe a heuristic algorithm for finding almost-safe sets given a size restriction, and show empirically that these sets outperform the trivial projection.We also show a connection between safe sets and Markov Random Fields and use it to furtherreduce the number of variables in the linear program, given some regularity assumptions on the frequent itemsets.     

2D compressed learning: support matrix machine with bilinear random projections

Machine Learning - Support matrix machine (SMM) is an efficient matrix classification method that can leverage the structure information within the matrix to improve the classification performance....     

Hybrid binary consensus in anonymous asynchronous systems using coins and failure detectors

The Journal of Supercomputing - Fault tolerance and agreement are essential capabilities of current distributed systems. In this context, Binary Consensus is a key problem. It is well-known that...     

Reconstructing a convex polygon from binary perspective projections

The data obtained from a binary perspective projection of a convex planar set is equivalent to the data obtained by tactile measurements using a certain kind of geometric probe composed of two line probes rotating about a common axis point. The reconstruction of a convex polygon (with V vertices) using this type of data is considered and a measurement strategy which guarantees a unique reconstruction following no more than 3V − 3 measurements is proposed. It is also shown that no strategy can achieve complete reconstruction using less than 3V − 3 measurements. Duality implies that the same reconstruction performance is achieved when probing with a composite finger probe.     

Sorted random projections for robust rotation-invariant texture classification

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS.     

基于二值随机森林的目标跟踪算法

针对压缩跟踪不能适应目标姿态变化导致跟踪失败的问题,提出了一种基于二值随机森林的目标跟踪算法。该算法对实时压缩跟踪算法的特征提取和分类这两个部分作了改进。首先,在梯度图像上进行多尺度滤波,获得目标的高维特征描述,利用一个稀疏矩阵进行压缩,获得表征目标的低维信息;然后,通过比较图像块对的大小,获得二值描述符,利用随机森林构造目标表示方法;最后,计算汉明匹配、寻找汉明距离最小的候选样本作为当前帧目标的状态估计,并在此基础上提取目标的特征来更新目标特征模板。与原算法相比,该算法对旋转、折叠、遮挡等姿态变化的目标跟踪性能更好。     

A binary Markov process model for random testing

A binary Markov process model is proposed for the random testing of software. This model is suggested for replacing the standard binomial distribution model, which is based on the easily-violated assumption of test runs being statistically independent of each other. In addition to a general result on the probability of having any specific number of software failures during testing, practical implications of the new model are also discussed. In particular, we demonstrate that, in general, the effect of a possible correlation between test runs cannot be ignored in estimating software reliability     

A new method for anomaly detection based on non-convex boundaries with random two-dimensional projections

The implementation of anomaly detection systems represents a key problem that has been focusing the efforts of scientific community. In this context, the use one-class techniques to model a training set of non-anomalous objects can play a significant role. One common approach to face the one-class problem is based on determining the geometric boundaries of the target set. More specifically, the use of convex hull combined with random projections offers good results but presents low performance when it is applied to non-convex sets. Then, this work proposes a new method that face this issue by implementing non-convex boundaries over each projection. The proposal was assessed and compared with the most common one-class techniques, over different sets, obtaining successful results.     

Distributed high dimensional information theoretical image registration via random projections

Information theoretical measures, such as entropy, mutual information, and various divergences, exhibit robust characteristics in image registration applications. However, the estimation of these quantities is computationally intensive in high dimensions. On the other hand, consistent estimation from pairwise distances of the sample points is possible, which suits random projection (RP) based low dimensional embeddings. We adapt the RP technique to this task by means of a simple ensemble method. To the best of our knowledge, this is the first distributed, RP based information theoretical image registration approach. The efficiency of the method is demonstrated through numerical examples.     

Memetic algorithms for reconstruction of binary images on triangular grids with 3 and 6 projections

When an image is given with only some measurable data, e.g., projections, the most important task is to reconstruct it, i.e., to find an image that provides the measured data. These tomographic problems are frequently used in the theory and applications of image processing. In this paper, memetic algorithms are investigated on triangular grids for the reconstruction of binary images using their three and six direction projections. The algorithm generates an initial population using the network flow algorithm for two of the input projections. The reconstructed images evolve towards an optimal solution or close to the optimal solution, by using crossover operators and guided mutation operators. The quality of the images is improved by using switching components and compactness operator.     

Reconstruction of 3-D binary tree-like structures from threemutually orthogonal projections

A method is developed to generate the 3D binary representation for a tree-like object from three mutually orthogonal projections. This is done by first backprojecting the binarized images from three directions and then iteratively removing artifacts in the backprojection. Three different algorithms have been developed: the Lagrange multiplier algorithm, the conjugate gradient algorithm, and the minimum-voxel representation algorithm (MRA). The performance of these algorithms under noise-free conditions is evaluated using mathematically projected images of a 3D tree structure. While all three algorithms are capable of producing a relatively accurate reconstruction, the MRA is superior not only because it requires the least amount of computation but also because it uses binary instead of gray-scale information in the input images. Reconstruction of 3D coronary arterial structures using MRA is further verified with X-ray images of a human chest phantom and shows a satisfactory performance. The result of this study should be valuable for 3D imaging of blood vessels     

On reliability of binary systems in a random environment

The influence of random environments to technical systems reliability is studied. A Markov model of reliability of the system that operates in a random Markov environment is proposed. General relations for stationary and non-stationary Quality of Service (QoS) characteristics of such system are given. Numerical study and comparison for a cold backup system operating in stable and random two-state environments are performed.     

Implementing regular cash with blind fixed-value electronic coins

We propose a generic framework for the development of an off-line peer-to-peer electronic payment system with fixed-value electronic coins. The proposed scheme simulates the issuance, circulation and characteristics of conventional cash and satisfies important security, privacy and usability requirements, offering a comparative advantage over other existing solutions. We exploit the strong security characteristics of a typical tamperproof device such as a smart card, and the trust services provided by a Public Key Infrastructure. The electronic coins are fixed, signed data structures that may be distributed, stored and processed by external applications only in their blind (encrypted) form. Non-divisibility is counterbalanced by the low complexity of the proposed solution, which enables the efficient exchange of multiple coins per transaction.     

Generating strictly binary trees at random based on convex polygon triangulations

A set of maximal non-intersecting diagonals can decompose a polygon into triangles, and the edges and diagonals can be converted into the external and internal nodes of a strictly binary tree. This paper gives algorithms to generate all types of triangulations and triangulations at random. Based on that, this paper gives an algorithm to generate strictly binary trees at random. The experimental results show that the numbers of various shapes of strictly binary trees generated are nearly equal. The algorithm to generate strictly binary trees at random can be transformed to the algorithm to randomly generate binary trees.     

基于马尔可夫随机场的加密二值图像有损压缩算法

尽管当前已有众多二值图像的压缩方法,但这些方法并不能直接应用于加密二值图像的压缩。在云计算、分布式处理等场景下,如何高效地对加密二值图像进行有损压缩仍然是一个挑战,而当前鲜有这方面的研究。针对此问题,提出了一种基于马尔可夫随机场(MRF)的加密二值图像有损压缩算法。该算法用MRF表征二值图像的空域统计特性,进而借助MRF及解压缩还原的像素推断加密二值图像压缩过程中被丢弃的像素。所提算法的发送方采用流密码对二值图像进行加密,云端先后利用分块均匀但块内随机的下抽样方式及低密度奇偶校验(LDPC)编码对加密二值图像进行压缩,接收方则通过构造包含解码、解密及MRF重构的联合因子图实现二值图像的有损重构。实验结果表明,所提算法获得了较好的压缩效率,在0.2~0.4 bpp压缩率时有损重构图像的比特误差率(BER)不超过5%;而与针对未加密原始二值图像的国际压缩标准JBIG2的压缩效率相比,所提算法的压缩效率与其相当。这些充分表明了所提算法的可行性与有效性。