Fast super-resolution algorithm using rotation-invariant ELBP classifier and hierarchical pattern matching

This paper proposes a fast super-resolution (SR) algorithm using content-adaptive two-dimensional (2D) finite impulse response (FIR) filters based on a rotation-invariant classifier. The proposed algorithm consists of a learning stage and an inference stage. In the learning stage, we cluster a sufficient number of low-resolution (LR) and high-resolution (HR) patch pairs into a specific number of groups using the rotation-invariant classifier, and choose a specific number of dominant clusters. Then, we compute the optimal 2D FIR filter(s) to synthesize a high-quality HR patch from an LR patch per cluster, and finally store the patch-adaptive 2D FIR filters in a dictionary. Also, we present a smart hierarchical addressing method for effective dictionary exploration in the inference stage. In the inference stage, the ELBP of each input LR patch is extracted in the same way as the learning stage, and the best matched FIR filter(s) to the input LR patch is found from the dictionary by the hierarchical addressing. Finally, we synthesize the HR patch by using the optimal 2D FIR filter. The experimental results show that the proposed algorithm produces better HR images than the existing SR methods, while providing fast running time.     

Bit-sequential array for pattern matching

This letter presents the design and analysis of a bit-sequential array for pattern matching applications. The architecture makes multiple use of each data sample, has built-in concurrency and pipelining, and is based on a highly modular design with only nearest neighbor connections between array modules. The array computes all occurrences of a pattern of length m, in the string of length n, in O(m + n) time and O(m) hardware. The pattern and the string are fed in sequentially and the match indicators come out in the same fashion, leading to a significant reduction in silicon area.     

Spectral scan matching for robot pose estimation

A novel scan matching method based on a spectral technique is proposed to estimate the robot pose by matching 2D range scans without an initial alignment. The proposed method finds consistent correspondence between two range scans, by considering how well their associated pairwise geometric relationships are satisfied.     

An unsupervised artificial immune classifier for multi/hyperspectral remote sensing imagery

A new method in computational intelligence namely artificial immune systems (AIS), which draw inspiration from the vertebrate immune system, have strong capabilities of pattern recognition. Even though AIS have been successfully utilized in several fields, few applications have been reported in remote sensing. Modern commercial imaging satellites, owing to their large volume of high-resolution imagery, offer greater opportunities for automated image analysis. Hence, we propose a novel unsupervised machine-learning algorithm namely unsupervised artificial immune classifier (UAIC) to perform remote sensing image classification. In addition to their nonlinear classification properties, UAIC possesses biological properties such as clonal selection, immune network, and immune memory. The implementation of UAIC comprises two steps: initially, the first clustering centers are acquired by randomly choosing from the input remote sensing image. Then, the classification task is carried out. This assigns each pixel to the class that maximizes stimulation between the antigen and the antibody. Subsequently, based on the class, the antibody population is evolved and the memory cell pool is updated by immune algorithms until the stopping criterion is met. The classification results are evaluated by comparing with four known algorithms: K-means, ISODATA, fuzzy K-means, and self-organizing map. It is shown that UAIC is an adaptive clustering algorithm, which outperforms other algorithms in all the three experiments we carried out.     

Reconfigurable approximate pattern matching architectures for nanotechnology

Approximate pattern matching is comparing an input pattern with a target pattern with a specified error tolerance. This ability to compensate for real-world sensor errors makes approximate pattern matching an ideal choice for a wide range of applications. This paper shows that two n-bit patterns may be matched with a single stage nanotechnology architecture using 2n+1 unit area resonant tunneling diodes (RTDs) and one RTD of 1.5 times the unit area. This architecture is reconfigurable for any target pattern and any error tolerance. We analyze current and power characteristics of the architecture and develop configurations to minimize the same. The robustness of the architecture to variations in device characteristics is also investigated.     

A recursive MISD architecture for pattern matching

Many applications require searching for multiple patterns in large data streams for which there is no preprocessed index to rely on for efficient lookups. An multiple instruction stream-single data stream (MISD) VLSI architecture that is based on a recursive divide and conquer approach to pattern matching is proposed. This architecture allows searching for multiple patterns simultaneously. The patterns can be constructed much like regular expressions, and add features such as requiring subpatterns to match in a specific order with some fuzzy distance between them, and the ability to allow errors according to prescribed thresholds, or ranges of such. The current implementation permits up to 127 simultaneous patterns at a clock frequency of 100 MHz, and does 1.024/spl times/10/sup 11/ character comparisons per second.     

On the upper bound of the probability of error of a linear pattern classifier for probabilistic pattern classes

Given a set of sample patterns for two pattern classes, some simple expressions for the upper bound of the probability of error for a linear pattern classifier and the optimal linear discriminant function minimizing the upper bound are obtained. Using these results, if the tolerable probability of error of classifying patterns in the two pattern classes is not smaller than this upper bound, not only a linear pattern classifier is known to be feasible, but also a satisfactory linear discriminant function is given. The results presented here are independent of the probability distribution of the patterns in the pattern classes. For some special cases, a smaller upper bound is found.     

高效的模式匹配算法研究

对入侵检测中模式匹配的方法进行了研究,分析了当前常用的模式匹配方法的弱点与不足,提出了一种基于三叉树结构的自动机方法,较好的避免了传统树型结构中由于节点数目变化而导致的不便。此外,利用匹配失败的信息,实现了跳跃匹配,提高了模式匹配的速度。*     

Context-dependent multilevel pattern matching for lossless image compression

In this paper, the multilevel pattern matching (MPM) code for compression of one-dimensional (1D) sequences is first generalized to compress two-dimensional (2D) images, resulting in a 2D multilevel pattern matching (MPM) code. It is shown that among all images of n pixels, the worst case redundancy of the 2D MPM code against any finite-template-based arithmetic code is O(1//spl radic/logn). This result contrasts unfavorably with the fact that among all 1D sequences of length n, the MPM code has a worst case redundancy of O(1/logn) against any finite-state arithmetic code; this is caused by the so-called 2D boundary effect. To alleviate the 2D boundary effect, we extend the 2D MPM code to the case of context modeling, yielding a context-dependent 2D MPM code. It is shown that among all images of n pixels, the context-dependent 2D MPM code has an O(1/logn) worst case redundancy against any finite-template-based arithmetic code satisfying a mild condition; this redundancy is better than that of the 2D MPM code without context models. Experimental results demonstrate that the context-dependent 2D MPM code significantly outperforms the 2D MPM code without context models for bi-level images. It is also demonstrated that, in terms of compression rates, the context-dependent 2D MPM code performs significantly better than the progressive coding mode of JBIG1 for both textual and bi-level images, and better than or comparably to the sequential coding mode of JBIG1 and JBIG2. In addition to its excellent compression performance, the context-dependent 2D MPM code allows progressive transmission of images.     

Quantum-based exact pattern matching algorithms for biological sequences

In computational biology, desired patterns are searched in large text databases, and an exact match is preferable. Classical benchmark algorithms obtain competent solutions for pattern matching in time, whereas quantum algorithm design is based on Grover's method, which completes the search in time. This paper briefly explains existing quantum algorithms and defines their processing limitations. Our initial work overcomes existing algorithmic constraints by proposing the quantum-based combined exact (QBCE) algorithm for the pattern-matching problem to process exact patterns. Next, quantum random access memory (QRAM) processing is discussed, and based on it, we propose the QRAM processing-based exact (QPBE) pattern-matching algorithm. We show that to find all occurrences of a pattern, the best case time complexities of the QBCE and QPBE algorithms are , and the exceptional worst case is bounded by . Thus, the proposed quantum algorithms achieve computational speedup. Our work is proved mathematically and validated with simulation, and complexity analysis demonstrates that our quantum algorithms are better than existing pattern-matching methods.     

基于面积特征的城区航空影像匹配方法

基于特征的影像匹配是一类极为广泛和重要的方法。已有的研究体现出了特征对于实施最为匹配的影像之一－－城区航空影像有着极为明显的优越性。文中给出一种新的匹配目标函数的构建,并基于此提出一种城区航空影像匹配算法,给出了算法的实现过程,并以部分东京城区图进行了试验,结果表明文中的方法具有乘法明了,可靠易行和抗旋转性的优点。     

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequently used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROIs containing biopsy-proven masses and 504 ROI's containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The authors' results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.     

时态图上图模式匹配研究综述

图数据模型是一种通用且已经得到广泛应用的可以处理数据对象间复杂关系的数据模型.然而,现有的大多数研究只关注静态图数据的结构或者顶点和边上的属性,忽略了现实中数据的一个重要特征即时态信息.忽略了时态信息将导致错过很多的有价值的信息,甚至得到错误的信息.作为图研究领域中重要的研究内容之一,图模式匹配问题的研究也需要考虑时态信息.考虑到图模式匹配研究的重要性以及时态信息对数据的重要性,本文根据时态图的快照模型、边流模型和区间模型以及时态图数据的时序性、持续性和演化性对时态图上图模式匹配问题进行了全面地综述,并总结了现有工作的不足.     

Solution of pattern matching inspection problem for grainy metallayers

In this paper, we demonstrate a new method of improving the defect controllability of grainy metal layers, for example, Hot-Al-Cu wiring, by enhancing the practical sensitivity of in-line inspectors. The problem in increasing practical sensitivity is the nuisance counts caused by grain boundaries, which do not cause electrical failures. We propose a method of decreasing the signal from the grain boundaries. On the grayscale images taken by pattern matching inspectors, grain boundaries are observed as gray on Hot-Al-Cu wiring, which is observed as white. If the illumination brightness is increased, the gray level of the grain boundaries becomes higher and saturates at the upper limit of grayscale, i.e., white. On the other hand, the gray level of the wiring stays white. Thus the signal, the grayscale difference between the grain boundaires and the wiring, can be decreased to almost zero. We call this phenomenon the "saturation effect." Our experimental results prove that the saturation effect due to illumination brightness optimization successfully cancels the nuisance counts caused by grain boundaries. The practical sensitivity limit is enhanced from 0.8 to 0.4 μm. This solution will greatly improve the defect controllability of grainy metal layers     

DEDUCE: A pattern matching method for automatic de-identification of Dutch medical text

In order to use medical text for research purposes, it is necessary to de-identify the text for legal and privacy reasons. We report on a pattern matching method to automatically de-identify medical text written in Dutch, which requires a low amount of effort to be hand tailored. First, a selection of Protected Health Information (PHI) categories is determined in cooperation with medical staff. Then, we devise a method for de-identifying all information in one of these PHI categories, that relies on lookup tables, decision rules and fuzzy string matching. Our de-identification method DEDUCE is validated on a test corpus of 200 nursing notes and 200 treatment plans obtained from the University Medical Center Utrecht (UMCU) in the Netherlands, achieving a total micro-averaged precision of 0.814, a recall of 0.916 and a F₁-score of 0.862. For person names, a recall of 0.964 was achieved, while no names of patients were missed.     

Respiratory-synchronized digital subtraction angiography based on a respiratory phase matching method

Catheter-based therapy is typically performed under fluoroscopic image observation. However, the vessel structure cannot be visualized on fluoroscopic images. To overcome this limitation, angiographic images are captured with a contrast agent, and digital subtraction angiography (DSA) images are acquired during the intervention. In thoracoabdominal DSA, patients have to hold their breath to match the respiratory phase during DSA acquisition. However, breath holding is difficult for some patients, including the elderly. If the organs move during DSA acquisition, artifacts can occur on DSA images, and the DSA acquisition must be performed again. In the present study, we describe and characterize a new respiratory phase matching method for respiratory-synchronized DSA acquisition under natural respiration. Preoperative angiographic and intra-operative fluoroscopic images were collected under natural respiration during the operation. For each fluoroscopic image, we used a pattern matching to select an angiographic image in the most similar respiratory phase. We then examined whether the method could be applied to both the free breathing DSA and the respiratory-synchronized roadmap. We found that our proposed respiratory phase matching method produced acceptable DSA images without breath holding, and that the processing could be performed in real time.     

Distributed pattern matching: a key to flexible and efficient P2P search

Flexibility and efficiency are the prime requirements for any P2P search mechanism. Existing P2P systems do not provide satisfactory solution for achieving these two conflicting goals. Unstructured search protocols (as adopted in Gnutella and FastTrack) provide search flexibility but exhibit poor performance characteristics. Structured search techniques (mostly Distributed Hash Table (DHT)-based), on the other hand, can efficiently route queries but support exact-match semantic only. In this paper we have defined Distributed Pattern Matching (DPM) problem and have presented a novel P2P architecture, named Distributed Pattern Matching System (DPMS), as a solution. Possible application areas of DPM include P2P search, service discovery and P2P databases. In DPMS, advertised patterns are replicated and aggregated by the peers, organized in a lattice-like hierarchy. Replication Improves availability and resilience to peer failure, and aggregation reduces storage overhead. An advertised pattern can be discovered using any subset of its 1-bits. Search complexity in DPMS is logarithmic to the total number of peers in the system. Advertisement overhead and guarantee on search completeness is comparable to that of DHT-based systems. We have presented mathematical analysis and simulation results to demonstrate the effectiveness of DPMS     

On the fly pattern matching for intrusion detection with Snort

Intrusion Detection Systems are becoming necessary tools for system administrators to protect their network. However they find more and more difficulties with high speed networks. To enhance their capacity and deal with evasion techniques, frequently used by hackers, we have introduced a new method to filter the network traffic. The detection method, while being stateful, processes each packet as soon as it is received. We have employed this strategy after a new classification of detection rules. Then, we have used efficient multisearch methods and suitable datastructure for signatures. The method has been successfully implemented as an extension of the Intrusion Detection System “Snort”.