多尺度空间特征提取的脊柱图像拼接算法

针对脊柱图像视野有限,提出一种基于优化MSFE的脊柱图像自动拼接算法。设计出一种基于尺度因子变化的高斯卷积模板尺寸自适应调整以及双向配对办法;相似性度量采用城市距离;利用RANSAC算法去除错配,从而确定待拼接图像之间的变换参数,最后利用加权平均对图像融合。对实际取得的多幅脊柱图像拼接结果表明该算法具有很好的实时性和鲁棒性。     

A Novel Mixed-Valence Tetranuclear Iron-Oxygen Clusters in the Organically Templated Iron Phosphate [H3N(CH2)2NH3]2 Fe4O(PO4)4 ċH2O

A novel iron phosphate templated with ethylenediammonium cation, [H₃N(CH₂)₂NH₃]₂ Fe₄ O(PO₄)₄H₂O, has been synthesized hydrothermally and characterized by single-crystal X-ray diffraction, thermo-gravimetric analysis, magnetic susceptibility and Mossbauer spectroscopy. The compound crystallizes in the mono-clinic space group C2 with a= 30.3801(5),b= 10.1204(5),c= 10.0977(5) Å, = 107.712(1)°, V = 2957.5(4) ų and Z = 6. The structure contains 5 and 6-coordinated Fe atoms bridged by ₄-oxygen and PO₄ groups to form two types of mixed-valence Fe₄P₄ cubane-like clusters, which are connected via Fe- O- P bonds to give rise to intersecting channels that house the ethylenediammonium cations and water molecules. Room-temperature Mossbauer data confirm the presence of Fe^II and Fe^III.     

Porous TiC Ceramics Derived from Wood Template

A porous TiC ceramic with a woodlike structure was prepared from wood template through tetrabutyl titanate vacuum-infiltration. The tetrabutyl titanate was first decomposed to TiO₂, and then in-situ reaction between TiO₂ and carbon template took place in the cellular wall at high sintering temperature. The original structure of wood was retained. In addition, the dimensional shrinkage of the pores after the removal of the residual carbon was different according to the difference of the wood density.     

Gradient orientation pattern matching with the Hamming distance

This paper presents a novel pattern matching technique that is robust to illumination changes and the occlusion problem. The technique is based on the matching of gradient orientations in place of traditional image features such as intensities or gradients. Gradient orientations depend on the texture in an image. They are known to be insensitive to changes of image intensities that are often caused by time-varying illuminations or the auto-gain control (AGC) function of the camera. Moreover,the proposed method employs a voting strategy in the process of matching gradient orientations. The method works remarkably well even when a large part of the pattern is occluded with a foreign object. Consequently, the proposed method is robust to both irregular lighting conditions and the occlusion problem.     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

HEGM: A hierarchical elastic graph matching for hand gesture recognition

A hierarchical scheme for elastic graph matching applied to hand gesture recognition is proposed. The proposed algorithm exploits the relative discriminatory capabilities of visual features scattered on the images, assigning the corresponding weights to each feature. A boosting algorithm is used to determine the structure of the hierarchy of a given graph. The graph is expressed by annotating the nodes of interest over the target object to form a bunch graph. Three annotation techniques, manual, semi-automatic, and automatic annotation are used to determine the position of the nodes. The scheme and the annotation approaches are applied to explore the hand gesture recognition performance. A number of filter banks are applied to hand gestures images to investigate the effect of using different feature representation approaches. Experimental results show that the hierarchical elastic graph matching (HEGM) approach classified the hand posture with a gesture recognition accuracy of 99.85% when visual features were extracted by utilizing the Histogram of Oriented Gradient (HOG) representation. The results also provide the performance measures from the aspect of recognition accuracy to matching benefits, node positions correlation and consistency on three annotation approaches, showing that the semi-automatic annotation method is more efficient and accurate than the other two methods.     

Fast fingerprint identification for large databases

Fingerprint matching has emerged as an effective tool for human recognition due to the uniqueness, universality and invariability of fingerprints. Many different approaches have been proposed in the literature to determine faithfully if two fingerprint images belong to the same person. Among them, minutiae-based matchers highlight as the most relevant techniques because of their discriminative capabilities, providing precise results. However, performing a fingerprint identification over a large database can be an inefficient task due to the lack of scalability and high computing times of fingerprint matching algorithms.In this paper, we propose a distributed framework for fingerprint matching to tackle large databases in a reasonable time. It provides a general scheme for any kind of matcher, so that its precision is preserved and its time of response can be reduced.To test the proposed system, we conduct an extensive study that involves both synthetic and captured fingerprint databases, which have different characteristics, analyzing the performance of three well-known minutiae-based matchers within the designed framework. With the available hardware resources, our distributed model is able to address up to 400 000 fingerprints in approximately half a second. Additional details are provided at http://sci2s.ugr.es/ParallelMatching.     

k-NN classification of handwritten characters via accelerated GAT correlation

This paper addresses the problem of reinforcing the ability of the k-NN classification of handwritten characters via distortion-tolerant template matching techniques with a limited quantity of data. We compare three kinds of matching techniques: the conventional simple correlation, the tangent distance, and the global affine transformation (GAT) correlation. Although the k-NN classification method is straightforward and powerful, it consumes a lot of time. Therefore, to reduce the computational cost of matching in k-NN classification, we propose accelerating the GAT correlation method by reformulating its computational model and adopting efficient lookup tables. Recognition experiments performed on the IPTP CDROM1B handwritten numerical database show that the matching techniques of the simple correlation, the tangent distance, and the accelerated GAT correlation achieved recognition rates of 97.07%, 97.50%, and 98.70%, respectively. The computation time ratios of the tangent distance and the accelerated GAT correlation to the simple correlation are 26.3 and 36.5 to 1.0, respectively.     

以聚酰胺-胺树形分子为模板制备AgI纳米簇

本文以聚酰胺-胺(PAMAM)树形分子为模板,原位制备AgI纳米簇.系统地研究了AgI纳米簇制备过程中各种反应条件如树形分子端基、反应时间、Ag 与PAMAM摩尔比等对AgI纳米簇粒径的影响,分别用紫外-可见光谱、荧光光谱、透射电镜等对所制备的纳米簇进行表征.在相同的条件下,以G4.5-COOH3为模板较以G5.0-NH2为模板制备的AgI纳米簇粒径小、分布均匀,这主要取决于G4.5-COOCH3PAMAM树形分子所起的“内模板”作用.G4·5-COOH3树形分子浓度为1×10-5mol/L,Ag 与树形分子摩尔比为30∶1时所制备的AgI纳米簇的粒径分布均匀、稳定性好,室温避光可稳定存在两个月以上.     

An efficient parallel-network packet pattern-matching approach using GPUs

In the past few years, the increase in interest usage has been substantial. The high network bandwidth speed and the large amount of threats pose challenges to current network intrusion detection systems, which manage high amounts of network traffic and perform complicated packet processing. Pattern matching is a computationally intensive process included in network intrusion detection systems. In this paper, we present an efficient graphics processing unit (GPU)-based network packet pattern-matching algorithm by leveraging the computational power of GPUs to accelerate pattern-matching operations and subsequently increase the overall processing throughput. According to the experimental results, the proposed algorithm achieved a maximal traffic processing throughput of over 2 Gbit/s. The results demonstrate that the proposed GPU-based algorithm can effectively enhance the performance of network intrusion detection systems.