Pedestrian detection for mobile bus surveillance

In this paper, we present a system for pedestrian detection involving scenes captured by mobile bus surveillance cameras in busy city streets. Our approach integrates scene localization, foreground and background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data. In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarities and second stage further clusters these aligned frames in terms of lighting. This produces clusters of images which are differential in viewpoint and lighting. A kernel density estimation (KDE) method for colour and gradient foreground-background separation are then used to construct background model for each image cluster which is subsequently used to detect all foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be identified. We have tested our system on a set of real bus video datasets and the experimental results verify that our system works well in practice.

Sparse representation with multi-manifold analysis for texture classification from few training images

Texture classification is one of the most important tasks in computer vision field and it has been extensively investigated in the last several decades. Previous texture classification methods mainly used the template matching based methods such as Support Vector Machine and k-Nearest-Neighbour for classification. Given enough training images the state-of-the-art texture classification methods could achieve very high classification accuracies on some benchmark databases. However, when the number of training images is limited, which usually happens in real-world applications because of the high cost of obtaining labelled data, the classification accuracies of those state-of-the-art methods would deteriorate due to the overfitting effect. In this paper we aim to develop a novel framework that could correctly classify textural images with only a small number of training images. By taking into account the repetition and sparsity property of textures we propose a sparse representation based multi-manifold analysis framework for texture classification from few training images. A set of new training samples are generated from each training image by a scale and spatial pyramid, and then the training samples belonging to each class are modelled by a manifold based on sparse representation. We learn a dictionary of sparse representation and a projection matrix for each class and classify the test images based on the projected reconstruction errors. The framework provides a more compact model than the template matching based texture classification methods, and mitigates the overfitting effect. Experimental results show that the proposed method could achieve reasonably high generalization capability even with as few as 3 training images, and significantly outperforms the state-of-the-art texture classification approaches on three benchmark datasets. © 2014 Elsevier B.V. All rights reserved.

Algorithm for web services matching

UDDI is a standard for publishing and discovery of web services. UDDI registries provide keyword searches for web services. The search functionality is very simple and fails to account for relationships between web services. In this paper, we propose an algorithm which retrieves closely related web services. The proposed algorithm is based on singular value decomposition (SVD) in linear algebra, which reveals semantic relationships among web services. The preliminary evaluation shows the effectiveness and feasibility of the algorithm.

Giving content to the principle of proportionality: happiness and pain as the universal currency for matching offence seriousness and penalty severity

The principle of proportionality prescribes that the punishment should equal the crime. It is one of the most important principles of sentencing. Yet, despite its widespread acceptance it offers no meaningful guide to sentencing. Hence penalty levels fluctuate greatly between jurisdictions and within jurisdictions. This is because there is no universally agreed criterion for measuring offence seriousness or penalty severity. This article suggests that the appropriate criteria for matching offence seriousness and penalty severity is the level of unhappiness or pain stemming from each of these impositions. Thus, for example, the level of pain meted out to a rape offender should equal the level of pain caused to a rape victim. Emerging scientific studies on human well-being and happiness show that human beings are similarly built in terms of the experiences that are either conducive or inimical to well-being. This commonality provides a strong foundation to be confident to make reasonably accurate predictions concerning the extent to which adverse events, such as being the victim of a criminal offence or subjected to a form of criminal sanction will stifle human flourishing. This will then allow us to match accurately offence seriousness and penalty level.

MDSM: microarray database schema matching using the Hungarian method

Current microarray databases use different terminologies and structures and thereby limit the sharing of data and collating of results between laboratories. Consequently, an effective integrated microarray data model is required. One important process to develop such an integrated database is schema matching. In this paper, we propose an effective schema matching approach called MDSM, to syntactically and semantically map attributes of different microarray schemas. The contribution from this work will be used later to create microarray global schemas. Since microarray data is complex, we use microarray ontology to improve the measuring accuracy of the similarity between attributes. The similarity relations can be represented as weighted bipartite graphs. We determine the best schema matching by computing the optimal matching in a bipartite graph using the Hungarian optimisation method. Experimental results show that our schema matching approach is effective and flexible to use in different kinds of database models such as; database schema, XML schema, and web site map. Finally, a case study on an existing public microarray schema is carried out using the proposed method.

Image matching using TI multi-wavelet transform

A multi-resolution image matching technique based on multiwavelets followed by a coarse to fine strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed by a geometric topological refining approach along with the uniqueness constraint whereas the illuminative variation is dealt by using windowed normalized correlation.

Fingerprint recognition system using hybrid matching techniques

With an increasing emphasis on the emerging automatic person identification application, biometrics based, especially fingerprint-based identification, is receiving a lot of attention. This research developed an automatic fingerprint recognition system (AFRS) based on a hybrid between minutiae and correlation based techniques to represent and to match fingerprint; it improved each technique individually. It was noticed that, in the hybrid approach, as a result of an improvement of minutiae extraction algorithm in post-process phase that combines the two algorithms, the performance of the minutia algorithm improved. An improvement in the ridge algorithm that used centre point in fingerprint instead of reference point was also observed. Experiments indicate that the hybrid technique performs much better than each algorithm individually.

Forecasting inflation, industrial output and exchange rates : a template study for India

This paper reports results from a forecasting study for inflation, industrial output and exchange rates for India. We cannot reject the null hypothesis for linearity for all series used except for the growth rate of the foreign exchange series and our analysis is based on linear models, ARIMA and bivariate transfer functions and restricted VAR. Forecasting performance is evaluated using the models’ root mean-squared error differences and Theil’s inequality coefficients from recursive origin static, fixed origin dynamic and rolling origin dynamic forecasts. For models based on weekly data, based on RMSEs, we find that the bivariate models improve upon the forecasts of the ARIMA model while for models based on monthly data the ARIMA model has almost always better performance. In choosing between the two bivariate models on the basis of RMSEs, our overall results tend to support the use of a restricted VAR, as this model had the best forecasting performance more frequently than the transfer function model.

Focused ion beam milling as a universal template technique for patterned growth of carbon nanotubes

Focused ion beam (FIB) milling system has been used to create nanosized patterns as the template for patterned growth of carbon nanotubes on Si substrate surface without predeposition of metal catalysts. Carbon nanotubes only nucleate and grow on the template under controlled pyrolysis of iron phthalocyanine at 1000 °C. The size, growth direction, and density of the patterned nanotubes can be controlled under different growth conditions and template sizes. Atomic force microscopy and electron microscopy analyses reveal that the selective growth on the FIB template is due to its special surface morphology and crystalline structure.

Substitution reactions of carbon nanotube template

Substitution reactions between carbon nanotube (CNT) template and SiO with the formation of carbon rich silicon oxide nanowires (SiO–C-NWs) have been investigated using transmission electron microscopy and x-ray energy dispersive spectroscopy. The reaction was carried out by thermal annealing at 1200 °C for 1 h of a mixture of silicon monoxide (SiO) and iron (II) phthalocyanine, FeC32N8H16 (FePc) powders. Multiwalled CNTs were produced first via pyrolysis of FePc at a lower temperature (1000 °C). SiO vapors reacted with the CNTs at higher temperatures to produce amorphous SiO–C-NWs with a uniform diameter and a length in tens of micrometers. The special bamboolike structure of the CNTs allows the reaction to start from the external surface of the tubes and transform each CNT into a solid nanowire section by section.

Transmission electron microscopy investigation of substitution reactions from carbon nanotube template to silicon carbide nanowires

Substitution reactions between multiwalled carbon nanotubes and silicon monoxide vapour have been investigated using transmission electron microscopy. Different reactions occurred inside the multiwalled nanotubes and on the nanotube external surfaces, resulting in the formation of silicon carbide nanowires with a core–shell structure. The substitution reaction process and end products are strongly affected by nanotube structures and a ball milling treatment of the starting materials.