Robust facial feature extraction and matching

Automated feature extraction and correspondence determination is an extremely important problem in the face recognition community as it often forms the foundation of the normalisation and database construction phases of many recognition and verification systems. This paper presents a completely automatic feature extraction system based upon a modified volume descriptor. These features form a stable descriptor for faces and are utilised in a reversible jump Markov chain Monte Carlo correspondence algorithm to automatically determine correspondences which exist between faces. The developed system is invariant to changes in pose and occlusion and results indicate that it is also robust to minor face deformations which may be present with variations in expression.

Real-time power line extraction from unmanned aerial system video images

In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.

Sources of hydrochemical variability and implications associated with CSG water extraction

Coal Seam Gas (CSG) production is achieved by extracting groundwater to depressurize coal seam aquifers in order to promote methane gas desorption from coal micropores. CSG waters are characteristically alkaline, have a neutral pH (~7), are of the Na-HCO3-Cl type, and exhibit brackish salinity. In 2004, a CSG exploration company carried out a gas flow test in an exploration well located in Maramarua (Waikato Region, New Zealand). This resulted in 33 water samples exhibiting noteworthy chemical variations induced by pumping. This research identifies the main causes of hydrochemical variations in CSG water, makes recommendations to manage this effect, and discusses potential environmental implications. Hydrochemical variations were studied using Factor Analysis and this was supported with hydrochemical modelling and a laboratory experiment. This reveals carbon dioxide (CO2) degassing as the principal source of hydrochemical variability (about 33%). Factor Analysis also shows that major ion variations could also reflect changes in hydrochemical composition induced by different pumping regimes. Subsequent chloride, calcium, and TDS variations could be a consequence of analytical errors potentially committed during laboratory determinations. CSG water chemical variations due to degassing during pumping can be minimized with good completion and production techniques; variations due to sample degassing can be controlled by taking precautions during sampling, transit, storage and analysis. In addition, the degassing effect observed in CSG waters can lead to an underestimation of their potential environmental effect. Calcium precipitation due to exposure to normal atmospheric pressure results in a 23% increase in SAR values from Maramarua CSG water samples.

Ontology-based feature modeling for construction information extraction from a building information model

A building information model (BIM) provides a rich representation of a building's design. However, there are many challenges in getting construction-specific information from a BIM, limiting the usability of BIM for construction and other downstream processes. This paper describes a novel approach that utilizes ontology-based feature modeling, automatic feature extraction based on ifcXML, and query processing to extract information relevant to construction practitioners from a given BIM. The feature ontology generically represents construction-specific information that is useful for a broad range of construction management functions. The software prototype uses the ontology to transform the designer-focused BIM into a construction-specific feature-based model (FBM). The formal query methods operate on the FBM to further help construction users to quickly extract the necessary information from a BIM. Our tests demonstrate that this approach provides a richer representation of construction-specific information compared to existing BIM tools.

Optimisation of bio-oil extraction process from Beauty Leaf (Calophyllum inophyllum) oil seed as a second generation biodiesel source

The Beauty Leaf tree (Calophyllum inophyllum) is a potential source of non-edible vegetable oil for producing future generation biodiesel because of its ability to grow in a wide range of climate conditions, easy cultivation, high fruit production rate, and the high oil content in the seed. This plant naturally occurs in the coastal areas of Queensland and the Northern Territory in Australia, and is also widespread in south-east Asia, India and Sri Lanka. Although Beauty Leaf is traditionally used as a source of timber and orientation plant, its potential as a source of second generation biodiesel is yet to be exploited. In this study, the extraction process from the Beauty Leaf oil seed has been optimised in terms of seed preparation, moisture content and oil extraction methods. The two methods that have been considered to extract oil from the seed kernel are mechanical oil extraction using an electric powered screw press, and chemical oil extraction using n-hexane as an oil solvent. The study found that seed preparation has a significant impact on oil yields, especially in the screw press extraction method. Kernels prepared to 15% moisture content provided the highest oil yields for both extraction methods. Mechanical extraction using the screw press can produce oil from correctly prepared product at a low cost, however overall this method is ineffective with relatively low oil yields. Chemical extraction was found to be a very effective method for oil extraction for its consistence performance and high oil yield, but cost of production was relatively higher due to the high cost of solvent. However, a solvent recycle system can be implemented to reduce the production cost of Beauty Leaf biodiesel. The findings of this study are expected to serve as the basis from which industrial scale biodiesel production from Beauty Leaf can be made.

Extracting news blog hot topics based on the W2T methodology

Although topic detection and tracking techniques have made great progress, most of the researchers seldom pay more attention to the following two aspects. First, the construction of a topic model does not take the characteristics of different topics into consideration. Second, the factors that determine the formation and development of hot topics are not further analyzed. In order to correctly extract news blog hot topics, the paper views the above problems in a new perspective based on the W2T (Wisdom Web of Things) methodology, in which the characteristics of blog users, context of topic propagation and information granularity are investigated in a unified way. The motivations and features of blog users are first analyzed to understand the characteristics of news blog topics. Then the context of topic propagation is decomposed into the blog community, topic network and opinion network, respectively. Some important factors such as the user behavior pattern, opinion leader and network opinion are identified to track the development trends of news blog topics. Moreover, a blog hot topic detection algorithm is proposed, in which news blog hot topics are identified by measuring the duration, topic novelty, attention degree of users and topic growth. Experimental results show that the proposed method is feasible and effective. These results are also useful for further studying the formation mechanism of opinion leaders in blogspace.

Mapping semantic knowledge for unsupervised text categorisation

Text categorisation is challenging, due to the complex structure with heterogeneous, changing topics in documents. The performance of text categorisation relies on the quality of samples, effectiveness of document features, and the topic coverage of categories, depending on the employing strategies; supervised or unsupervised; single labelled or multi-labelled. Attempting to deal with these reliability issues in text categorisation, we propose an unsupervised multi-labelled text categorisation approach that maps the local knowledge in documents to global knowledge in a world ontology to optimise categorisation result. The conceptual framework of the approach consists of three modules; pattern mining for feature extraction; feature-subject mapping for categorisation; concept generalisation for optimised categorisation. The approach has been promisingly evaluated by compared with typical text categorisation methods, based on the ground truth encoded by human experts.

Pharmacokinetics of isofraxidin in rat plasma after oral administration of the extract of acanthopanax senticosus using HPLC with solid phase extraction method

High-performance liquid chromatography coupled with solid phase extraction method was developed for determination of isofraxidin in rat plasma after oral administration of Acanthopanax senticosus extract (ASE), and pharmacokinetic parameters of isofraxidin either in ASE or pure compound were measured. The HPLC analysis was performed on a Dikma Diamonsil RP(18) column (4.6 mm x 150 mm, 5 microm) with the isocratic elution of solvent A (acetonitrile) and solvent B (0.1% aqueous phosphoric acid, v/v) (A : B = 22 : 78) and the detection wavelength was set at 343 nm. The calibration curve was linear over the range of 0.156-15.625 microg/ml. The limit of detection was 60 ng/ml. The intra-day precision was 5.8%, and the inter-day precision was 6.0%. The recovery was 87.30+/-1.73%. When the dosage of ASE is equal to pure compound caculated by the amount of isofraxidin, it has been found to have two maximum concentrations in plasma while the pure compound only showed one peak in the plasma concentration-time curve. The determined content of isofraxidin in plasma after oral administration of ASE is the total contents of free isofraxidin and its precursors in ASE in vitro. The pharmacokinetic characteristics of ASE showed the priority of the extract and the properities of traditional Chinese medicine.

Pharmacokinetics of cimifugin in rat plasma after oral administration of the extract of Saposhnikovia divaricatae root : Determination of cimifugin by high performance liquid chromatography coupled with solid phase extraction

High performance liquid chromatography (HPLC) coupled with the solid phase extraction method was developed for determining cimifugin (a coumarin derivative; one of Saposhnikovia divaricatae's constituents) in rat plasma after oral administration of Saposhnikovia divaricatae extract (SDE), and the pharmacokinetics of cimifugin either in SDE or as a single compound was investigated. The HPLC analysis was performed on a commercially available column (4.6 mm x 200 mm, 5 pm) with the isocratic elution of solvent A (Methanol) and solvent B (Water) (A:B=60:40) and the detection wavelength was set at 250 nm. The calibration curve was linear over the range of 0.100-10.040 microg/mL. The limit of detection was 30 ng/mL. At the rat plasma concentrations of 0.402, 4.016, 10.040 microg/mL, the intra-day precision was 6.21%, 3.98%, and 2.23%; the inter-day precision was 7.59%, 4.26%, and 2.09%, respectively. The absolute recovery was 76.58%, 76.61%, and 77.67%, respectively. When the dosage of SDE was equal to the pure compound calculated by the amount of cimifugin, it was found to have two maximum peaks while the pure compound only showed one peak in the plasma concentration-time curve. The pharmacokinetic characteristics of SDE showed the superiority of the extract and the properties of traditional Chinese medicine.

Comparison of genomic DNA extraction techniques from whole blood samples : a time, cost and quality evaluation study

Genomic DNA obtained from patient whole blood samples is a key element for genomic research. Advantages and disadvantages, in terms of time-efficiency, cost-effectiveness and laboratory requirements, of procedures available to isolate nucleic acids need to be considered before choosing any particular method. These characteristics have not been fully evaluated for some laboratory techniques, such as the salting out method for DNA extraction, which has been excluded from comparison in different studies published to date. We compared three different protocols (a traditional salting out method, a modified salting out method and a commercially available kit method) to determine the most cost-effective and time-efficient method to extract DNA. We extracted genomic DNA from whole blood samples obtained from breast cancer patient volunteers and compared the results of the product obtained in terms of quantity (concentration of DNA extracted and DNA obtained per ml of blood used) and quality (260/280 ratio and polymerase chain reaction product amplification) of the obtained yield. On average, all three methods showed no statistically significant differences between the final result, but when we accounted for time and cost derived for each method, they showed very significant differences. The modified salting out method resulted in a seven- and twofold reduction in cost compared to the commercial kit and traditional salting out method, respectively and reduced time from 3 days to 1 hour compared to the traditional salting out method. This highlights a modified salting out method as a suitable choice to be used in laboratories and research centres, particularly when dealing with a large number of samples.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

Structured feature extraction using association rules

As of today, opinion mining has been widely used to iden- tify the strength and weakness of products (e.g., cameras) or services (e.g., services in medical clinics or hospitals) based upon people's feed- back such as user reviews. Feature extraction is a crucial step for opinion mining which has been used to collect useful information from user reviews. Most existing approaches only find individual features of a product without the structural relationships between the features which usually exists. In this paper, we propose an approach to extract features and feature relationship, represented as tree structure called a feature hi- erarchy, based on frequent patterns and associations between patterns derived from user reviews. The generated feature hierarchy profiles the product at multiple levels and provides more detailed information about the product. Our experiment results based on some popularly used review datasets show that the proposed feature extraction approach can identify more correct features than the baseline model. Even though the datasets used in the experiment are about cameras, our work can be ap- plied to generate features about a service such as the services in hospitals or clinics.

Pattern-based topics for document modelling in information filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features, and; (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models

Detecting news topics from microblogs using sequential pattern mining

This thesis presents a sequential pattern based model (PMM) to detect news topics from a popular microblogging platform, Twitter. PMM captures key topics and measures their importance using pattern properties and Twitter characteristics. This study shows that PMM outperforms traditional term-based models, and can potentially be implemented as a decision support system. The research contributes to news detection and addresses the challenging issue of extracting information from short and noisy text.

Mining specific features for acquiring user information needs

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.