Temporal-based support vector machines for facial expression recognition

When classifying a signal, ideally we want our classifier to trigger a large response when it encounters a positive example and have little to no response for all other examples. Unfortunately in practice this does not occur with responses fluctuating, often causing false alarms. There exists a myriad of reasons why this is the case, most notably not incorporating the dynamics of the signal into the classification. In facial expression recognition, this has been highlighted as one major research question. In this paper we present a novel technique which incorporates the dynamics of the signal which can produce a strong response when the peak expression is found and essentially suppresses all other responses as much as possible. We conducted preliminary experiments on the extended Cohn-Kanade (CK+) database which shows its benefits. The ability to automatically and accurately recognize facial expressions of drivers is highly relevant to the automobile. For example, the early recognition of “surprise” could indicate that an accident is about to occur; and various safeguards could immediately be deployed to avoid or minimize injury and damage. In this paper, we conducted initial experiments on the extended Cohn-Kanade (CK+) database which shows its benefits.

Large margin vector quantization

In this paper we describe the Large Margin Vector Quantization algorithm (LMVQ), which uses gradient ascent to maximise the margin of a radial basis function classifier. We present a derivation of the algorithm, which proceeds from an estimate of the class-conditional probability densities. We show that the key behaviour of Kohonen's well-known LVQ2 and LVQ3 algorithms emerge as natural consequences of our formulation. We compare the performance of LMVQ with that of Kohonen's LVQ algorithms on an artificial classification problem and several well known benchmark classification tasks. We find that the classifiers produced by LMVQ attain a level of accuracy that compares well with those obtained via LVQ1, LVQ2 and LVQ3, with reduced storage complexity. We indicate future directions of enquiry based on the large margin approach to Learning Vector Quantization.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

Fuzzy logic power system stabilizer in multimachine stability studies

Power system stabilizers (PSS) work well at the particular network configuration and steady state conditions for which they were designed. Once conditions change, their performance degrades. This can be overcome by an intelligent nonlinear PSS based on fuzzy logic. Such a fuzzy logic power system stabilizer (FLPSS) is developed, using speed and power deviation as inputs, and provides an auxiliary signal for the excitation system of a synchronous motor in a multimachine power system environment. The FLPSS's effect on the system damping is then compared with a conventional power system stabilizer's (CPSS) effect on the system. The results demonstrate an improved system performance with the FLPSS and also that the FLPSS is robust

Vector control of a high power induction machine

The design and implementation of a high-power (2 MW peak) vector control drive is described. The inverter switching frequency is low, resulting in high-harmonic-content current waveforms. A block diagram of the physical system is given, and each component is described in some detail. The problem of commanded slip noise sensitivity, inherent in high-power vector control drives, is discussed, and a solution is proposed. Results are given which demonstrate the successful functioning of the system

Service bid comparisons by fuzzy ranking in open railway market timetabling

In an open railway access market, the Infrastructure Provider (IP), upon the receipts of service bids from the Train Service Providers (TSPs), assigns track access rights according to its own business objectives and the merits of the bids; and produces the train service timetable through negotiations. In practice, IP chooses to negotiate with the TSPs one by one in such a sequence that IP optimizes its objectives. The TSP bids are usually very complicated, containing a large number of parameters in different natures. It is a difficult task even for an expert to give a priority sequence for negotiations from the contents of the bids. This study proposes the application of fuzzy ranking method to compare and prioritize the TSP bids in order to produce a negotiation sequence. The results of this study allow investigations on the behaviors of the stakeholders in bid preparation and negotiation, as well as evaluation of service quality in the open railway market.

Fuzzy logic traffic control at a road junction with time-varying flow rates

Fuzzy logic has been applied to control traffic at road junctions. A simple controller with one fixed rule-set is inadequate to minimise delays when traffic flow rate is time-varying and likely to span a wide range. To achieve better control, fuzzy rules adapted to the current traffic conditions are used.

Hierarchical fuzzy logic traffic control at a road junction

Traffic control at road junctions is one of the major concerns in most metropolitan cities. Controllers of various approaches are available and the required control action is the effective green-time assigned to each traffic stream within a traffic-light cycle. The application of fuzzy logic provides the controller with the capability to handle uncertain natures of the system, such as drivers’ behaviour and random arrivals of vehicles. When turning traffic is allowed at the junction, the number of phases in the traffic-light cycle increases. The additional input variables inevitably complicate the controller and hence slow down the decision-making process, which is critical in this real-time control problem. In this paper, a hierarchical fuzzy logic controller is proposed to tackle this traffic control problem at a 2-way road junction with turning traffic. The two levels of fuzzy logic controllers devise the minimum effective green-time and fine-tune it respectively at each phase of a traffic-light cycle. The complexity of the controller at each level is reduced with smaller rule-set. The performance of this hierarchical controller is examined by comparison with a fixed-time controller under various traffic conditions. Substantial delay reduction has been achieved as a result and the performance and limitation of the controller will be discussed.

A prioritised fuzzy constraint satisfaction approach to model agent negotiation for railway scheduling

With the recent regulatory reforms in a number of countries, railways resources are no longer managed by a single party but are distributed among different stakeholders. To facilitate the operation of train services, a train service provider (SP) has to negotiate with the infrastructure provider (IP) for a train schedule and the associated track access charge. This paper models the SP and IP as software agents and the negotiation as a prioritized fuzzy constraint satisfaction (PFCS) problem. Computer simulations have been conducted to demonstrate the effects on the train schedule when the SP has different optimization criteria. The results show that by assigning different priorities on the fuzzy constraints, agents can represent SPs with different operational objectives.

Hierarchical fuzzy logic traffic control at a road junction using genetic algorithms

Traffic control at a road junction by a complex fuzzy logic controller is investigated. The increase in the complexity of junction means more number of input variables must be taken into account, which will increase the number of fuzzy rules in the system. A hierarchical fuzzy logic controller is introduced to reduce the number of rules. Besides, the increase in the complexity of the controller makes formulation of the fuzzy rules difficult. A genetic algorithm based off-line leaning algorithm is employed to generate the fuzzy rules. The learning algorithm uses constant flow-rates as training sets. The system is tested by both constant and time-varying flow-rates. Simulation results show that the proposed controller produces lower average delay than a fixed-time controller does under various traffic conditions.

Accurate urban road model reconstruction from high resolution remotely sensed imagery based on Support Vector Machine and Gabor filters

In this paper, we presented an automatic system for precise urban road model reconstruction based on aerial images with high spatial resolution. The proposed approach consists of two steps: i) road surface detection and ii) road pavement marking extraction. In the first step, support vector machine (SVM) was utilized to classify the images into two categories: road and non-road. In the second step, road lane markings are further extracted on the generated road surface based on 2D Gabor filters. The experiments using several pan-sharpened aerial images of Brisbane, Queensland have validated the proposed method.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Characterisation of the tritrophic interactions between tobacco yellow dwarf virus, its vector and host-plants

Tobacco yellow dwarf virus (TbYDV, family Geminiviridae, genus Mastrevirus) is an economically important pathogen causing summer death and yellow dwarf disease in bean (Phaseolus vulgaris L.) and tobacco (Nicotiana tabacum L.), respectively. Prior to the commencement of this project, little was known about the epidemiology of TbYDV, its vector and host-plant range. As a result, disease control strategies have been restricted to regular poorly timed insecticide applications which are largely ineffective, environmentally hazardous and expensive. In an effort to address this problem, this PhD project was carried out in order to better understand the epidemiology of TbYDV, to identify its host-plant and vectors as well as to characterise the population dynamics and feeding physiology of the main insect vector and other possible vectors. The host-plants and possible leafhopper vectors of TbYDV were assessed over three consecutive growing seasons at seven field sites in the Ovens Valley, Northeastern Victoria, in commercial tobacco and bean growing properties. Leafhoppers and plants were collected and tested for the presence of TbYDV by PCR. Using sweep nets, twenty-three leafhopper species were identified at the seven sites with Orosius orientalis the predominant leafhopper. Of the 23 leafhopper species screened for TbYDV, only Orosius orientalis and Anzygina zealandica tested positive. Forty-two different plant species were also identified at the seven sites and tested. Of these, TbYDV was only detected in four dicotyledonous species, Amaranthus retroflexus, Phaseolus vulgaris, Nicotiana tabacum and Raphanus raphanistrum. Using a quadrat survey, the temporal distribution and diversity of vegetation at four of the field sites was monitored in order to assess the presence of, and changes in, potential host-plants for the leafhopper vector(s) and the virus. These surveys showed that plant composition and the climatic conditions at each site were the major influences on vector numbers, virus presence and the subsequent occurrence of tobacco yellow dwarf and bean summer death diseases. Forty-two plant species were identified from all sites and it was found that sites with the lowest incidence of disease had the highest proportion of monocotyledonous plants that are non hosts for both vector and the virus. In contrast, the sites with the highest disease incidence had more host-plant species for both vector and virus, and experienced higher temperatures and less rainfall. It is likely that these climatic conditions forced the leafhopper to move into the irrigated commercial tobacco and bean crop resulting in disease. In an attempt to understand leafhopper species diversity and abundance, in and around the field borders of commercially grown tobacco crops, leafhoppers were collected from four field sites using three different sampling techniques, namely pan trap, sticky trap and sweep net. Over 51000 leafhopper samples were collected, which comprised 57 species from 11 subfamilies and 19 tribes. Twentythree leafhopper species were recorded for the first time in Victoria in addition to several economically important pest species of crops other than tobacco and bean. The highest number and greatest diversity of leafhoppers were collected in yellow pan traps follow by sticky trap and sweep nets. Orosius orientalis was found to be the most abundant leafhopper collected from all sites with greatest numbers of this leafhopper also caught using the yellow pan trap. Using the three sampling methods mentioned above, the seasonal distribution and population dynamics of O. orientalis was studied at four field sites over three successive growing seasons. The population dynamics of the leafhopper was characterised by trimodal peaks of activity, occurring in the spring and summer months. Although O. orientalis was present in large numbers early in the growing season (September-October), TbYDV was only detected in these leafhoppers between late November and the end of January. The peak in the detection of TbYDV in O. orientalis correlated with the observation of disease symptoms in tobacco and bean and was also associated with warmer temperatures and lower rainfall. To understand the feeding requirements of Orosius orientalis and to enable screening of potential control agents, a chemically-defined artificial diet (designated PT-07) and feeding system was developed. This novel diet formulation allowed survival for O. orientalis for up to 46 days including complete development from first instar through to adulthood. The effect of three selected plant derived proteins, cowpea trypsin inhibitor (CpTi), Galanthus nivalis agglutinin (GNA) and wheat germ agglutinin (WGA), on leafhopper survival and development was assessed. Both GNA and WGA were shown to reduce leafhopper survival and development significantly when incorporated at a 0.1% (w/v) concentration. In contrast, CpTi at the same concentration did not exhibit significant antimetabolic properties. Based on these results, GNA and WGA are potentially useful antimetabolic agents for expression in genetically modified crops to improve the management of O. orientalis, TbYDV and the other pathogens it vectors. Finally, an electrical penetration graph (EPG) was used to study the feeding behaviour of O. orientalis to provide insights into TbYDV acquisition and transmission. Waveforms representing different feeding activity were acquired by EPG from adult O. orientalis feeding on two plant species, Phaseolus vulgaris and Nicotiana tabacum and a simple sucrose-based artificial diet. Five waveforms (designated O1-O5) were observed when O. orientalis fed on P. vulgaris, while only four (O1-O4) and three (O1-O3) waveforms were observed during feeding on N. tabacum and the artificial diet, respectively. The mean duration of each waveform and the waveform type differed markedly depending on the food source. This is the first detailed study on the tritrophic interactions between TbYDV, its leafhopper vector, O. orientalis, and host-plants. The results of this research have provided important fundamental information which can be used to develop more effective control strategies not only for O. orientalis, but also for TbYDV and other pathogens vectored by the leafhopper.