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2012

Detection

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Full-Text Articles in Physical Sciences and Mathematics

A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum Dec 2012

A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum

Dr Fok Hing Chi Tivive

In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy …

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A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona Dec 2012

A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona

Associate Professor Wanqing Li

This paper proposes a novel weighted template matching method. It employs a generalized distance transform (GDT) and an orientation map (OM). The GDT allows us to weight the distance transform more on the strong edge points and the OM provides supplementary local orientation information for matching. Based on the matching method, a two-stage human detection method consisting of template matching and Bayesian verification is developed. Experimental results have shown that the proposed method can effectively reduce the false positive and false negative detection rates and perform superiorly in comparison to the conventional Chamfer matching method.

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Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona Dec 2012

Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona

Associate Professor Wanqing Li

Motivated by the non-linear manifold learning ability of the Kernel Principal Component Analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification tool, which implicitly learns a smooth manifold from noisy data that scatter over the feature space. For a new instance of HOG feature, its distance to the manifold that is measured by its reconstruction …

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Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li Dec 2012

Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li

Associate Professor Wanqing Li

This paper proposes a new two-stage human detection method involving matching and verification. A Bayesian framework is developed to verify the matching score obtained from a weighted distance measure. Performance evaluation indicates that the proposed method is able to utilize the flexible matching scheme and produce superior true positive, true negative and low misclassification rates.

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Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li Dec 2012

Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li

Associate Professor Wanqing Li

This paper presents a human detection method using contour- based local motion features. The local motion is encoded using a variant of the popular Local Binary Pattern (LBP) called Non-Redundant Local Binary Pattern (NRLBP) descriptor computed on the difference image of two consecutive frames. In addition, the local motion features are extracted along the human's boundary contour. Localising features on the contours has the advantage of utilizing a precise human shape description. A motivation of the proposed method is that most of informative movements are performed on boundary contours of the body parts, e.g. legs of pedestrians. Evaluation of the …

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Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan Dec 2012

Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan

Associate Professor Wanqing Li

This paper presents a novel and low complexity method for real-time video-based smoke detection. As a local texture operator, Non-Redundant Local Binary Pattern (NRLBP) is more discriminative and robust to illumination changes in comparison with original Local Binary Pattern (LBP), thus is employed to encode the appearance information of smoke. Non-Redundant Local Motion Binary Pattern (NRLMBP), which is computed on the difference image of consecutive frames, is introduced to capture the motion information of smoke. Experimental results show that NRLBP outperforms the original LBP in the smoke detection task. Furthermore, the combination of NRLBP and NRLMBP, which can be considered …

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Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li Dec 2012

Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li

Associate Professor Wanqing Li

An approach to the problem of illumination variations in face detection that uses classifier fusion is presented. Multiple face detectors are seperately trained for different illumination environments and their results are combined using a combination rule. To define the illumination environments, the training samples are clustered based on their illumination using unsupervised training. Different methods of clustering the samples and combining the outputs of the classifiers are examined. Experiments with the AR face database show that the proposed method achieves higher accuracy than the traditional monolithic face detection method.

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A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona Dec 2012

A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona

Associate Professor Wanqing Li

In the state-of-the-art video-based smoke detection methods, the representation of smoke mainly depends on the visual information in the current image frame. In the case of light smoke, the original background can be still seen and may deteriorate the characterization of smoke. The core idea of this paper is to demonstrate the superiority of using smoke component for smoke detection. In order to obtain smoke component, a blended image model is constructed, which basically is a linear combination of background and smoke components. Smoke opacity which represents a weighting of the smoke component is also defined. Based on this model, …

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Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona Dec 2012

Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona

Associate Professor Wanqing Li

Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: …

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An Improved Template Matching Method For Object Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona Dec 2012

An Improved Template Matching Method For Object Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona

Associate Professor Wanqing Li

This paper presents an improved template matching method that combines both spatial and orientation information in a simple and effective way. The spatial information is obtained through a generalized distance transform (GDT) that weights the distance transform more on the strong edge pixels and the orientation information is represented as an orientation map (OM) which is calculated from local gradient. We applied the proposed method to detect humans, cars, and maple leaves from images. The experimental results have shown that the proposed method outperforms the existing template matching methods and is robust against cluttered background.

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Electrical Defect Detection In Thermal Image, Ahmed Haidar, Geoffrey Asiegbu, Kamarul Hawari, Faisal Ibrahim Dec 2012

Electrical Defect Detection In Thermal Image, Ahmed Haidar, Geoffrey Asiegbu, Kamarul Hawari, Faisal Ibrahim

Dr Ahmed Mohamed Ahmed Haidar

Electrical and Electronic objects, which have a temperature of operating condition above absolute zero, emit infrared radiation. This radiation can be measured on the infrared spectral band of the electromagnetic spectrum using thermal imaging. Faults on electrical systems are expensive in terms of plant downtime, damage, loss of production or risk from fire. If the threshold temperature is timely detected, the electrical equipment failures can be avoided. This paper presents a straightforward approach for thermal analysis that examines power loads and large area thermal characteristics. A thermal imaging camera was used to collect thermal pictures of the tested system under …

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Ontology-Based Knowledge Representation For A P2p Multi-Agent Distributed Intrusion Detection System, Dayong Ye, Quan Bai, Minjie Zhang Dec 2012

Ontology-Based Knowledge Representation For A P2p Multi-Agent Distributed Intrusion Detection System, Dayong Ye, Quan Bai, Minjie Zhang

Associate Professor Minjie Zhang

Many research efforts on application of ontology in network security have been done in the past decade. However, they mostly stop at initial proposal or focus on framework design without detailed representation of intrusion or attack and relevant detection knowledge with ontology. In this paper, the design and implementation of ontology-based knowledge representation for a peer-to-peer multi-agent distributed intrusion detection system (ontology-based MADIDS) are introduced. An example which demonstrates the representation of an attack with ontology and the relevant detection process is also presented. In ontology-Based MADIDS, ontology technique enables peers in the system and agents in one peer to …

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Fast Quality-Guided Phase Unwrapping Algorithm For 3d Profilometry Based On Object Image Edge Detection, Ke Chen, Jiangtao Xi, Yanguang Yu Dec 2012

Fast Quality-Guided Phase Unwrapping Algorithm For 3d Profilometry Based On Object Image Edge Detection, Ke Chen, Jiangtao Xi, Yanguang Yu

Dr Yanguang Yu

A main challenge associated with 3-dimentional fringe pattern profilometry (3D-FPP) systems is the unwrapping of phase maps resulted from complex object surface shapes with both robustness and speed guaranteed. In this paper we propose a new quality-guided phase unwrapping algorithm. In contrast to the conventional quality-guided methods, we classify pixels on wrapped phase map into two types by detecting edge pixels on object image: high quality (HQ) pixels corresponding to smooth phase changes and low quality (LQ) ones to rough phase changes. In order to improve the computational efficiency, these two types of pixels are unwrapped by means of different …

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A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona Dec 2012

A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona

Dr Lei Wang

In the state-of-the-art video-based smoke detection methods, the representation of smoke mainly depends on the visual information in the current image frame. In the case of light smoke, the original background can be still seen and may deteriorate the characterization of smoke. The core idea of this paper is to demonstrate the superiority of using smoke component for smoke detection. In order to obtain smoke component, a blended image model is constructed, which basically is a linear combination of background and smoke components. Smoke opacity which represents a weighting of the smoke component is also defined. Based on this model, …

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What Has My Classifier Learned? Visualizing The Classification Rules Of Bag-Of-Feature Model By Support Region Detection, Lingqiao Liu, Lei Wang Dec 2012

What Has My Classifier Learned? Visualizing The Classification Rules Of Bag-Of-Feature Model By Support Region Detection, Lingqiao Liu, Lei Wang

Dr Lei Wang

In the past decade, the bag-of-feature model has established itself as the state-of-the-art method in various visual classification tasks. Despite its simplicity and high performance, it normally works as a black box and the classification rule is not transparent to users. However, to better understand the classification process, it is favorable to look into the black box to see how an image is recognized. To fill this gap, we developed a tool called Restricted Support Region Set (RSRS) Detection which can be utilized to visualize the image regions that are critical to the classification decision. More specifically, we define the …

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Are Drug Detection Dogs And Mass-Media Campaigns Likely To Be Effective Policy Responses To Psychostimulant Use And Related Harm? Results From An Agent-Based Simulation Model, David Moore, Lisa Maher, Christine Siokou, Rachael Green, Anne Dray, Rebecca Jenkinson, Susan Hudson, Gabriele Bammer, Pascal Perez, Paul Dietze Nov 2012

Are Drug Detection Dogs And Mass-Media Campaigns Likely To Be Effective Policy Responses To Psychostimulant Use And Related Harm? Results From An Agent-Based Simulation Model, David Moore, Lisa Maher, Christine Siokou, Rachael Green, Anne Dray, Rebecca Jenkinson, Susan Hudson, Gabriele Bammer, Pascal Perez, Paul Dietze

Professor Pascal Perez

Background Agent-based simulation models can be used to explore the impact of policy and practice on drug use and related consequences. In a linked paper (Perez et al., 2011), we described SimAmph, an agent-based simulation model for exploring the use of psychostimulants and related harm amongst young Australians. Methods In this paper, we use the model to simulate the impact of two policy scenarios on engagement in drug use and experience of drug-related harm: (i) the use of passive-alert detection (PAD) dogs by police at public venues and (ii) the introduction of a mass-media drug prevention campaign. Results The findings …

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Allocated Harmonic Quantities As The Basis For Source Detection, Timothy Browne, Sarath Perera, Victor Gosbell Nov 2012

Allocated Harmonic Quantities As The Basis For Source Detection, Timothy Browne, Sarath Perera, Victor Gosbell

Associate Professor Sarath Perera

A considerable body of literature examines assessment, from measurements, of whether it is the network or a customer installation which makes the greater contribution to harmonic distortion at a point of common coupling. However, the customer contribution to harmonic distortion at a point of common coupling depends heavily upon the definition chosen for that contribution. For example, expressing contributions as currents instead of voltages or vice versa may lead to large changes in results. Further, it can be shown that the harmonic voltage at the point of common coupling cannot be expressed independently of the network conditions, meaning that the …

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Automatic Detection Of Microcalcifications In Mammorgams Using A Fuzzy Classifier, Fazel Naghdy, Golshah Naghdy, Agustinus Drijarkara Nov 2012

Automatic Detection Of Microcalcifications In Mammorgams Using A Fuzzy Classifier, Fazel Naghdy, Golshah Naghdy, Agustinus Drijarkara

Associate Professor Golshah Naghdy

No abstract provided.

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A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum Nov 2012

A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum

Associate Professor Kashem Muttaqi

Distributed generation (DG) has recently drawn the interest to meet the increased load demand with minimum investment. But cohesive operation of these DG sources, in a grid-connected environment, gives rise to several issues during abnormal conditions of the utility system. This paper addresses the detection method of one such crucial event which is “islanding”. A short length window based Mahalanobis Distance method has been proposed in this paper to detect islanding. A trade-off between computational time and accuracy has been maintained to make it reliable and acceptable. In this method, network parameters such as rate of change of frequency (ROCOF), …

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A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum Nov 2012

A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum

Professor Salim Bouzerdoum

Distributed generation (DG) has recently drawn the interest to meet the increased load demand with minimum investment. But cohesive operation of these DG sources, in a grid-connected environment, gives rise to several issues during abnormal conditions of the utility system. This paper addresses the detection method of one such crucial event which is “islanding”. A short length window based Mahalanobis Distance method has been proposed in this paper to detect islanding. A trade-off between computational time and accuracy has been maintained to make it reliable and acceptable. In this method, network parameters such as rate of change of frequency (ROCOF), …

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A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum Nov 2012

A Car Detection System Based On Hierarchical Visual Features, Fok Hing Chi Tivive, Abdesselam Bouzerdoum

Professor Salim Bouzerdoum

In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy …

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Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi Nov 2012

Markov Random Fields For Abnormal Behavior Detection On Highways, Son Lam Phung, Philippe L. Bouttefroy, Abdesselam Bouzerdoum, Azeddine Beghdadi

Professor Salim Bouzerdoum

This paper introduces a new paradigm for abnormal behavior detection relying on the integration of contextual information in Markov random fields. Contrary to traditional methods, the proposed technique models the local density of object feature vector, therefore leading to simple and elegant criterion for behavior classification. We develop a Gaussian Markov random field mixture catering for multi-modal density and integrating the neighborhood behavior into a local estimate. The convergence of the random field is ensured by online learning through a stochastic clustering algorithm. The system is tested on an extensive dataset (over 2800 vehicles) for behavior modeling. The experimental results …

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Automatic Left Ventricle Detection In Echocardiographic Images For Deformable Contour Initialization, Cher Hau Seng, Ramazan Demirli, Moeness G. Amin, Jason L. Seachrist, Abdesselam Bouzerdoum Nov 2012

Automatic Left Ventricle Detection In Echocardiographic Images For Deformable Contour Initialization, Cher Hau Seng, Ramazan Demirli, Moeness G. Amin, Jason L. Seachrist, Abdesselam Bouzerdoum

Professor Salim Bouzerdoum

The accurate left ventricular boundary detection in echocardiographic images allow cardiologists to study and assess cardiomyopathy in patients. Due to the tedious and time consuming manner of manually tracing the borders, deformable models are generally used for left ventricle segmentations. However, most deformable models require a good initialization, which is usually outlined manually by the user. In this paper, we propose an automated left ventricle detection method for two-dimensional echocardiographic images that could serve as an initialization for deformable models. The proposed approach consists of pre-processing and post-processing stages, coupled with the watershed segmentation. The pre-processing stage enhances the overall …

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Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum Nov 2012

Reduced Training Of Convolutional Neural Networks For Pedestrian Detection, Giang Hoang Nguyen, Son Lam Phung, Abdesselam Bouzerdoum

Professor Salim Bouzerdoum

Pedestrian detection is a vision task with many practical applications in video surveillance, road safety, autonomous driving and military. However, it is much more difficult compared to the detection of other visual objects, because of the tremendous variations in the inner region as well as the outer shape of the pedestrian pattern. In this paper, we propose a pedestrian detection approach that uses convolutional neural network (CNN) to differentiate pedestrian and non-pedestrian patterns. Among several advantages, the CNN integrates feature extraction and classification into one single, fully adaptive structure. It can extract two-dimensional features at increasing scales, and it is …

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Direct Detection Of Additives And Degradation Products From Polymers By Liquid Extraction Surface Analysis Employing Chip-Based Nanospray Mass Spectrometry, Martin Paine, Phillip Barker, Shane A. Maclaughlin, Todd W. Mitchell, Stephen J. Blanksby Oct 2012

Direct Detection Of Additives And Degradation Products From Polymers By Liquid Extraction Surface Analysis Employing Chip-Based Nanospray Mass Spectrometry, Martin Paine, Phillip Barker, Shane A. Maclaughlin, Todd W. Mitchell, Stephen J. Blanksby

Stephen Blanksby

Rationale: Polymer-based surface coatings in outdoor applications experience accelerated degradation due to exposure to solar radiation, oxygen and atmospheric pollutants. These deleterious agents cause undesirable changes to the polymers aesthetic and mechanical properties reducing its lifetime. The use of antioxidants such as hindered amine light stabilisers (HALS) retard these degradative processes, however, mechanisms for HALS action and polymer degradation are poorly understood. Methods: Detection of the hindered amine light stabiliser (HALS) TINUVIN®123 (bis (1-octyloxy-2,2,6,6-tetramethyl-4-piperidyl) sebacate) and the polymer degradation products directly from a polyester-based coil coating was achieved by liquid extraction surface analysis (LESA) coupled to a triple quadrupole QTRAP® …

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Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona Sep 2012

Kernel Pca Of Hog Features For Posture Detection, Peng Cheng, Wanqing Li, Philip Ogunbona

Professor Philip Ogunbona

Motivated by the non-linear manifold learning ability of the Kernel Principal Component Analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification tool, which implicitly learns a smooth manifold from noisy data that scatter over the feature space. For a new instance of HOG feature, its distance to the manifold that is measured by its reconstruction …

Go to article

Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li Sep 2012

Human Detection Based On Weighted Template Matching, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li

Professor Philip Ogunbona

This paper proposes a new two-stage human detection method involving matching and verification. A Bayesian framework is developed to verify the matching score obtained from a weighted distance measure. Performance evaluation indicates that the proposed method is able to utilize the flexible matching scheme and produce superior true positive, true negative and low misclassification rates.

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Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li Sep 2012

Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li

Professor Philip Ogunbona

This paper presents a human detection method using contour- based local motion features. The local motion is encoded using a variant of the popular Local Binary Pattern (LBP) called Non-Redundant Local Binary Pattern (NRLBP) descriptor computed on the difference image of two consecutive frames. In addition, the local motion features are extracted along the human's boundary contour. Localising features on the contours has the advantage of utilizing a precise human shape description. A motivation of the proposed method is that most of informative movements are performed on boundary contours of the body parts, e.g. legs of pedestrians. Evaluation of the …

Go to article

Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona Sep 2012

Human Detection Using Local Shape And Non-Redundant Binary Patterns, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona

Professor Philip Ogunbona

Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: …

Go to article

A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona Sep 2012

A Novel Template Matching Method For Human Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona

Professor Philip Ogunbona

This paper proposes a novel weighted template matching method. It employs a generalized distance transform (GDT) and an orientation map (OM). The GDT allows us to weight the distance transform more on the strong edge points and the OM provides supplementary local orientation information for matching. Based on the matching method, a two-stage human detection method consisting of template matching and Bayesian verification is developed. Experimental results have shown that the proposed method can effectively reduce the false positive and false negative detection rates and perform superiorly in comparison to the conventional Chamfer matching method.

Go to article

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