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Full-Text Articles in Physical Sciences and Mathematics
Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
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 …
Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan
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 …
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
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.
A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
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, …
An Improved Template Matching Method For Object Detection, Duc Thanh Nguyen, Wanqing Li, Philip Ogunbona
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.
Electrical Defect Detection In Thermal Image, Ahmed Haidar, Geoffrey Asiegbu, Kamarul Hawari, Faisal Ibrahim
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 …
Fast Quality-Guided Phase Unwrapping Algorithm For 3d Profilometry Based On Object Image Edge Detection, Ke Chen, Jiangtao Xi, Yanguang Yu
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 …
A Novel Video-Based Smoke Detection Method Using Image Separation, Hongda Tian, Wanqing Li, Lei Wang, Philip Ogunbona
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, …
What Has My Classifier Learned? Visualizing The Classification Rules Of Bag-Of-Feature Model By Support Region Detection, Lingqiao Liu, Lei Wang
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 …
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
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 …
Allocated Harmonic Quantities As The Basis For Source Detection, Timothy Browne, Sarath Perera, Victor Gosbell
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 …
Automatic Detection Of Microcalcifications In Mammorgams Using A Fuzzy Classifier, Fazel Naghdy, Golshah Naghdy, Agustinus Drijarkara
Automatic Detection Of Microcalcifications In Mammorgams Using A Fuzzy Classifier, Fazel Naghdy, Golshah Naghdy, Agustinus Drijarkara
Associate Professor Golshah Naghdy
No abstract provided.
A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum
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), …
A Short Length Window-Based Method For Islanding Detection In Distributed Generation, Mollah Alam, Kashem Muttaqi, Abdesselam Bouzerdoum
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), …
Automatic Left Ventricle Detection In Echocardiographic Images For Deformable Contour Initialization, Cher Hau Seng, Ramazan Demirli, Moeness G. Amin, Jason L. Seachrist, Abdesselam Bouzerdoum
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 …
Human Detection With Contour-Based Local Motion Binary Patterns, Duc Thanh Nguyen, Philip Ogunbona, Wanqing Li
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 …
Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan
Smoke Detection In Videos Using Non-Redundant Local Binary Pattern-Based Features, Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, Ce Zhan
Professor Philip Ogunbona
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 …
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Illumination Invariant Face Detection Using Classifier Fusion, Alister Cordiner, Philip Ogunbona, Wanqing Li
Professor Philip Ogunbona
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.