Sonya Coleman - Academia.edu (original) (raw)

Papers by Sonya Coleman

2020 25th International Conference on Pattern Recognition (ICPR), 2021

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 2020

Activity recognition relates to the automatic visual detection and interpretation of human behavi... more Activity recognition relates to the automatic visual detection and interpretation of human behaviour and is emerging as an active domain of computer vision. It has important applications such as identifying individuals who are at risk of suicide in public locations such as bridges or railway stations. These individuals are known to exhibit easily observable activities and behaviours such as pacing, looking up and down the railway tracks, and leaving objects on the platform. In order to detect these behaviours, an approach to individual person activity recognition is needed which can run in real time and monitor multiple individuals in parallel. We present a method for human activity recognition using skeletal keypoints and investigate how using varying sample rates and sequence lengths impacts accuracy. The results show that for any given sequence length, optimising the sample rate can result in an overall increase in classification accuracy and improvement in run-time. Results demonstrate that finding the optimal time period over which to sample frames is more important than simply decreasing the number of frames sampled. Further, we show that keypoint based activity recognition approaches outperform other state of the art approaches. Finally, we show that this approach is fast enough for real time activity recognition when up to 14 people are present in the image whilst maintaining a high degree of accuracy.

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), 2019

Research by psychologists have shown that subjects had a preference for a side of a face when it ... more Research by psychologists have shown that subjects had a preference for a side of a face when it was expressing emotions. This paper seeks to find what accuracies can be attained when only a segment of the face is considered. We show that using one side of the face only reduces accuracy by 0.34% but at half the computationally time required. Various other sections of the face are evaluated for similar performance. We demonstrate that using smaller portions of the face have an expected computation reduction but dont suffer the same degree of accuracy loss. For evaluating we train with a Convolutional Neural Network. To test what portions of a facial image are useful, the full face, half face, eyes, single eye, mouth and half of the mouth are chosen. These images come from the JAFFE, CK+ and KDEF datasets.

Electronics Letters, 2018

A novel and efficient metric learning strategy for person re-identification is proposed. Person r... more A novel and efficient metric learning strategy for person re-identification is proposed. Person re-identification is formulated as a multi-domain learning problem. The assumption that the feature distributions from different camera views are the same is overthrown in this Letter. ID-based transfer component analysis (IDB-TCA) is proposed to learn a shared subspace, in which the differences in the feature distribution between source domain and target domain are significantly reduced. Experimental evaluation on the CUHK01 dataset demonstrates that metric learning with IDB-TCA embedded outperforms state-of-art metric methods for person re-identification.

IEEE transactions on neural networks and learning systems, Nov 14, 2015

A wash trade refers to the illegal activities of traders who utilize carefully designed limit ord... more A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors. Effective approaches for analyzing and detecting wash trade in a real-life market have yet to be developed. This paper analyzes and conceptualizes the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of...

2015 International Joint Conference on Neural Networks (IJCNN), 2015

ABSTRACT

2015 International Joint Conference on Neural Networks (IJCNN), 2015

Even though artificial vision has been in development for over half a century it still fares poor... more Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. Existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques.

This paper introduces the 'Introduction to Programming' course delivered in a number of l... more This paper introduces the 'Introduction to Programming' course delivered in a number of local secondary level schools over a period of 3 years. In the project we specifically aimed to address the issue of non-completion by targeting schools that have pupils who progress to Computer Science courses. Non-completion is a problem within Northern Ireland with a non-completion rate of 14.4% which is significantly higher than the UK benchmark of 9.7%. Within the Faculty of Computing and Engineering, there is a high rate of non-completion mainly due to a high rate of early leavers (who often indicate that the course was not what they expected) and those who fail the first year. We have worked to decrease the number of students failing first year over a three year period and in the academic year 08-09 came below the Faculty average. Initiatives such as small group tutorials, extended studies advice and inductions have helped to improve the retention figures. However, the non-completi...

Proceedings of the International Conference on Neural Computation Theory and Applications, 2014

Modelling biological systems is difficult due to insufficient knowledge about the internal compon... more Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013

ABSTRACT The surface properties of an object and the environment in which it is located are impor... more ABSTRACT The surface properties of an object and the environment in which it is located are important for robot grasping and manipulation. Physical contact with an object using tactile sensors can enable the retrieval of detailed information about the object, i.e. compressibility, surface texture and thermal properties. This paper describes a system that classifies materials based on their thermal properties alone, minimising the amount of manipulation required. Following acquisition of data from a sophisticated tactile sensor, the system uses an Artificial Neural Network (ANN) to classify materials based on representations of their thermal properties. The system was compared with human performance in the task of classifying materials and was found to perform better.

2011 IEEE International Conference on Mechatronics and Automation, 2011

ABSTRACT

Lecture Notes in Computer Science, 2013

A common requirement for image processing tasks is to achieve realtime performance. One approach ... more A common requirement for image processing tasks is to achieve realtime performance. One approach towards achieving this for tradition rectangular pixel-based images is to use an integral image that enables feature extraction at multiple scales in a fast and efficient manner. Alternative research has introduced the concept of hexagonal pixel-based images that closely mimic the human visual system: a real-time visual system. To enhance real time capability, we present a novel integral image for hexagonal pixel based images and associated multi-scale operator implementation that significantly accelerates the feature detection process. We demonstrate that the use of integral images enables significantly faster computation than the use of conventional spiral convolution or the use of neighbourhood address look-up tables.

Lecture Notes in Computer Science, 2009

For many years the concept of using hexagonal pixels for image capture has been investigated, and... more For many years the concept of using hexagonal pixels for image capture has been investigated, and several advantages of such an approach have been highlighted. Recently there has been a renewed interest in the use of hexagonal images, representation of architectures for such images and general hexagonal image processing. Therefore, we present multiscale hexagonal gradient operators, developed within the finite element framework, for use directly on hexagonal pixel-based images. We demonstrate these operators using two environments: a virtual hexagonal environment and the direct use of simulated hexagonal pixel-based images. In both scenarios, we evaluate the proposed operators and compare them with the use of standard image processing operators on typical square pixel-based images, demonstrating improved results in the case of simulated hexagonal pixel-based images.

2006 International Conference on Image Processing, 2006

The use of omnidirectional cameras has had a significant impact on the success of vision systems ... more The use of omnidirectional cameras has had a significant impact on the success of vision systems for video surveillance and autonomous robot navigation. Typically images obtained from such cameras are transformed to sparse panoramic images that are interpolated prior to low level image processing. We present a graph theoretic approach that enables image processing techniques, principally feature extraction, to be

2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014

Electronic trading in global markets and exchanges requires sophisticated communication and data ... more Electronic trading in global markets and exchanges requires sophisticated communication and data management systems. Novel computational infrastructures and trading strategies are required to support the massive amount of incoming streaming data, where the main problem is in latency management. Multi-agent Systems have been recognized as a promising solution to address complex problems in many areas such as biology, social sciences and financial markets and may provide powerful and flexible solutions for implementing trading engines. In addition, reconfigurable hardware based on Field Programmable Gate Arrays (FPGAs) offers many important performance benefits over software implementations, such as reducing decision making latency and high-throughput data processing. Robust and scalable trading engines can be developed by leveraging the benefits of reconfigurable FPGA platforms. This paper presents a multi-agent architecture in reconfigurable hardware for financial applications and the implementation of a trading engine for pre-trade analysis as a validation scenario. Performance results show that calculation of technical indicators and trading strategy evaluation to generate trading signals with a latency of 550 ns is achievable.

Lecture Notes in Computer Science, 2014

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

2011 11th International Conference on Intelligent Systems Design and Applications, 2011

IEEE journal of biomedical and health informatics, Jan 26, 2014

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injur... more Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior...

2011 6th International Conference on System of Systems Engineering, 2011

In this paper, a multi-robot networking paradigm is presented. This provides a general framework ... more In this paper, a multi-robot networking paradigm is presented. This provides a general framework for coordination among a group of robots. An experiment is conducted showing the effectiveness of the developed network paradigm where a robot controls a group of robots. A coordinated traffic scheduling method is proposed for mobile robots. The aim is to build onboard knowledge for autonomous robots without ranging sensors (sonar or laser range finder) and/or cameras. In this work, more emphasis is given on the exploration of interactions between a pair of robots. The robots share their positions, orientations and safety information and the decision of a robot depends on interactions of the forward safe paths (FSPs) of these robots. The properties of intersection of two straight lines are used to classify different situations. The proposed method is discussed in details with various combinations of scenarios. Simulation results are presented to show the effectiveness of the proposed method.

14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007

2020 25th International Conference on Pattern Recognition (ICPR), 2021

2020 IEEE 4th International Conference on Image Processing, Applications and Systems (IPAS), 2020

Activity recognition relates to the automatic visual detection and interpretation of human behavi... more Activity recognition relates to the automatic visual detection and interpretation of human behaviour and is emerging as an active domain of computer vision. It has important applications such as identifying individuals who are at risk of suicide in public locations such as bridges or railway stations. These individuals are known to exhibit easily observable activities and behaviours such as pacing, looking up and down the railway tracks, and leaving objects on the platform. In order to detect these behaviours, an approach to individual person activity recognition is needed which can run in real time and monitor multiple individuals in parallel. We present a method for human activity recognition using skeletal keypoints and investigate how using varying sample rates and sequence lengths impacts accuracy. The results show that for any given sequence length, optimising the sample rate can result in an overall increase in classification accuracy and improvement in run-time. Results demonstrate that finding the optimal time period over which to sample frames is more important than simply decreasing the number of frames sampled. Further, we show that keypoint based activity recognition approaches outperform other state of the art approaches. Finally, we show that this approach is fast enough for real time activity recognition when up to 14 people are present in the image whilst maintaining a high degree of accuracy.

2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), 2019

Research by psychologists have shown that subjects had a preference for a side of a face when it ... more Research by psychologists have shown that subjects had a preference for a side of a face when it was expressing emotions. This paper seeks to find what accuracies can be attained when only a segment of the face is considered. We show that using one side of the face only reduces accuracy by 0.34% but at half the computationally time required. Various other sections of the face are evaluated for similar performance. We demonstrate that using smaller portions of the face have an expected computation reduction but dont suffer the same degree of accuracy loss. For evaluating we train with a Convolutional Neural Network. To test what portions of a facial image are useful, the full face, half face, eyes, single eye, mouth and half of the mouth are chosen. These images come from the JAFFE, CK+ and KDEF datasets.

Electronics Letters, 2018

A novel and efficient metric learning strategy for person re-identification is proposed. Person r... more A novel and efficient metric learning strategy for person re-identification is proposed. Person re-identification is formulated as a multi-domain learning problem. The assumption that the feature distributions from different camera views are the same is overthrown in this Letter. ID-based transfer component analysis (IDB-TCA) is proposed to learn a shared subspace, in which the differences in the feature distribution between source domain and target domain are significantly reduced. Experimental evaluation on the CUHK01 dataset demonstrates that metric learning with IDB-TCA embedded outperforms state-of-art metric methods for person re-identification.

IEEE transactions on neural networks and learning systems, Nov 14, 2015

A wash trade refers to the illegal activities of traders who utilize carefully designed limit ord... more A wash trade refers to the illegal activities of traders who utilize carefully designed limit orders to manually increase the trading volumes for creating a false impression of an active market. As one of the primary formats of market abuse, a wash trade can be extremely damaging to the proper functioning and integrity of capital markets. The existing work focuses on collusive clique detections based on certain assumptions of trading behaviors. Effective approaches for analyzing and detecting wash trade in a real-life market have yet to be developed. This paper analyzes and conceptualizes the basic structures of the trading collusion in a wash trade by using a directed graph of traders. A novel method is then proposed to detect the potential wash trade activities involved in a financial instrument by first recognizing the suspiciously matched orders and then further identifying the collusions among the traders who submit such orders. Both steps are formulated as a simplified form of...

2015 International Joint Conference on Neural Networks (IJCNN), 2015

ABSTRACT

2015 International Joint Conference on Neural Networks (IJCNN), 2015

Even though artificial vision has been in development for over half a century it still fares poor... more Even though artificial vision has been in development for over half a century it still fares poorly when compared to biological vision. The processing capabilities of biological visual systems are vastly superior in terms of power, speed, and performance. Inspired by this robust performance artificial vision systems have sought to take inspiration from biology by modeling aspects of biological vision systems. Existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. In this work we use state-of-the-art fuzzy neural network techniques to accurately model the responses of retinal ganglion cells. We illustrate how a self-organising fuzzy neural network can accurately model ganglion cell behaviour, and are a viable alternative to traditional system identification techniques.

This paper introduces the 'Introduction to Programming' course delivered in a number of l... more This paper introduces the 'Introduction to Programming' course delivered in a number of local secondary level schools over a period of 3 years. In the project we specifically aimed to address the issue of non-completion by targeting schools that have pupils who progress to Computer Science courses. Non-completion is a problem within Northern Ireland with a non-completion rate of 14.4% which is significantly higher than the UK benchmark of 9.7%. Within the Faculty of Computing and Engineering, there is a high rate of non-completion mainly due to a high rate of early leavers (who often indicate that the course was not what they expected) and those who fail the first year. We have worked to decrease the number of students failing first year over a three year period and in the academic year 08-09 came below the Faculty average. Initiatives such as small group tutorials, extended studies advice and inductions have helped to improve the retention figures. However, the non-completi...

Proceedings of the International Conference on Neural Computation Theory and Applications, 2014

Modelling biological systems is difficult due to insufficient knowledge about the internal compon... more Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO), 2013

ABSTRACT The surface properties of an object and the environment in which it is located are impor... more ABSTRACT The surface properties of an object and the environment in which it is located are important for robot grasping and manipulation. Physical contact with an object using tactile sensors can enable the retrieval of detailed information about the object, i.e. compressibility, surface texture and thermal properties. This paper describes a system that classifies materials based on their thermal properties alone, minimising the amount of manipulation required. Following acquisition of data from a sophisticated tactile sensor, the system uses an Artificial Neural Network (ANN) to classify materials based on representations of their thermal properties. The system was compared with human performance in the task of classifying materials and was found to perform better.

2011 IEEE International Conference on Mechatronics and Automation, 2011

ABSTRACT

Lecture Notes in Computer Science, 2013

A common requirement for image processing tasks is to achieve realtime performance. One approach ... more A common requirement for image processing tasks is to achieve realtime performance. One approach towards achieving this for tradition rectangular pixel-based images is to use an integral image that enables feature extraction at multiple scales in a fast and efficient manner. Alternative research has introduced the concept of hexagonal pixel-based images that closely mimic the human visual system: a real-time visual system. To enhance real time capability, we present a novel integral image for hexagonal pixel based images and associated multi-scale operator implementation that significantly accelerates the feature detection process. We demonstrate that the use of integral images enables significantly faster computation than the use of conventional spiral convolution or the use of neighbourhood address look-up tables.

Lecture Notes in Computer Science, 2009

For many years the concept of using hexagonal pixels for image capture has been investigated, and... more For many years the concept of using hexagonal pixels for image capture has been investigated, and several advantages of such an approach have been highlighted. Recently there has been a renewed interest in the use of hexagonal images, representation of architectures for such images and general hexagonal image processing. Therefore, we present multiscale hexagonal gradient operators, developed within the finite element framework, for use directly on hexagonal pixel-based images. We demonstrate these operators using two environments: a virtual hexagonal environment and the direct use of simulated hexagonal pixel-based images. In both scenarios, we evaluate the proposed operators and compare them with the use of standard image processing operators on typical square pixel-based images, demonstrating improved results in the case of simulated hexagonal pixel-based images.

2006 International Conference on Image Processing, 2006

The use of omnidirectional cameras has had a significant impact on the success of vision systems ... more The use of omnidirectional cameras has had a significant impact on the success of vision systems for video surveillance and autonomous robot navigation. Typically images obtained from such cameras are transformed to sparse panoramic images that are interpolated prior to low level image processing. We present a graph theoretic approach that enables image processing techniques, principally feature extraction, to be

2014 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr), 2014

Electronic trading in global markets and exchanges requires sophisticated communication and data ... more Electronic trading in global markets and exchanges requires sophisticated communication and data management systems. Novel computational infrastructures and trading strategies are required to support the massive amount of incoming streaming data, where the main problem is in latency management. Multi-agent Systems have been recognized as a promising solution to address complex problems in many areas such as biology, social sciences and financial markets and may provide powerful and flexible solutions for implementing trading engines. In addition, reconfigurable hardware based on Field Programmable Gate Arrays (FPGAs) offers many important performance benefits over software implementations, such as reducing decision making latency and high-throughput data processing. Robust and scalable trading engines can be developed by leveraging the benefits of reconfigurable FPGA platforms. This paper presents a multi-agent architecture in reconfigurable hardware for financial applications and the implementation of a trading engine for pre-trade analysis as a validation scenario. Performance results show that calculation of technical indicators and trading strategy evaluation to generate trading signals with a latency of 550 ns is achievable.

Lecture Notes in Computer Science, 2014

The use of general descriptive names, registered names, trademarks, service marks, etc. in this p... more The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.

2011 11th International Conference on Intelligent Systems Design and Applications, 2011

IEEE journal of biomedical and health informatics, Jan 26, 2014

Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injur... more Despite the emerging applications of diffusion tensor imaging (DTI) to mild traumatic brain injury (mTBI), very few investigations have been reported related to temporal changes in quantitative diffusion patterns, which may help to assess recovery from head injury and the long term impact associated with cognitive and behavioural impairments caused by mTBI. Most existing methods are focused on detection of mTBI affected regions rather than quantification of temporal changes following head injury. Furthermore, most methods rely on large data samples as required for statistical analysis and thus are less suitable for individual case studies. In this work, we introduce an approach based on group spatial independent component analysis (GICA), in which the diffusion scalar maps from an individual mTBI subject and the average of a group of controls are arranged according to their data collection time points. In addition, we propose a constrained GICA (CGICA) model by introducing the prior...

2011 6th International Conference on System of Systems Engineering, 2011

In this paper, a multi-robot networking paradigm is presented. This provides a general framework ... more In this paper, a multi-robot networking paradigm is presented. This provides a general framework for coordination among a group of robots. An experiment is conducted showing the effectiveness of the developed network paradigm where a robot controls a group of robots. A coordinated traffic scheduling method is proposed for mobile robots. The aim is to build onboard knowledge for autonomous robots without ranging sensors (sonar or laser range finder) and/or cameras. In this work, more emphasis is given on the exploration of interactions between a pair of robots. The robots share their positions, orientations and safety information and the decision of a robot depends on interactions of the forward safe paths (FSPs) of these robots. The properties of intersection of two straight lines are used to classify different situations. The proposed method is discussed in details with various combinations of scenarios. Simulation results are presented to show the effectiveness of the proposed method.

14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007