Ognjen Arandjelovic | University of St Andrews - Academia.edu

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Ognjen Arandjelovic

University of St Andrews, Computer Science, Faculty Member

University of St Andrews, Computer Science, Faculty Member |

Computer Vision

700 Followers | 6 Following | 5 Co-authors | —Total Views

Papers

On the Value of Life

International Journal of Applied Philosophy, 2022

That life has value is a tenet eliciting all but universal agreement, be it amongst philosophers,... more That life has value is a tenet eliciting all but universal agreement, be it amongst philosophers, policy-makers, or the general public. Yet, when it comes to its employment in practice, especially in the context of policies which require the balancing of different moral choices—for example in health care, foreign aid, or animal rights related decisions—it takes little for cracks to appear and for disagreement to arise as to what the value of life actually means and how it should guide our actions in the real world. I argue that in no small part this state of affairs is a consequence of the infirmity of the foundations that the claim respecting the value of life supervenes upon once its theological foundations are abandoned. Hence, I depart radically from the contemporary thought and argue that life has no inherent value. Far from lowering the portcullis to Pandemonium, the abandonment of the quasi-Platonistic claim that life has intrinsic value, when understood and applied correctly, leads to a comprehensive, consistent, and compassionate ethical framework for understanding the related problems. I illustrate this using several hotly debated topics, including speciesism and show how the ideas I introduce help us to interpret people’s choices and to resolve outstanding challenges which present an insurmountable obstacle to the existing ethical theories.

A Systemic Challenge in Dietetics: Methodological Inadequacies, Erroneous Claims, and Misleading Interpretations, and Transparency of Post-Publication Scrutiny

Nutrition and Health, 2022

Background: Obesity is sweeping across the developed world. Yet, the public remains largely confu... more Background: Obesity is sweeping across the developed world. Yet, the public remains largely confused when it comes to the nature of dietary habits which would serve to counteract this trend. Aim: I highlight the responsibility that the scientific community bears when it comes to the confusion, and explain the kind of actions that are needed if the public trust in science is to be maintained. Methods: Starting from an example of a recently published and prominently featured article in a leading journal, I analyse various common methodological aspects of dietetics research and the consequent claims, contextualizing this within the broader environment which includes the scientific publishing process and the mainstream media. Results: Methodological inadequacies, erroneous claims, and misleading interpretations of findings are often found in dietetics research, highlighting the deficiencies of the system which fails to uphold the fundamental principles of scientific inquiry. Conclusion: It imperative that individual scientists speak out and challenge poor science, unsatisfactory publishing processes, and bombastic and misleading communication of research

A Whole-Slide is Greater Than the Sum of Its...Patches

AAAI Workshop on AI to Accelerate Science and Engineering, 2022

Muscular-invasive bladder cancer (MIBC) is a common form of cancer which can necessitate complex ... more Muscular-invasive bladder cancer (MIBC) is a common form of cancer which can necessitate complex treatment decisions. Different methods involving machine learning have been developed with the goal of improving and making MIBC diagnosis more specific, and thus limiting the amount of invasive testing needed for MIBC patients. A particularly fruitful direction of research involves the use of tissue images and the application of deep learning. In order to deal with extremely large whole slide images (WSIs), the state of the art methods approach the problem by using a patch-based convolutional neural network which takes small patches (often 256 × 256 pixels) of WSIs as input and provides a classification of cancerous or not-cancerous as output. Patch-to-slide classification is then often achieved by classifying a WSI as cancerous if and only if the majority of its patches are classified as cancerous. In this work we compare different approaches to the integration of local, patch based decisions, as a means of arriving at a robust global, WSI based classification. Our results suggest that an absolute, positive patch count based decisionmaking, with an appropriately learnt threshold, achieves the best results.

Highly Accurate Gaze Estimation Using a Consumer RGB-D Sensor

International Joint Conference on Artificial Intelligence, 2016

Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

Sci

With the increase in the availability of annotated X-ray image data, there has been an accompanyi... more With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age,...

Fairer Citation-Based Metrics

I describe a simple modification which can be applied to any citation count-based index (e.g. Hir... more I describe a simple modification which can be applied to any citation count-based index (e.g. Hirsch’s h-index) quantifying a researcher’s publication output. The key idea behind the proposed approach is that the merit for the citations of a paper should be distributed amongst its authors according to their relative contributions. In addition to producing inherently fairer metrics I show that the proposed modification has the potential to partially normalize for the unfair effects of honorary authorship and thus discourage this practice.

Hallucinating Optimal High-Dimensional Subspaces

Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance

In this work we consider face recognition from face motion manifolds. An information-theoretic ap... more In this work we consider face recognition from face motion manifolds. An information-theoretic approach with Resistor-Average Distance (RAD) as a dissimilarity measure between distributions of face images is proposed. We introduce a kernel-based algorithm that retains the simplicity of the closed-form expression for the RAD between two normal distributions, while allowing for modelling of complex, nonlinear manifolds. Additionally, it is shown how errors in the face registration process can be modelled to significantly improve recognition. Recognition performance of our method is experimentally demonstrated and shown to outperform state-of-the-art algorithms. Recognition rates of 97-100% are consistently achieved on databases of 35-90 people.

COVID-19 and Science Communication: The Recording and Reporting of Disease Mortality

Information, 2022

The ongoing COVID-19 pandemic has brought science to the fore of the public discourse and conside... more The ongoing COVID-19 pandemic has brought science to the fore of the public discourse and considering the complexity of the issues involved, with it also the challenge of effective and informative science communication. A particularly contentious topic, in that it is both highly emotional in and of itself, as well as in that it sits at the nexus of the decision-making process regarding the handling of the pandemic, which has effected lockdowns, social behaviour measures, business closures, and others, concerns the recording and the reporting of the disease mortality. To clarify a point which has caused much controversy and anger in the public debate, the first part of the present article discusses the very fundamentals underlying the issue of causative attribution with regards to mortality, lays out the foundations of the statistical means of mortality estimation, and concretizes these by analysing the recording and reporting practices adopted in England, and their widespread misrepresentations. The second part of the article is empirical in nature. I present data and an analysis of how COVID-19 mortality has been reported in the mainstream media in the UK and the USA, including a comparative analysis both across the two countries as well as across different media outlets. The findings clearly demonstrate a uniform and worrying lack of understanding of the relevant technical subject matter by the media in both countries. Of particular interest is the finding that with a remarkable regularity (ρ >0.998) the greater the number of articles a media outlet published on COVID-19 mortality, the greater the proportion of its articles misrepresented the disease mortality figures.

Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

ArXiv, 2016

Large amounts of electronic medical records collected by hospitals across the developed world off... more Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors' expectations and demonstrate that the high prediction accuracy is maintain...

Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and Its Significance

Every day consumers make decisions on whether or not to buy a product. In some cases the decision... more Every day consumers make decisions on whether or not to buy a product. In some cases the decision is based solely on price but in many instances the purchasing decision is more complex, and many more factors might be considered before the final commitment is made. In an effort to make purchasing more likely, in addition to considering the asking price, companies frequently introduce additional elements to the offer which are aimed at increasing the perceived value of the purchase. The goal of the present work is to examine using data driven machine learning, whether specific objective and readily measurable factors influence customers’ decisions. These factors inevitably vary to a degree from consumer to consumer so a combination of external factors, combined with the details processed at the time the price of a product is learnt, form a set of independent variables that contextualize purchasing behaviour. Using a large real world data set (which will be made public following the pu...

Live-wire 3D medical images segmentation

ArXiv, 2013

This report describes the design, implementation, evaluation and original enhancements to the Liv... more This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation. Live-Wire 2D employs a semi-automatic paradigm; the user is asked to select a few boundary points of the object to segment, to steer the process in the right direction, while the result is displayed in real time. In our implementation segmentation is extended to three dimensions by performing this process on a slice-by-slice basis. User's time and involvement is further reduced by allowing him to specify object contours in planes orthogonal to the slices. If these planes are chosen strategically, Live-Wire 3D can perform 2D segmentation in the plane of each slice automatically. This report also proposes two improvements to the original method, path heating and a new graph edge feature function based on variance of path properties along the boundary. We show that these improvements lead up to a 33% reduction in interaction with the user,...

Towards computer vision based ancient coin recognition in the wild — Automatic reliable image preprocessing and normalization

2017 International Joint Conference on Neural Networks (IJCNN), 2017

As an attractive area of application in the sphere of cultural heritage, in recent years automati... more As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifica...

Highly accurate gaze estimation using a consumer RGB-depth sensor

Determining the direction in which a person is looking is an important problem in a wide range of... more Determining the direction in which a person is looking is an important problem in a wide range of HCI applications. In this paper we describe a highly accurate algorithm that performs gaze estimation using an affordable and widely available device such as Kinect. The method we propose starts by performing accurate head pose estimation achieved by fitting a person specific morphable model of the face to depth data. The ordinarily competing requirements of high accuracy and high speed are met concurrently by formulating the fitting objective function as a combination of terms which excel either in accurate or fast fitting, and then by adaptively adjusting their relative contributions throughout fitting. Following pose estimation, pose normalization is done by re-rendering the fitted model as a frontal face. Finally gaze estimates are obtained through regression from the appearance of the eyes in synthetic, normalized images. Using EYEDIAP, the standard public dataset for the evaluatio...

Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibi... more In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in vid...

A note on the capability profile - localized increase in force production and its effect on overall resistance training performance

In this article I resolve formally the apparent paradox that arises in the context of the computa... more In this article I resolve formally the apparent paradox that arises in the context of the computational model of resistance exercise introduced in my previous work. Contrary to intuition, the model seems to allow for a localized increase in force production to degrade the ultimate exercise performance. I show this not to be the case.

Matching objects across the textured-smooth continuum

The problem of 3D object recognition is of immense practical importance, with the last decade wit... more The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of ...

Information and knowing when to forget it

2017 International Joint Conference on Neural Networks (IJCNN), 2017

In this paper we propose several novel approaches for incorporating forgetting mechanisms into se... more In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps unintuitively) benefit from the latter too. We demonstrate that if forgetting is implemented in a purposeful and date driven manner, there are a number of benefits which can be gained from discarding information. The framework we introduce is a general one and can be used with any baseline predictor of choice. Hence in this sense it is best described as a meta-algorithm. The method we described was developed through a series of steps which increase the adaptability of the model, while being data driven. We first discussed a weakly adaptive forgetting process which we termed passive ...

How Good is the Science That Informs Government Policy? A Lesson From the U.K.’s Response to 2020 CoV-2 Outbreak

Journal of Bioethical Inquiry

In an era when public faith in politicians is dwindling, yet trust in scientists remains relative... more In an era when public faith in politicians is dwindling, yet trust in scientists remains relatively high, governments are increasingly emphasizing the role of science based policy-making in response to challenges such as climate change and global pandemics. In this paper we question the quality of some scientific advice given to governments and the robustness and transparency of the entire framework which envelopes such advice, all of which raise serious ethical concerns. In particular we focus on the so-called Imperial Model which heavily influenced the government of the United Kingdom in devising its response to the COVID-19 crisis. We focus on and highlight several fundamental methodological flaws of the model, raise concerns as to the robustness of the system which permitted these to remain unchallenged, and discuss the relevant ethical consequences.

Light Curve Analysis From Kepler Spacecraft Collected Data

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, Jun 6, 2017

On the Value of Life

International Journal of Applied Philosophy, 2022

That life has value is a tenet eliciting all but universal agreement, be it amongst philosophers,... more That life has value is a tenet eliciting all but universal agreement, be it amongst philosophers, policy-makers, or the general public. Yet, when it comes to its employment in practice, especially in the context of policies which require the balancing of different moral choices—for example in health care, foreign aid, or animal rights related decisions—it takes little for cracks to appear and for disagreement to arise as to what the value of life actually means and how it should guide our actions in the real world. I argue that in no small part this state of affairs is a consequence of the infirmity of the foundations that the claim respecting the value of life supervenes upon once its theological foundations are abandoned. Hence, I depart radically from the contemporary thought and argue that life has no inherent value. Far from lowering the portcullis to Pandemonium, the abandonment of the quasi-Platonistic claim that life has intrinsic value, when understood and applied correctly, leads to a comprehensive, consistent, and compassionate ethical framework for understanding the related problems. I illustrate this using several hotly debated topics, including speciesism and show how the ideas I introduce help us to interpret people’s choices and to resolve outstanding challenges which present an insurmountable obstacle to the existing ethical theories.

A Systemic Challenge in Dietetics: Methodological Inadequacies, Erroneous Claims, and Misleading Interpretations, and Transparency of Post-Publication Scrutiny

Nutrition and Health, 2022

Background: Obesity is sweeping across the developed world. Yet, the public remains largely confu... more Background: Obesity is sweeping across the developed world. Yet, the public remains largely confused when it comes to the nature of dietary habits which would serve to counteract this trend. Aim: I highlight the responsibility that the scientific community bears when it comes to the confusion, and explain the kind of actions that are needed if the public trust in science is to be maintained. Methods: Starting from an example of a recently published and prominently featured article in a leading journal, I analyse various common methodological aspects of dietetics research and the consequent claims, contextualizing this within the broader environment which includes the scientific publishing process and the mainstream media. Results: Methodological inadequacies, erroneous claims, and misleading interpretations of findings are often found in dietetics research, highlighting the deficiencies of the system which fails to uphold the fundamental principles of scientific inquiry. Conclusion: It imperative that individual scientists speak out and challenge poor science, unsatisfactory publishing processes, and bombastic and misleading communication of research

A Whole-Slide is Greater Than the Sum of Its...Patches

AAAI Workshop on AI to Accelerate Science and Engineering, 2022

Muscular-invasive bladder cancer (MIBC) is a common form of cancer which can necessitate complex ... more Muscular-invasive bladder cancer (MIBC) is a common form of cancer which can necessitate complex treatment decisions. Different methods involving machine learning have been developed with the goal of improving and making MIBC diagnosis more specific, and thus limiting the amount of invasive testing needed for MIBC patients. A particularly fruitful direction of research involves the use of tissue images and the application of deep learning. In order to deal with extremely large whole slide images (WSIs), the state of the art methods approach the problem by using a patch-based convolutional neural network which takes small patches (often 256 × 256 pixels) of WSIs as input and provides a classification of cancerous or not-cancerous as output. Patch-to-slide classification is then often achieved by classifying a WSI as cancerous if and only if the majority of its patches are classified as cancerous. In this work we compare different approaches to the integration of local, patch based decisions, as a means of arriving at a robust global, WSI based classification. Our results suggest that an absolute, positive patch count based decisionmaking, with an appropriately learnt threshold, achieves the best results.

Highly Accurate Gaze Estimation Using a Consumer RGB-D Sensor

International Joint Conference on Artificial Intelligence, 2016

Nuances of Interpreting X-ray Analysis by Deep Learning and Lessons for Reporting Experimental Findings

Sci

With the increase in the availability of annotated X-ray image data, there has been an accompanyi... more With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age,...

Fairer Citation-Based Metrics

I describe a simple modification which can be applied to any citation count-based index (e.g. Hir... more I describe a simple modification which can be applied to any citation count-based index (e.g. Hirsch’s h-index) quantifying a researcher’s publication output. The key idea behind the proposed approach is that the merit for the citations of a paper should be distributed amongst its authors according to their relative contributions. In addition to producing inherently fairer metrics I show that the proposed modification has the potential to partially normalize for the unfair effects of honorary authorship and thus discourage this practice.

Hallucinating Optimal High-Dimensional Subspaces

Face Recognition from Face Motion Manifolds Using Robust Kernel Resistor-Average Distance

In this work we consider face recognition from face motion manifolds. An information-theoretic ap... more In this work we consider face recognition from face motion manifolds. An information-theoretic approach with Resistor-Average Distance (RAD) as a dissimilarity measure between distributions of face images is proposed. We introduce a kernel-based algorithm that retains the simplicity of the closed-form expression for the RAD between two normal distributions, while allowing for modelling of complex, nonlinear manifolds. Additionally, it is shown how errors in the face registration process can be modelled to significantly improve recognition. Recognition performance of our method is experimentally demonstrated and shown to outperform state-of-the-art algorithms. Recognition rates of 97-100% are consistently achieved on databases of 35-90 people.

COVID-19 and Science Communication: The Recording and Reporting of Disease Mortality

Information, 2022

The ongoing COVID-19 pandemic has brought science to the fore of the public discourse and conside... more The ongoing COVID-19 pandemic has brought science to the fore of the public discourse and considering the complexity of the issues involved, with it also the challenge of effective and informative science communication. A particularly contentious topic, in that it is both highly emotional in and of itself, as well as in that it sits at the nexus of the decision-making process regarding the handling of the pandemic, which has effected lockdowns, social behaviour measures, business closures, and others, concerns the recording and the reporting of the disease mortality. To clarify a point which has caused much controversy and anger in the public debate, the first part of the present article discusses the very fundamentals underlying the issue of causative attribution with regards to mortality, lays out the foundations of the statistical means of mortality estimation, and concretizes these by analysing the recording and reporting practices adopted in England, and their widespread misrepresentations. The second part of the article is empirical in nature. I present data and an analysis of how COVID-19 mortality has been reported in the mainstream media in the UK and the USA, including a comparative analysis both across the two countries as well as across different media outlets. The findings clearly demonstrate a uniform and worrying lack of understanding of the relevant technical subject matter by the media in both countries. Of particular interest is the finding that with a remarkable regularity (ρ >0.998) the greater the number of articles a media outlet published on COVID-19 mortality, the greater the proportion of its articles misrepresented the disease mortality figures.

Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?

ArXiv, 2016

Large amounts of electronic medical records collected by hospitals across the developed world off... more Large amounts of electronic medical records collected by hospitals across the developed world offer unprecedented possibilities for knowledge discovery using computer based data mining and machine learning. Notwithstanding significant research efforts, the use of this data in the prediction of disease development has largely been disappointing. In this paper we examine in detail a recently proposed method which has in preliminary experiments demonstrated highly promising results on real-world data. We scrutinize the authors' claims that the proposed model is scalable and investigate whether the tradeoff between prediction specificity (i.e. the ability of the model to predict a wide number of different ailments) and accuracy (i.e. the ability of the model to make the correct prediction) is practically viable. Our experiments conducted on a data corpus of nearly 3,000,000 admissions support the authors' expectations and demonstrate that the high prediction accuracy is maintain...

Machine Learning Based Prediction of Consumer Purchasing Decisions: The Evidence and Its Significance

Every day consumers make decisions on whether or not to buy a product. In some cases the decision... more Every day consumers make decisions on whether or not to buy a product. In some cases the decision is based solely on price but in many instances the purchasing decision is more complex, and many more factors might be considered before the final commitment is made. In an effort to make purchasing more likely, in addition to considering the asking price, companies frequently introduce additional elements to the offer which are aimed at increasing the perceived value of the purchase. The goal of the present work is to examine using data driven machine learning, whether specific objective and readily measurable factors influence customers’ decisions. These factors inevitably vary to a degree from consumer to consumer so a combination of external factors, combined with the details processed at the time the price of a product is learnt, form a set of independent variables that contextualize purchasing behaviour. Using a large real world data set (which will be made public following the pu...

Live-wire 3D medical images segmentation

ArXiv, 2013

This report describes the design, implementation, evaluation and original enhancements to the Liv... more This report describes the design, implementation, evaluation and original enhancements to the Live-Wire method for 2D and 3D image segmentation. Live-Wire 2D employs a semi-automatic paradigm; the user is asked to select a few boundary points of the object to segment, to steer the process in the right direction, while the result is displayed in real time. In our implementation segmentation is extended to three dimensions by performing this process on a slice-by-slice basis. User's time and involvement is further reduced by allowing him to specify object contours in planes orthogonal to the slices. If these planes are chosen strategically, Live-Wire 3D can perform 2D segmentation in the plane of each slice automatically. This report also proposes two improvements to the original method, path heating and a new graph edge feature function based on variance of path properties along the boundary. We show that these improvements lead up to a 33% reduction in interaction with the user,...

Towards computer vision based ancient coin recognition in the wild — Automatic reliable image preprocessing and normalization

2017 International Joint Conference on Neural Networks (IJCNN), 2017

As an attractive area of application in the sphere of cultural heritage, in recent years automati... more As an attractive area of application in the sphere of cultural heritage, in recent years automatic analysis of ancient coins has been attracting an increasing amount of research attention from the computer vision community. Recent work has demonstrated that the existing state of the art performs extremely poorly when applied on images acquired in realistic conditions. One of the reasons behind this lies in the (often implicit) assumptions made by many of the proposed algorithms — a lack of background clutter, and a uniform scale, orientation, and translation of coins across different images. These assumptions are not satisfied by default and before any further progress in the realm of more complex analysis is made, a robust method capable of preprocessing and normalizing images of coins acquired ‘in the wild’ is needed. In this paper we introduce an algorithm capable of localizing and accurately segmenting out a coin from a cluttered image acquired by an amateur collector. Specifica...

Highly accurate gaze estimation using a consumer RGB-depth sensor

Determining the direction in which a person is looking is an important problem in a wide range of... more Determining the direction in which a person is looking is an important problem in a wide range of HCI applications. In this paper we describe a highly accurate algorithm that performs gaze estimation using an affordable and widely available device such as Kinect. The method we propose starts by performing accurate head pose estimation achieved by fitting a person specific morphable model of the face to depth data. The ordinarily competing requirements of high accuracy and high speed are met concurrently by formulating the fitting objective function as a combination of terms which excel either in accurate or fast fitting, and then by adaptively adjusting their relative contributions throughout fitting. Following pose estimation, pose normalization is done by re-rendering the fitted model as a frontal face. Finally gaze estimates are obtained through regression from the appearance of the eyes in synthetic, normalized images. Using EYEDIAP, the standard public dataset for the evaluatio...

Face recognition from video

In spite of over two decades of intense research, illumination and pose invariance remain prohibi... more In spite of over two decades of intense research, illumination and pose invariance remain prohibitively challenging aspects of face recognition for most practical applications. The objective of this work is to recognize faces using video sequences both for training and recognition input, in a realistic, unconstrained setup in which lighting, pose and user motion pattern have a wide variability and face images are of low resolution. In particular there are three areas of novelty: (i) we show how a photometric model of image formation can be combined with a statistical model of generic face appearance variation, learnt offline, to generalize in the presence of extreme illumination changes; (ii) we use the smoothness of geodesically local appearance manifold structure and a robust same-identity likelihood to achieve invariance to unseen head poses; and (iii) we introduce an accurate video sequence "reillumination" algorithm to achieve robustness to face motion patterns in vid...

A note on the capability profile - localized increase in force production and its effect on overall resistance training performance

In this article I resolve formally the apparent paradox that arises in the context of the computa... more In this article I resolve formally the apparent paradox that arises in the context of the computational model of resistance exercise introduced in my previous work. Contrary to intuition, the model seems to allow for a localized increase in force production to degrade the ultimate exercise performance. I show this not to be the case.

Matching objects across the textured-smooth continuum

The problem of 3D object recognition is of immense practical importance, with the last decade wit... more The problem of 3D object recognition is of immense practical importance, with the last decade witnessing a number of breakthroughs in the state of the art. Most of the previous work has focused on the matching of textured objects using local appearance descriptors extracted around salient image points. The recently proposed bag of boundaries method was the first to address directly the problem of matching smooth objects using boundary features. However, no previous work has attempted to achieve a holistic treatment of the problem by jointly using textural and shape features which is what we describe herein. Due to the complementarity of the two modalities, we fuse the corresponding matching scores and learn their relative weighting in a data specific manner by optimizing discriminative performance on synthetically distorted data. For the textural description of an object we adopt a representation in the form of a histogram of SIFT based visual words. Similarly the apparent shape of ...

Information and knowing when to forget it

2017 International Joint Conference on Neural Networks (IJCNN), 2017

In this paper we propose several novel approaches for incorporating forgetting mechanisms into se... more In this paper we propose several novel approaches for incorporating forgetting mechanisms into sequential prediction based machine learning algorithms. The broad premise of our work, supported and motivated in part by recent findings stemming from neurology research on the development of human brains, is that knowledge acquisition and forgetting are complementary processes, and that learning can (perhaps unintuitively) benefit from the latter too. We demonstrate that if forgetting is implemented in a purposeful and date driven manner, there are a number of benefits which can be gained from discarding information. The framework we introduce is a general one and can be used with any baseline predictor of choice. Hence in this sense it is best described as a meta-algorithm. The method we described was developed through a series of steps which increase the adaptability of the model, while being data driven. We first discussed a weakly adaptive forgetting process which we termed passive ...

How Good is the Science That Informs Government Policy? A Lesson From the U.K.’s Response to 2020 CoV-2 Outbreak

Journal of Bioethical Inquiry

In an era when public faith in politicians is dwindling, yet trust in scientists remains relative... more In an era when public faith in politicians is dwindling, yet trust in scientists remains relatively high, governments are increasingly emphasizing the role of science based policy-making in response to challenges such as climate change and global pandemics. In this paper we question the quality of some scientific advice given to governments and the robustness and transparency of the entire framework which envelopes such advice, all of which raise serious ethical concerns. In particular we focus on the so-called Imperial Model which heavily influenced the government of the United Kingdom in devising its response to the COVID-19 crisis. We focus on and highlight several fundamental methodological flaws of the model, raise concerns as to the robustness of the system which permitted these to remain unchallenged, and discuss the relevant ethical consequences.

Light Curve Analysis From Kepler Spacecraft Collected Data

Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, Jun 6, 2017

Determining Chess Game State From an Image

Identifying the configuration of chess pieces from an image of a chessboard is a problem in compu... more Identifying the configuration of chess pieces from an image of a chessboard is a problem in computer vision that has not yet been solved accurately. However, it is important for helping amateur chess players improve their games by facilitating automatic computer analysis without the overhead of manually entering the pieces. Current approaches are limited by the lack of large datasets and are not designed to adapt to unseen chess sets. This paper puts forth a new dataset synthesised from a 3D model that is an order of magnitude larger than existing ones. Trained on this dataset, a novel end-to-end chess recognition system is presented that combines traditional computer vision techniques with deep learning. It localises the chessboard using a RANSAC-based algorithm that computes a projective transformation of the board onto a regular grid. Using two convolutional neural networks, it then predicts an occupancy mask for the squares in the warped image and finally classifies the pieces. The described system achieves an error rate of 0.23% per square on the test set, 28 times better than the current state of the art. Further, a few-shot transfer learning approach is developed that is able to adapt the inference system to a previously unseen chess set using just two photos of the starting position, obtaining a per-square accuracy of 99.83% on images of that new chess set. The dataset is released publicly; code and trained models are available at this URL.