# Dr Anil Anthony Bharath

Faculty of EngineeringDepartment of Bioengineering

Professor of Biologically Inspired Computation & Inference

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### Contact

+44 (0)20 7594 5463a.bharath

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### Location

4.31Royal School of MinesSouth Kensington Campus

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## Publications

Publication Type
Year
to

145 results found

Sorteberg WE, Garasto S, Cantwell CC, Bharath AAet al., 2020, Approximating the Solution of Surface Wave Propagation Using Deep Neural Networks

Poster

Kothari S, Gionfrida L, Bharath AA, Abraham Set al., 2019, Artificial Intelligence (AI) and rheumatology: a potential partnership., Rheumatology (Oxford), Vol: 58, Pages: 1894-1895

Journal article

Creswell A, Bharath AA, 2019, Denoising adversarial autoencoders, IEEE Transactions on Neural Networks and Learning Systems, Vol: 30, Pages: 968-984, ISSN: 2162-2388

Unsupervised learning is of growing interest becauseit unlocks the potential held in vast amounts of unlabelled data tolearn useful representations for inference. Autoencoders, a formof generative model, may be trained by learning to reconstructunlabelled input data from a latent representation space. Morerobust representations may be produced by an autoencoderif it learns to recover clean input samples from corruptedones. Representations may be further improved by introducingregularisation during training to shape the distribution of theencoded data in the latent space. We suggestdenoising adversarialautoencoders, which combine denoising and regularisation, shap-ing the distribution of latent space using adversarial training.We introduce a novel analysis that shows how denoising maybe incorporated into the training and sampling of adversarialautoencoders. Experiments are performed to assess the contri-butions that denoising makes to the learning of representationsfor classification and sample synthesis. Our results suggest thatautoencoders trained using a denoising criterion achieve higherclassification performance, and can synthesise samples that aremore consistent with the input data than those trained withouta corruption process.

Journal article

Wang Y, Yang Y, Liu Y-X, Bharath AAet al., 2019, A Recursive Ensemble Learning Approach With Noisy Labels or Unlabeled Data, IEEE ACCESS, Vol: 7, Pages: 36459-36470, ISSN: 2169-3536

Journal article

Uslu F, Bharath AA, 2019, A recursive Bayesian approach to describe retinal vasculature geometry, Pattern Recognition, Vol: 87, Pages: 157-169, ISSN: 0031-3203

Deep networks have recently seen significant application to the analysis of medical image data, particularly for segmentation and disease classification. However, there are many situations in which the purpose of analysing a medical image is to perform parameter estimation, assess connectivity or determine geometric relationships. Some of these tasks are well served by probabilistic trackers, including Kalman and particle filters. In this work, we explore how the probabilistic outputs of a single-architecture deep network may be coupled to a probabilistic tracker, taking the form of a particle filter. The tracker provides information not easily available with current deep networks, such as a unique ordering of points along vessel centrelines and edges, whilst the construction of observation models for the tracker is simplified by the use of a deep network. We use the analysis of retinal images in several datasets as the problem domain, and compare estimates of vessel width in a standard dataset (REVIEW) with manually determined measurements.

Journal article

Cantwell C, Mohamied Y, Tzortzis K, Garasto S, Houston C, Chowdhury R, Ng F, Bharath A, Peters Net al., 2019, Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling, Computers in Biology and Medicine, Vol: 104, Pages: 339-351, ISSN: 0010-4825

We review some of the latest approaches to analysing cardiac electrophysiology data using machine learning and predictive modelling. Cardiac arrhythmias, particularly atrial fibrillation, are a major global healthcare challenge. Treatment is often through catheter ablation, which involves the targeted localised destruction of regions of the myocardium responsible for initiating or perpetuating the arrhythmia. Ablation targets are either anatomically defined, or identified based on their functional properties as determined through the analysis of contact intracardiac electrograms acquired with increasing spatial density by modern electroanatomic mapping systems. While numerous quantitative approaches have been investigated over the past decades for identifying these critical curative sites, few have provided a reliable and reproducible advance in success rates. Machine learning techniques, including recent deep-learning approaches, offer a potential route to gaining new insight from this wealth of highly complex spatio-temporal information that existing methods struggle to analyse. Coupled with predictive modelling, these techniques offer exciting opportunities to advance the field and produce more accurate diagnoses and robust personalised treatment. We outline some of these methods and illustrate their use in making predictions from the contact electrogram and augmenting predictive modelling tools, both by more rapidly predicting future states of the system and by inferring the parameters of these models from experimental observations.

Journal article

Sorteberg W, Garasto S, Cantwell C, Bharath Aet al., Approximating the solution of Surface Wave Propagation Using Deep Neural Networks, INNS Big Data and Deep Learning 2019, Publisher: Springer, ISSN: 2661-8141

Partial differential equations formalise the understanding of the behaviour of the physical world that humans acquire through experience and observation. Through their numerical solution, such equations are used to model and predict the evolution of dynamical systems. However, such techniques require extensive computational resources and assume the physics are prescribed \textit{a priori}. Here, we propose a neural network capable of predicting the evolution of a specific physical phenomenon: propagation of surface waves enclosed in a tank, which, mathematically, can be described by the Saint-Venant equations. The existence of reflections and interference makes this problem non-trivial. Forecasting of future states (i.e. spatial patterns of rendered wave amplitude) is achieved from a relatively small set of initial observations. Using a network to make approximate but rapid predictions would enable the active, real-time control of physical systems, often required for engineering design. We used a deep neural network comprising of three main blocks: an encoder, a propagator with three parallel Long Short-Term Memory layers, and a decoder. Results on a novel, custom dataset of simulated sequences produced by a numerical solver show reasonable predictions for as long as 80 time steps into the future on a hold-out dataset. Furthermore, we show that the network is capable of generalising to two other initial conditions that are qualitatively different from those seen at training time.

Conference paper

Garasto S, Nicola W, Bharath A, Schultz Set al., Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex, 2019 9th International IEEE/EMBS Conference on Neural Engineering(NER), Publisher: IEEE

Interpreting the neural code involves decoding the firing pattern of sensory neurons from the perspective of a downstream population. Performing such a read-out is an essential step for the understanding of sensory information processing in the brain and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to categorize stimuli using a predefined set of labels, less attention has been given to full-stimulus reconstruction, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of 103 neurons in layer 2/3 of mouse primary visual cortex. Using an optimal linear estimator, we investigated which factors drive the reconstruction performance at the pixel level. We find the density of receptive fields to be the most influential feature. Finally, we use the receptive field data and simulations from a linear-nonlinear Poisson model to extrapolate decoding accuracy as a function of network size. Based on our analysis on a public dataset, reconstruction performance using two-photon protocols might be considerably improved if the receptive fields are sampled more uniformly in the full visual field. These results provide practical experimental guidelines to boost the accuracy of full-stimulus reconstruction.

Conference paper

Creswell A, Bharath A, 2018, Inverting the generator of a generative adversarial network, IEEE Transactions on Neural Networks and Learning Systems, ISSN: 2162-2388

Generative adversarial networks (GANs) learn a deep generative model that is able to synthesize novel, high-dimensional data samples. New data samples are synthesized by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting properties that may be useful for downstream tasks such as classification or retrieval. Unfortunately, GANs do not offer an inverse model,'' a mapping from data space back to latent space, making it difficult to infer a latent representation for a given data sample. In this paper, we introduce a technique, inversion, to project data samples, specifically images, to the latent space using a pretrained GAN. Using our proposed inversion technique, we are able to identify which attributes of a data set a trained GAN is able to model and quantify GAN performance, based on a reconstruction loss. We demonstrate how our proposed inversion technique may be used to quantitatively compare the performance of various GAN models trained on three image data sets. We provide codes for all of our experiments in the website (https://github.com/ToniCreswell/InvertingGAN).

Journal article

Creswell A, Pouplin A, Bharath AA, 2018, Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data., IET Computer Vision, Vol: abs/1801.00693, ISSN: 1751-9640

The authors propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but the amount of labelled data is limited. They consider the specific case of classifying skin lesions as either benign or malignant. In this setting, the authors’ proposed approach – the semi-supervised, denoising adversarial autoencoder – is able to utilise vast amounts of unlabelled data to learn a representation for skin lesions, and small amounts of labelled data to assign class labels based on the learned representation. They perform an ablation study to analyse the contributions of both the adversarial and denoising components and compare their work with state-of-the-art results. They find that their model yields superior classification performance, especially when evaluating their model at high sensitivity values.

Journal article

Creswell A, While T, Dumoulin V, Arulkumaran K, Sengupta B, Bharath AAet al., 2018, Generative adversarial networks: an overview, IEEE Signal Processing Magazine, Vol: 35, Pages: 53-65, ISSN: 1053-5888

Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image synthesis, semantic image editing, style transfer, image superresolution, and classification. The aim of this review article is to provide an overview of GANs for the signal processing community, drawing on familiar analogies and concepts where possible. In addition to identifying different methods for training and constructing GANs, we also point to remaining challenges in their theory and application.

Journal article

Uslu F, Bharath AA, 2018, A Multi-task Network to Detect Junctions in Retinal Vasculature, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG

Working paper

Arulkumaran K, Deisenroth MP, Brundage M, Bharath AAet al., 2017, A brief survey of deep reinforcement learning, IEEE Signal Processing Magazine, Vol: 34, Pages: 26-38, ISSN: 1053-5888

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higher-level understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.

Journal article

Arulkumaran K, Deisenroth MP, Brundage M, Bharath AAet al., 2017, A brief survey of deep reinforcement learning, IEEE Signal Processing Magazine, Vol: 34, Pages: 26-38, ISSN: 1053-5888

Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.

Journal article

Bass C, Helkkula P, De Paola V, Clopath C, Bharath AAet al., 2017, Detection of axonal synapses in 3D two-photon images., PLoS ONE, Vol: 12, ISSN: 1932-6203

Studies of structural plasticity in the brain often require the detection and analysis of axonal synapses (boutons). To date, bouton detection has been largely manual or semi-automated, relying on a step that traces the axons before detection the boutons. If tracing the axon fails, the accuracy of bouton detection is compromised. In this paper, we propose a new algorithm that does not require tracing the axon to detect axonal boutons in 3D two-photon images taken from the mouse cortex. To find the most appropriate techniques for this task, we compared several well-known algorithms for interest point detection and feature descriptor generation. The final algorithm proposed has the following main steps: (1) a Laplacian of Gaussian (LoG) based feature enhancement module to accentuate the appearance of boutons; (2) a Speeded Up Robust Features (SURF) interest point detector to find candidate locations for feature extraction; (3) non-maximum suppression to eliminate candidates that were detected more than once in the same local region; (4) generation of feature descriptors based on Gabor filters; (5) a Support Vector Machine (SVM) classifier, trained on features from labelled data, and was used to distinguish between bouton and non-bouton candidates. We found that our method achieved a Recall of 95%, Precision of 76%, and F1 score of 84% within a new dataset that we make available for accessing bouton detection. On average, Recall and F1 score were significantly better than the current state-of-the-art method, while Precision was not significantly different. In conclusion, in this article we demonstrate that our approach, which is independent of axon tracing, can detect boutons to a high level of accuracy, and improves on the detection performance of existing approaches. The data and code (with an easy to use GUI) used in this article are available from open source repositories.

Journal article

Creswell A, Bharath AA, 2016, Inverting The Generator Of A Generative Adversarial Network

Generative adversarial networks (GANs) learn to synthesise new samples from ahigh-dimensional distribution by passing samples drawn from a latent spacethrough a generative network. When the high-dimensional distribution describesimages of a particular data set, the network should learn to generate visuallysimilar image samples for latent variables that are close to each other in thelatent space. For tasks such as image retrieval and image classification, itmay be useful to exploit the arrangement of the latent space by projectingimages into it, and using this as a representation for discriminative tasks.GANs often consist of multiple layers of non-linear computations, making themvery difficult to invert. This paper introduces techniques for projecting imagesamples into the latent space using any pre-trained GAN, provided that thecomputational graph is available. We evaluate these techniques on both MNISTdigits and Omniglot handwritten characters. In the case of MNIST digits, weshow that projections into the latent space maintain information about thestyle and the identity of the digit. In the case of Omniglot characters, weshow that even characters from alphabets that have not been seen duringtraining may be projected well into the latent space; this suggests that thisapproach may have applications in one-shot learning.

Working paper

Charalambous CC, Bharath AA, 2016, A data augmentation methodology for training machine/deep learning gait recognition algorithms

There are several confounding factors that can reduce the accuracy of gaitrecognition systems. These factors can reduce the distinctiveness, or alter thefeatures used to characterise gait, they include variations in clothing,lighting, pose and environment, such as the walking surface. Full invariance toall confounding factors is challenging in the absence of high-quality labelledtraining data. We introduce a simulation-based methodology and asubject-specific dataset which can be used for generating synthetic videoframes and sequences for data augmentation. With this methodology, we generateda multi-modal dataset. In addition, we supply simulation files that provide theability to simultaneously sample from several confounding variables. The basisof the data is real motion capture data of subjects walking and running on atreadmill at different speeds. Results from gait recognition experimentssuggest that information about the identity of subjects is retained withinsynthetically generated examples. The dataset and methodology allow studiesinto fully-invariant identity recognition spanning a far greater number ofobservation conditions than would otherwise be possible.

Working paper

Creswell A, Bharath AA, 2016, Adversarial training for sketch retrieval, Computer Vision – ECCV 2016 Workshops, Publisher: Springer Verlag, ISSN: 0302-9743

Generative Adversarial Networks (GAN) are able to learn excellentrepresentations for unlabelled data which can be applied to image generationand scene classification. Representations learned by GANs have not yet beenapplied to retrieval. In this paper, we show that the representations learnedby GANs can indeed be used for retrieval. We consider heritage documents thatcontain unlabelled Merchant Marks, sketch-like symbols that are similar tohieroglyphs. We introduce a novel GAN architecture with design features thatmake it suitable for sketch retrieval. The performance of this sketch-GAN iscompared to a modified version of the original GAN architecture with respect tosimple invariance properties. Experiments suggest that sketch-GANs learnrepresentations that are suitable for retrieval and which also have increasedstability to rotation, scale and translation compared to the standard GANarchitecture.

Conference paper

Charalambous C, Bharath AA, 2016, A data augmentation methodology for training machine/deep learning gait recognition algorithms, British Machine Vision Conference, Publisher: BMVA Press, Pages: 110.1-110.12

There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait; they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.

Conference paper

Arulkumaran K, Dilokthanakul N, Shanahan M, Bharath AAet al., 2016, Classifying options for deep reinforcement learning, Publisher: IJCAI

Deep reinforcement learning is the learning of multiple levels ofhierarchical representations for reinforcement learning. Hierarchicalreinforcement learning focuses on temporal abstractions in planning andlearning, allowing temporally-extended actions to be transferred between tasks.In this paper we combine one method for hierarchical reinforcement learning -the options framework - with deep Q-networks (DQNs) through the use ofdifferent "option heads" on the policy network, and a supervisory network forchoosing between the different options. We show that in a domain where we haveprior knowledge of the mapping between states and options, our augmented DQNachieves a policy competitive with that of a standard DQN, but with much lowersample complexity. This is achieved through a straightforward architecturaladjustment to the DQN, as well as an additional supervised neural network.

Working paper

Othman BA, Greenwood C, Abuelela AF, Bharath AA, Chen S, Theodorou I, Douglas T, Uchida M, Ryan M, Merzaban JS, Porter AEet al., 2016, Targeted Cancer Therapy: Correlative Light-Electron Microscopy Shows RGD-Targeted ZnO Nanoparticles Dissolve in the Intracellular Environment of Triple Negative Breast Cancer Cells and Cause Apoptosis with Intratumor Heterogeneity (Adv. Healthcare Mater. 11/2016)., Advanced Healthcare Materials, Vol: 5, Pages: 1248-1248, ISSN: 2192-2640

On page 1310 J. S. Merzaban, A. E. Porter, and co-workers present fluorescently labeled RGD-targeted ZnO nanoparticles (NPs; green) for the targeted delivery of cytotoxic ZnO to integrin αvβ3 receptors expressed on triple negative breast cancer cells. Correlative light-electron microscopy shows that NPs dissolve into ionic Zn(2+) (blue) upon uptake and cause apoptosis (red) with intra-tumor heterogeneity, thereby providing a possible strategy for targeted breast cancer therapy. Cover design by Ivan Gromicho.

Journal article

Rivera-Rubio J, Arulkumaran K, Rishi H, Alexiou I, Bharath AAet al., 2016, An assistive haptic interface for appearance-based indoor navigation, Computer Vision and Image Understanding, Vol: 149, Pages: 126-145, ISSN: 1090-235X

Computer vision remains an under-exploited technology for assistive devices. Here, we propose a navigation technique using low-resolution images from wearable or hand-held cameras to identify landmarks that are indicative of a user’s position along crowdsourced paths. We test the components of a system that is able to provide blindfolded users with information about location via tactile feedback. We assess the accuracy of vision-based localisation by making comparisons with estimates of location derived from both a recent SLAM-based algorithm and from indoor surveying equipment. We evaluate the precision and reliability by which location information can be conveyed to human subjects by analysing their ability to infer position from electrostatic feedback in the form of textural (haptic) cues on a tablet device. Finally, we describe a relatively lightweight systems architecture that enables images to be captured and location results to be served back to the haptic device based on journey information from multiple users and devices.

Journal article

Othman BA, Greenwood C, Abuelela AF, Bharath AA, Chen S, Theodorou I, Douglas T, Uchida M, Ryan M, Merzaban JS, Porter AEet al., 2016, Correlative light-electron microscopy shows RGD-targeted ZnO nanoparticles dissolve in the intracellular environment of triple negative breast cancer cells and cause apoptosis with intra-tumor heterogeneity, Advanced Healthcare Materials, Vol: 5, Pages: 1310-1325, ISSN: 2192-2640

ZnO nanoparticles (NPs) are reported to show a high degree of cancer cell selectivity with potential use in cancer imaging and therapy. Questions remain about the mode by which the ZnO NPs cause cell death, whether they exert an intra- or extra-35 cellular effect, and the resistance among different cancer cell types to ZnO NP exposure. The present study quantified the variability between the cellular toxicity, dynamics of cellular uptake and dissolution of bare and RGD (Arg-Gly-Asp)-targeted ZnO NPs by MDA-MB-231 cells. Compared to bare ZnO NPs, RGD-targeting of the ZnO NPs to integrin αvβ3 receptors expressed on MDA-MB-231 cells appeared to increase the toxicity of the ZnO NPs to breast cancer cells at lower doses. Confocal microscopy of live MDA-MB-231 cells confirmed uptake of both classes of ZnO NPs with a commensurate rise in intracellular Zn2+ concentration prior to cell death. The response of the cells within the population to intracellular Zn2+ was highly heterogeneous. In addition, the results emphasize the utility of dynamic and quantitative imaging in understanding cell uptake and processing of targeted therapeutic ZnO NPs at the cellular level by heterogeneous cancer cell populations, which could be crucial for the development of optimized treatment strategies.

Journal article

Ma ZB, Yang Y, Liu YX, Bharath AAet al., 2016, Recurrently decomposable 2-D convolvers for FPGA-based digital image processing, IEEE Transactions on Circuits and Systems, Vol: 63, Pages: 979-983, ISSN: 1549-7747

Two-dimensional (2-D) convolution is a widely used operation in image processing and computer vision, characterized by intensive computation and frequent memory accesses. Previous efforts to improve the performance of field-programmable gate array (FPGA) convolvers focused on the design of buffering schemes and on minimizing the use of multipliers. A recently proposed recurrently decomposable (RD) filter design method can reduce the computational complexity of 2-D convolutions by splitting the convolution between an image and a large mask into a sequence of convolutions using several smaller masks. This brief explores how to efficiently implement RD based 2-D convolvers using FPGA. Three FPGA architectures are proposed based on RD filters, each with a different buffering scheme. The conclusion is that RD based architectures achieve higher area efficiency than other previously reported state-of-the-art methods, especially for larger convolution masks. An area efficiency metric is also suggested, which allows the most appropriate architecture to be selected.

Journal article

Rivera-Rubio J, Alexiou I, Bharath AA, 2015, Appearance-based indoor localization: a comparison of patch descriptor performance, Pattern Recognition Letters, Vol: 66, Pages: 109-117, ISSN: 1872-7344

Vision is one of the most important of the senses, and humans use it extensively during navigation. We evaluated different types of image and video frame descriptors that could be used to determine distinctive visual landmarks for localizing a person based on what is seen by a camera that they carry. To do this, we created a database containing over 3 km of video-sequences with ground-truth in the form of distance travelled along different corridors. Using this database, the accuracy of localization—both in terms of knowing which route a user is on—and in terms of position along a certain route, can be evaluated. For each type of descriptor, we also tested different techniques to encode visual structure and to search between journeys to estimate a user’s position. The techniques include single-frame descriptors, those using sequences of frames, and both color and achromatic descriptors. We found that single-frame indexing worked better within this particular dataset. This might be because the motion of the person holding the camera makes the video too dependent on individual steps and motions of one particular journey. Our results suggest that appearance-based information could be an additional source of navigational data indoors, augmenting that provided by, say, radio signal strength indicators (RSSIs). Such visual information could be collected by crowdsourcing low-resolution video feeds, allowing journeys made by different users to be associated with each other, and location to be inferred without requiring explicit mapping. This offers a complementary approach to methods based on simultaneous localization and mapping (SLAM) algorithms.

Journal article

Liu Y, Yang Y, Bharath A, 2015, Recurrently decomposable 2-D filters, Journal of Computational Information Systems, Vol: 11, Pages: 1773-1779, ISSN: 1553-9105

Copyright © 2015 Binary Information Press. The study of spatial convolution is returning to relevance due to recent rapid developments in deep learning theory and the corresponding growth in the use of convolution neural networks. However, the finite-impulse response filters that are widely used for spatial convolution are usually both numerous and non-separable, making the associatd computational burden higher. In this letter, we exploit the property that large 2-D filters can be computed either as the combinations of 1-D filters (conventional Cartesianseparable case) or the combinations of smaller 2-D ones, which we call recurrently-decomposable filters. The proposed new nested structure greatly reduces the computational complexity at no cost in terms of performance. We describe the 2-D filter decomposition in terms of several unconstrained optimization problems and give solutions to these problems. Finally, we conclude the paper with the application to a 2-D fan filter to demonstrate the validity.

Journal article

Rivera-Rubio J, Alexiou I, Bharath AA, 2015, Associating Locations Between Indoor Journeys from Wearable Cameras, 13th European Conference on Computer Vision (ECCV), Publisher: SPRINGER-VERLAG BERLIN, Pages: 29-44, ISSN: 0302-9743

Conference paper

Rivera-Rubio J, Alexiou I, Bharath AA, 2015, Indoor Localisation with Regression Networks and Place Cell Models., Publisher: BMVA Press, Pages: 147.1-147.1

Conference paper

Rivera-Rubio J, Idrees S, Alexiou I, Hadjilucas L, Bharath AAet al., 2014, Small Hand-held Object Recognition Test (SHORT), Pages: 524-531

Conference paper

Kis Z, Towhidi L, Ip H, Drakakis E, Bharath A, Krams Ret al., 2014, An in-situ electroporation and flow device for mechanotransduction studies, Microarrays: Principles, Applications and Technologies, Editors: James V Rogers, Pages: 49-68, ISBN: 978-1-62948-669-7

Book chapter

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