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All Outputs (127)

A Collaborative Artefact Reconstruction Environment (2017)
Conference Proceeding
Woolley, S. I., Ch’ng, E., Hernandez-Munoz, L., Gehlken, E., Collins, T., Nash, D., …Hanes, L. (2017). A Collaborative Artefact Reconstruction Environment. . https://doi.org/10.14236/ewic/HCI2017.53

A novel collaborative artefact reconstruction environment design is presented that is informed by experimental task observation and participatory design. The motivation for the design was to enable collaborative human and computer effort in the recon... Read More about A Collaborative Artefact Reconstruction Environment.

Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning (2017)
Conference Proceeding
Zheng, Y., Williams, B. M., Pratt, H., Al-Bander, B., Wu, X., & Zhao, Y. (2017). Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning.

Purpose : Three-dimensional (3D) optical coherence tomography (OCT) images are increasingly used in the management of eye disease, yet there has been no corresponding increase in the availability of software tools to support the analysis of large amo... Read More about Computer aided diagnosis of age-related macular degeneration in 3D OCT images by deep learning.

Automatic Feature Learning Method for Detection of Retinal Landmarks (2017)
Conference Proceeding
Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., Al-Ataby, A., & Zheng, Y. (2017). Automatic Feature Learning Method for Detection of Retinal Landmarks. . https://doi.org/10.1109/DeSE.2016.4

This paper presents an automatic deep learning method for location detection of important retinal landmarks, the fovea and optic disc (OD) in digital fundus retinal images with the potential for use in an automated screening and grading system. The p... Read More about Automatic Feature Learning Method for Detection of Retinal Landmarks.

Visual Augmentation Glasses for People with Impaired Vision (2017)
Conference Proceeding
Al-Ataby, A., Younis, O., Al-Nuaimy, W., Al-Taee, M., Sharaf, Z., & Al-Bander, B. (2017). Visual Augmentation Glasses for People with Impaired Vision. . https://doi.org/10.1109/DeSE.2016.6

This paper presents the preliminary design and development of a set visual augmentation glasses with the potential to assist people with varying degrees of loss of vision. The wearable spectacles are intended to be non-obstructive, and therefore empl... Read More about Visual Augmentation Glasses for People with Impaired Vision.

Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance (2017)
Conference Proceeding
Ragab Sayed, M., Yuting Lim, R., Mandal, B., Li, L., Hwee Lim, J., & Sim, T. (2017). Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance. In No. 7: Science of Intelligence: Computational Principles of Natural and Artificial Intelligence

Researchers have conducted many studies on human attentions and their eye gaze patterns for face recognition (FR), hoping to inspire new ideas to develop computer vision (CV) algorithms which perform like or even better than human. Yet, while these s... Read More about Analysis of Human Attentions for Face Recognition on Natural Videos and Comparison with CV Algorithm on Performance.

Structural Knowledge Extraction from Mobility Data (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., Ortolani, M., & Pergola, G. (2016). Structural Knowledge Extraction from Mobility Data. In G. Adorni, S. Cagnoni, M. Gori, & M. Maratea (Eds.), AI*IA 2016 Advances in Artificial Intelligence -. https://doi.org/10.1007/978-3-319-49130-1_22

Knowledge extraction has traditionally represented one of the most interesting challenges in AI; in recent years, however, the availability of large collections of data has increased the awareness that “measuring” does not seamlessly translate into “... Read More about Structural Knowledge Extraction from Mobility Data.

Spontaneous Versus Posed Smiles—Can We Tell the Difference? (2016)
Conference Proceeding
Mandal, B., & Ouarti, N. (2017). Spontaneous Versus Posed Smiles—Can We Tell the Difference?. . https://doi.org/10.1007/978-981-10-2107-7_24

Smile is an irrefutable expression that shows the physical state of the mind in both true and deceptive ways. Generally, it shows happy state of the mind, however, ‘smiles’ can be deceptive, for example people can give a smile when they feel happy an... Read More about Spontaneous Versus Posed Smiles—Can We Tell the Difference?.

Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images (2016)
Conference Proceeding
Jabbar, S. I., Day, C. R., Heinz, N., & Chadwick, E. K. (2016). Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images. In 2016 International Joint Conference on Neural Networks (IJCNN)

Fast and accurate segmentation of musculoskeletal ultrasound images is an on-going challenge. Two principal factors make this task difficult: firstly, the presence of speckle noise arising from the interference that accompanies all coherent imaging a... Read More about Using Convolutional Neural Network for Edge Detection in Musculoskeletal Ultrasound Images.

Diabetic macular edema grading based on deep neural networks (2016)
Conference Proceeding
Al-Bander, B., Al-Nuaimy, W., Al-Taee, M. A., Williams, B. M., & Zheng, Y. (2016). Diabetic macular edema grading based on deep neural networks. In Proceedings of the Ophthalmic Medical Image Analysis International Workshop 3 (121–128). https://doi.org/10.17077/omia.1055

Diabetic Macular Edema (DME) is a major cause of vision loss in diabetes. Its early detection and treatment is therefore a vital task in management of diabetic retinopathy. In this paper, we propose a new featurelearning approach for grading the seve... Read More about Diabetic macular edema grading based on deep neural networks.

Detecting Similarities in Mobility Patterns (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Detecting Similarities in Mobility Patterns. In Frontiers in Artificial Intelligence and Applications (167 - 178). https://doi.org/10.3233/978-1-61499-682-8-167

The wide spread of low-cost personal devices equipped with GPS sensors has paved the way towards the creation of customized services based on user mobility habits and able to track and assist users in everyday activities, according to their current l... Read More about Detecting Similarities in Mobility Patterns.

Gaining insight by structural knowledge extraction (2016)
Conference Proceeding
Cottone, P., Gaglio, S., Lo Re, G., & Ortolani, M. (2016). Gaining insight by structural knowledge extraction.

The availability of increasingly larger and more complex datasets has boosted the demand for systems able to analyze them automatically. The design and implementation of effective systems requires coding knowledge about the application domain inside... Read More about Gaining insight by structural knowledge extraction.

Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition (2016)
Conference Proceeding
Song, S., Chandrasekhar, V., Mandal, B., Li, L., Lim, J., Babu, G. S., …Cheung, N. (2016). Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition. . https://doi.org/10.1109/cvprw.2016.54

In this paper, we propose a multimodal multi-stream deep learning framework to tackle the egocentric activity recognition problem, using both the video and sensor data. First, we experiment and extend a multi-stream Convolutional Neural Network to le... Read More about Multimodal Multi-Stream Deep Learning for Egocentric Activity Recognition.

Gl-learning: an optimized framework for grammatical inference (2016)
Conference Proceeding
Cottone, P., Ortolani, M., & Pergola, G. (2016). Gl-learning: an optimized framework for grammatical inference. In CompSysTech '16: Computer Systems and Technologies 2016. https://doi.org/10.1145/2983468.2983502

In this paper, we present a new open-source software library, Gl-learning, for grammatical inference. The rise of new application scenarios in recent years has required optimized methods to address knowledge extraction from huge amounts of data and t... Read More about Gl-learning: an optimized framework for grammatical inference.

#hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK (2016)
Conference Proceeding
de Quincey, E., Kyriacou, T., & Pantin, T. (2016). #hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK. . https://doi.org/10.1145/2896338.2896342

This paper describes a longitudinal study that has collected and analysed over 512,000 UK geolocated tweets over 2 years from June 2012 that contained instances of the words "hayfever" and "hay fever". The results indicate that the temporal distribut... Read More about #hayfever; A Longitudinal Study into Hay Fever Related Tweets in the UK.

Privacy Preserving Attribute Based Encryption for Multiple Cloud Collaborative Environment (2015)
Conference Proceeding
Komninos, N., & Junejo, A. K. (2015). Privacy Preserving Attribute Based Encryption for Multiple Cloud Collaborative Environment. . https://doi.org/10.1109/UCC.2015.104

In a Multiple Cloud Collaborative Environment (MCCE), cloud users and cloud providers interact with each other via a brokering service to request and provision cloud services. The brokering service considers several pieces of data to broker the best... Read More about Privacy Preserving Attribute Based Encryption for Multiple Cloud Collaborative Environment.