Outputs (1084)

Varietal classification of rice seeds using RGB and hyperspectral images. (2020)
Journal Article
FABIYI, S.D., VU, H., TACHTATZIS, C., MURRAY, P., HARLE, D., DAO, T.K., ANDONOVIC, I., REN, J. and MARSHALL, S. 2020. Varietal classification of rice seeds using RGB and hyperspectral images. IEEE access [online], 8, pages 22493-22505. Available from: https://doi.org/10.1109/ACCESS.2020.2969847

Inspection of rice seeds is a crucial task for plant nurseries and farmers since it ensures seed quality when growing seedlings. Conventionally, this process is performed by expert inspectors who manually screen large samples of rice seeds to identif... Read More about Varietal classification of rice seeds using RGB and hyperspectral images..

MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. (2020)
Journal Article
CHEN, W., YANG, Z., REN, J., CAO, J., CAI, N., ZHAO, H. and YUEN, P. 2020. MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection. Pattern recognition [online], 102, article 107213. Available from: https://doi.org/10.1016/j.patcog.2020.107213

Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system. Without the category labels, it is chall... Read More about MIMN-DPP: maximum-information and minimum-noise determinantal point processes for unsupervised hyperspectral band selection..

Human activity recognition with deep metric learners. (2020)
Conference Proceeding
MARTIN, K., WIJEKOON, A. and WIRATUNGA, N. 2019. Human activity recognition with deep metric learners. In Kapetanakis, S. and Borck, H. (eds.) Proceedings of the 27th International conference on case-based reasoning workshop (ICCBR-WS19), co-located with the 27th International conference on case-based reasoning (ICCBR19), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings, 2567. Aachen: CEUR-WS [online], pages 8-17. Available from: http://ceur-ws.org/Vol-2567/paper1.pdf

Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-... Read More about Human activity recognition with deep metric learners..

Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas. (2020)
Journal Article
HU, S., QIN, J., REN, J., ZHAO, H., REN, J., and HONG, H. 2020. Automatic extraction of water inundation areas using sentinel-1 data for large plain areas. Remote sensing [online], 12(2), article 243. Available from: https://doi.org/10.3390/rs12020243

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with th... Read More about Automatic extraction of water inundation areas using Sentinel-1 data for large plain areas..

Learning from small and imbalanced dataset of images using generative adversarial neural networks. (2019)
Thesis
ALI-GOMBE, A. 2019. Learning from small and imbalanced dataset of images using generative adversarial neural networks. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a ma... Read More about Learning from small and imbalanced dataset of images using generative adversarial neural networks..

Food survey using exploratory data analysis. (2019)
Conference Proceeding
RAMYASRI, R., ISHASANJIDA, S., PARASA, D. and BANO, S. 2019. Food survey using exploratory data analysis. In Proceedings of the 2nd International conference on intelligent communication and computational techniques (ICCT 2019), 28-29 September 2019, Jaipur, India. Piscataway: IEEE [online], pages 258-264. Available from: https://doi.org/10.1109/ICCT46177.2019.8969016

A person's eating habits are the most important aspect of maintaining one's physical wellbeing, which in turn is key to enduring the stresses and emotional hurdles that are so commonplace in our modern lifestyles. Our research shows that, over the pa... Read More about Food survey using exploratory data analysis..

Social media survey using decision tree and naive Bayes classification. (2019)
Conference Proceeding
ROSHINI, T., SIREESHA, P.V., PARASA, D. and BANO, S. 2019. Social media survey using decision tree and naive Bayes classification. In Proceedings of the 2nd International conference on intelligent communication and computational techniques (ICCT 2019), 28-29 September 2019, Jaipur, India. Piscataway: IEEE [online], pages 265-270. Available from: https://doi.org/10.1109/ICCT46177.2019.8969058

Social media - a website or an application that is used to create and share content among a social network - is one of the most important aspects of our day-to-day life. Recent studies claim that an average person spends roughly 142 minutes per day o... Read More about Social media survey using decision tree and naive Bayes classification..

Classification of binary fracture using CNN. (2019)
Conference Proceeding
CHITTAJALLU, S.M., MANDALANENI, N.L.D., PARASA, D. and BANO, S. 2019. Classification of binary fracture using CNN. In Proceedings of the 1st Global conference for advancement in technology (GCAT 2019), 18-20 October 2019, Bangalore, India. Piscataway: IEEE [online]. Available from: https://doi.org/10.1109/GCAT47503.2019.8978468

One of the major problems faced by any living organism since infancy are musculoskeletal injuries. To keep it quite simple musculoskeletal injuries are a range of disorders involving muscles, bones, tendons, blood vessels, nerves and other soft tissu... Read More about Classification of binary fracture using CNN..

Food places classification in egocentric images using Siamese neural networks. (2019)
Conference Proceeding
SARKER, M.M.K., BANU, S.F., RASHWAN, H.A., ABDEL-NASSER, M., SINGH, V.K., CHAMBON, S., RADEVA, P. and PUIG, D. 2019. Food places classification in egocentric images using Siamese neural networks. In Sabater-Mir, J., Torra, V., Aguiló, I. and González-Hidalgo, M. (eds.) Artificial intelligence research and development: proceedings of the 22nd International conference of the Catalan Association for Artificial Intelligence (CCIA 2019), 23-25 October 2019, Colònia de Sant Jordi, Spain. Frontiers in artificial intelligence and applications, 319. Amsterdam: IOS Press [online], pages 145-151. Available from: https://doi.org/10.3233/FAIA190117

Wearable cameras have become more popular in recent years for capturing unscripted moments in the first-person, which help in analysis of the user's lifestyle. In this work, we aim to identify the daily food patterns of a person through recognition o... Read More about Food places classification in egocentric images using Siamese neural networks..

Deep heterogeneous ensemble. (2019)
Journal Article
NGUYEN, T.T., DANG, M.T., PHAM, T.D., DAO, L.P., LUONG, A.V., MCCALL, J. and LIEW, A.W.C. 2019. Deep heterogeneous ensemble. Australian journal of intelligent information processing systems [online], 16(1): special issue on neural information processing: proceedings of the 26th International conference on neural information processing (ICONIP 2019), 12-15 December 2019, Sydney, Australia, pages 1-9. Available from: http://ajiips.com.au/papers/V16.1/v16n1_5-13.pdf

In recent years, deep neural networks (DNNs) have emerged as a powerful technique in many areas of machine learning. Although DNNs have achieved great breakthrough in processing images, video, audio and text, it also has some limitations... Read More about Deep heterogeneous ensemble..