Outputs (87)

Saliency-based bit plane detection for network applications (2020)
Journal Article
Asadzadeh Kaljahi, M., Shivakumara, P., Hakak, S., Yamani Idna Idris, M., Hossein Anisi, M., & Rajan, D. (2020). Saliency-based bit plane detection for network applications. Multimedia Tools and Applications, 79, 18495–18513. https://doi.org/10.1007/s11042-020-08741-9

Transmitting image data without losing significant information is challenging for any network application especially when large color images are transmitted through TCP communication protocol. This is due to network limitations such as buffer overflo... Read More about Saliency-based bit plane detection for network applications.

A text-context-aware CNN network for multi-oriented and multi-language scene text detection (2020)
Presentation / Conference Contribution

The existing deep learning based state-of-theart scene text detection methods treat scene texts a type of general objects, or segment text regions directly. The latter category achieves remarkable detection results on arbitrary orientation and large... Read More about A text-context-aware CNN network for multi-oriented and multi-language scene text detection.

Compressive sensing based convolutional neural network for object detection (2020)
Journal Article
Wu, Y., Meng, Z., Palaiahnakote, S., & Lu, T. (2020). Compressive sensing based convolutional neural network for object detection. Malaysian journal of computer science, 33(1), 78-89. https://doi.org/10.22452/mjcs.vol33no1.5

Deep neural networks (DNN) have shown significant performance in several domains including computer vision and machine learning. Convolutional Neural Networks (CNN), known as a particular type of DNN, have shown their promising potentials in discover... Read More about Compressive sensing based convolutional neural network for object detection.

A scene image classification technique for a ubiquitous visual surveillance system (2019)
Journal Article
Asadzadeh Kaljahi, M., Palaiahnakote, S., Hossein Anisi, M., Yamani Idna Idris, M., Blumenstein, M., & Khurram Khan, M. (2019). A scene image classification technique for a ubiquitous visual surveillance system. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-018-6151-x

The concept of smart cities has quickly evolved to improve the quality of life and provide public safety. Smart cities mitigate harmful environmental impacts and offences and bring energy-efficiency, cost saving and mechanisms for better use of resou... Read More about A scene image classification technique for a ubiquitous visual surveillance system.

A Novel Character Segmentation-Reconstruction Approach for License Plate Recognition (2019)
Journal Article
Khare, V., Shivakumara, P., Seng Chan, C., Lu, T., Kim Meng, L., Hock Woon, H., & Blumenstein, M. (2019). A Novel Character Segmentation-Reconstruction Approach for License Plate Recognition. Expert systems with applications, 131, 219-239. https://doi.org/10.1016/j.eswa.2019.04.030

Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characte... Read More about A Novel Character Segmentation-Reconstruction Approach for License Plate Recognition.

A new Local Fractional Entropy-Based model for kidney MRI image enhancement (2018)
Journal Article
Al-Shamasneh, A. R., Jalab, H. A., Palaiahnakote, S., Hanum Obaidellah, U., Ibrahim, R. W., & El-Melegy, M. T. (2018). A new Local Fractional Entropy-Based model for kidney MRI image enhancement. Entropy, https://doi.org/10.3390/e20050344

Kidney image enhancement is challenging due to the unpredictable quality of MRI images, as well as the nature of kidney diseases. The focus of this work is on kidney images enhancement by proposing a new Local Fractional Entropy (LFE)-based model. T... Read More about A new Local Fractional Entropy-Based model for kidney MRI image enhancement.

Compressing YOLO network by compressive sensing (2018)
Presentation / Conference Contribution

Object detection is one of the fundamental challenges in pattern recognition community. Recently, convolutional neural networks (CNN) are increasingly exploited in object detection, showing their promising potentials of generatively discovering patte... Read More about Compressing YOLO network by compressive sensing.

Context-Aware Attention LSTM Network for Flood Prediction (2018)
Presentation / Conference Contribution

To minimize the negative impacts brought by floods, researchers from pattern recognition community utilize artificial intelligence based methods to solve the problem of flood prediction. Inspired by the significant power of Long Short-Term Memory (LS... Read More about Context-Aware Attention LSTM Network for Flood Prediction.

Local and Global Bayesian Network based Model for Flood Prediction (2018)
Presentation / Conference Contribution

To minimize the negative impacts brought by floods, researchers from pattern recognition community pay special attention to the problem of flood prediction by involving technologies of machine learning. In this paper, we propose to construct hierarch... Read More about Local and Global Bayesian Network based Model for Flood Prediction.

Em-SLAM: A Fast and Robust Monocular SLAM Method for Embedded Systems (2018)
Presentation / Conference Contribution

Simultaneous Localization and Mapping (SLAM) is difficult to deploy in the embedded systems due to its high computation cost and stable input requirements. Building on excellent algorithms of recent years, we present Em-SLAM, a monocular SLAM method... Read More about Em-SLAM: A Fast and Robust Monocular SLAM Method for Embedded Systems.