All Outputs (79)

A New Method for Detecting Altered Text in Document Images (2020)
Presentation / Conference Contribution

As more and more office documents are captured, stored, and shared in digital format, and as image editing software becomes increasingly more powerful, there is a growing concern about document authenticity. For example, texts in property documents c... Read More about A New Method for Detecting Altered Text in Document Images.

A new unified method for detecting text from marathon runners and sports players in video (PR-D-19-01078R2) (2020)
Journal Article
Nag, S., Shivakumara, P., Pal, U., Lu, T., & Blumenstein, M. (2020). A new unified method for detecting text from marathon runners and sports players in video (PR-D-19-01078R2). Pattern recognition, 107, https://doi.org/10.1016/j.patcog.2020.107476

Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper... Read More about A new unified method for detecting text from marathon runners and sports players in video (PR-D-19-01078R2).

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.

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.

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.

Residual-based approach for authenticating pattern of multi-style diacritical Arabic texts (2018)
Journal Article
Hakak, S., Kamsin, A., Palaiahnakote, S., Tayan, O., Mohd. Yamani Idna Idris, & Zuhaili Abukhir, K. (2018). Residual-based approach for authenticating pattern of multi-style diacritical Arabic texts. PloS one, https://doi.org/10.1371/journal.pone.0198284

Arabic script is highly sensitive to changes in meaning with respect to the accurate arrangement of diacritics and other related symbols. The most sensitive Arabic text available online is the Digital Qur’an, the sacred book of Revelation in Islam th... Read More about Residual-based approach for authenticating pattern of multi-style diacritical Arabic texts.

Cloud of line distribution for arbitrary text detection in scene/video/license plate images (2018)
Presentation / Conference Contribution

Detecting arbitrary oriented text in scene and license plate images is challenging due to multiple adverse factors caused by images of diversified applications. This paper proposes a novel idea of extracting Cloud of Line Distribution (COLD) for the... Read More about Cloud of line distribution for arbitrary text detection in scene/video/license plate images.

Rough-fuzzy based scene categorization for text detection and recognition in video (2018)
Journal Article
Roy, S., Shivakumara, P., Jain, N., Khare, V., Dutta, A., Pal, U., & Lu, T. (2018). Rough-fuzzy based scene categorization for text detection and recognition in video. Pattern recognition, 80, 64-82. https://doi.org/10.1016/j.patcog.2018.02.014

Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different clas... Read More about Rough-fuzzy based scene categorization for text detection and recognition in video.

A Robust Symmetry-Based Method for Scene/Video Text Detection through Neural Network (2018)
Presentation / Conference Contribution

Text detection in video/scene images has gained a significant attention in the field of image processing and document analysis due to the inherent challenges caused by variations in contrast, orientation, background, text type, font type, non-uniform... Read More about A Robust Symmetry-Based Method for Scene/Video Text Detection through Neural Network.

Modeling spatial layout for scene image understanding via a novel multiscale sum-product network (2016)
Journal Article
Yuan, Z., Wang, H., Wang, L., Lu, T., Palaiahnakote, S., & Lim Tan, C. (2016). Modeling spatial layout for scene image understanding via a novel multiscale sum-product network. Expert systems with applications, https://doi.org/10.1016/j.eswa.2016.07.015

Semantic image segmentation is challenging due to the large intra-class variations and the complex spatial layouts inside natural scenes. This paper investigates this problem by designing a new deep architecture, called multiscale sum-product network... Read More about Modeling spatial layout for scene image understanding via a novel multiscale sum-product network.

Text segmentation in degraded historical document images (2016)
Journal Article
Kavitha, A., Shivakumara, P., Kumar, G., & Lu, T. (2016). Text segmentation in degraded historical document images. Egyptian Informatics Journal, 17(2), 189-197. https://doi.org/10.1016/j.eij.2015.11.003

Text segmentation from degraded Historical Indus script images helps Optical Character Recognizer (OCR) to achieve good recognition rates for Hindus scripts; however, it is challenging due to complex background in such images. In this paper, we prese... Read More about Text segmentation in degraded historical document images.

Multi-oriented text detection for intra-frame in H.264/AVC video (2015)
Presentation / Conference Contribution

Text detection in compressed video has received much attention in recent years due to the effectiveness of DCT coefficients and motion vectors in realizing several applications. In this paper, a new text detection, which utilizes AC coefficients in t... Read More about Multi-oriented text detection for intra-frame in H.264/AVC video.