Dr Shivakumara Palaiahnakote's Outputs (9)

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.

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.

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.