S. Srinivasan, K. Rajakumar^*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2481-2496, 2023, DOI:10.32604/csse.2023.040817 - 28 July 2023

Abstract

The high dimensionalhyperspectral image classification is a challenging task due to the spectral feature vectors. The high correlation between these features and the noises greatly affects the classification performances. To overcome this, dimensionality reduction techniques are widely used. Traditional image processing applications recently propose numerous deep learning models. However, in hyperspectral image classification, the features of deep learning models are less explored. Thus, for efficient hyperspectral image classification, a depth-wise convolutional neural network is presented in this research work. To handle the dimensionality issue in the classification process, an optimized self-organized map model is employed

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Yazeed Yasin Ghadi¹, Israr Akhter², Suliman A. Alsuhibany³, Tamara al Shloul⁴, Ahmad Jalal², Kibum Kim^5,*

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1455-1471, 2022, DOI:10.32604/iasc.2022.025013 - 25 May 2022

Abstract Event detection systems are mainly used to observe and monitor human behavior via red green blue (RGB) images and videos. Event detection using RGB images is one of the challenging tasks of the current era. Human detection, position and orientation of human body parts in RGB images is a critical phase for numerous systems models. In this research article, the detection of human body parts by extracting context-aware energy features for event recognition is described. For this, silhouette extraction, estimation of human body parts, and context-aware features are extracted. To optimize the context-intelligence vector, we… More >