Paheding Sidike

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Extreme Learning Machine (ELM) has been introduced as a new algorithm for training single hidden layer feed-forward neural networks (SLFNs) instead of the classical gradient-based algorithms. Based on the consistency property of data, which enforce similar samples to share similar properties, ELM is a biologically inspired learning algorithm with SLFNs that(More)
The goal of super-resolution (SR) is to increase the spatial resolution of a low-resolution (LR) image by a certain factor using either single or multiple LR input images. This paper presents a machine learning-based approach to reconstruct a high-resolution (HR) image from a single LR image. Inspired by the human visual cortex system, which is sensitive to(More)
In hyperspectral imaging, pixels of interest generally incorporate information from disparate components which requires quantitative decomposition of these pixels to extract desired information. Since hyperspectral sensors collect data in hundreds of spectral bands, it is essential to perform spectral unmixing to identify the spectra of all endmembers in(More)
In spite of the advances in pattern recognition technology, Handwritten Bangla Character Recognition (HBCR) (such as alpha-numeric and special characters) remains largely unsolved due to the presence of many perplexing characters and excessive cursive in Bangla handwriting. Even the best existing recognizers do not lead to satisfactory performance for(More)
Spatial information has shown significant contribution for hyperspectral image classification. Local Binary Pattern (LBP) can be used for extracting spatial texture features, however it is incapable of capturing textural and structural features of images at various resolution. Hence, we present a multiscale scheme on Complete LBP (CLBP) as well as on LBP to(More)