Paheding Sidike

Learn More
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)
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)
Extreme Learning Machines (ELM) has been introduced as a new algorithm for training single hidden layer feedforward neural networks instead of the classical gradient-based approaches. Based on the consistency property of data, which enforces similar samples to share similar properties, ELM is a biologically inspired learning algorithm that learns much(More)
The challenges existing for current intensity-based histogram feature tracking methods in wide area motion imagery include object structural information distortions and background variations, such as different pavement or ground types. All of these challenges need to be met in order to have a robust object tracker, while attaining to be computed at an(More)
  • 1