An open-set framework for underwater image classification using autoencoders

Neural network-based underwater image classification for Autonomous Underwater Vehicles

Image processing has been one of hot issues for real world robot applications such as navigation and visual servoing. In case of underwater robot application, however, conventional optical camera-based images have many limitations for real application due to visibility in turbid water, image saturation under underwater light in the deep water, and short visible range in the water. Thus, most of...

An integrated framework for image classification

This paper presents a novel method for classifying an image into one of predeened classes in a data bank by applying mutual information to a representation of the Fourier amplitude domain. Template and test images are made translation and rotation invariant through the Fourier-Mellin transform. While mutual information could be employed here, we choose instead to apply it to the lower dimension...

Image Tranformation Using Variational Autoencoders

The way data are stored in a computer is definitively not the most intelligible approach that one can think about even though it makes computation and communication very convenient. This issue is essentially equivalent to dimensionality reduction problem under the assumption that the data can be embedded into a low-dimensional smooth manifold (Olah [2014]). We have seen couple of examples in th...

A multiphase level set framework for image segmentation using

Unsupervised Texture Image Segmentation Using MRFEM Framework

Texture image analysis is one of the most important working realms of image processing in medical sciences and industry. Up to present, different approaches have been proposed for segmentation of texture images. In this paper, we offered unsupervised texture image segmentation based on Markov Random Field (MRF) model. First, we used Gabor filter with different parameters’ (frequency, orientatio...