Corresponding Author: Atiampo Kodjo Armand Laboratoire de Recherche en Informatique et Telecommunication, Institut National Polytechnique Félix Houphouet-Boigny, Abidjan, Cote d’Ivoire Email: [email protected] Abstract: The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from di...

Article history: Received 28 November 2014 Received in revised form 3 February 2015 Accepted 1 April 2015 Available online 13 April 2015

This paper includes face detection and recognition with the help of morphological shared weight neural network. Face detection is achieved with the combination of morphological hit miss operation and edge detection. Feature extraction of face image is done using Principal Component Analysis. These features are used to train the neural network. The output of neural network is compared with datab...

Speaker recognition suffers severe performance degradation under noisy environments. To solve this problem, we propose a novel method based on morphological component analysis. This method employs a universal background dictionary (UBD) to model common variability of all speakers, a speech dictionary of each speaker to model special variability of this speaker and a noise dictionary to model va...

In the last decade, the study of cosmic microwave background (CMB) data has become one of the most powerful tools to study and understand the Universe. More precisely, measuring the CMB power spectrum leads to the estimation of most cosmological parameters. Nevertheless, accessing such precious physical information requires extracting several different astrophysical components from the data. Re...

Morphological component analysis (MCA) for signal separation decomposes a signal into a superposition of morphological subcomponents, each of which is approximately sparse in a certain dictionary. Some of the dictionaries can also be modified to make them adaptive to local structure in images. We show that signal separation performance can be improved over the previous MCA approaches by replaci...

Morphological component analysis (MCA)[1, 2] is a popular image processing algorithm that extracts degrading patterns or textures from images and simultaneously performs inpainting (estimation of lost pixels). MCA has a wide range of uses, including MRI image enhancement and restoration of old photographs. However, in these authors’ opinions, an application that has been widely overlooked is th...