In this paper, we show the progress for Arabic speech recognition by incorporating contextual information into the process of morphological decomposition. The new approach achieves lower out-of-vocabulary and word error rates when compared to our previous work, in which the morphological decomposition relies on word-level information only. We also describe how the vocalization procedure is impr...

Shape representation and description plays an important role in many areas of computer vision and pattern recognition. Mathematical morphology has emerged as a standard tool for detecting and isolating specific geometric structures in image data. Morphological shape decomposition techniques extract the structural components from the shape. The representation efficiency of the existing morpholog...

Pixel-wise classification in high-dimensional multivariate images is investigated. The proposed method deals with the joint use of spectral and spatial information provided in hyperspectral images. Additive morphological decomposition (AMD) based on morphological operators is proposed. AMD defines a scale-space decomposition for multivariate images without any loss of information. AMD is modele...

This thesis contributes to the field of mathematical morphology and illustrates how multivariate statistics and machine learning techniques can be exploited to design vector ordering and to include results of morphological operators in the pipeline of multivariate image analysis. In particular, we make use of supervised learning, random projections, tensor representations and conditional transf...

Morphological residues represent an image in a hierarchical way by means of a decomposition of its structures and according to a size parameter λ. From this decomposition, we can obtain a relation between the different residual levels associated with the complexity of the image structures. In this work, we introduce a new method to filter out components of gray-scale images based on the morphol...

A novel approach of edge detection is proposed that utilizes a bidimensional empirical mode decomposition (BEMD) method as the primary tool. For this purpose, a recently developed fast and adaptive BEMD (FABEMD) is used to decompose the given image into several bidimensional intrinsic mode functions (BIMFs). In FABEMD, order statistics filters (OSFs) are employed to get the upper and lower enve...