Francisco Gallegos

Adaptive Vector Directional Filters to Process Multichannel Images

We present a novel algorithm that can suppress impulsive noise in video colour sequences. It uses order statistics, and directional and adaptive processing techniques.

Order Statistics - Fuzzy Approach in Processing of Multichannel Images and Video Sequences

There exist different applications of the image processing, such as medical imaging, high definition television, virtual reality, remote sensing, ultrasound and radar imaging, etc. In these applications, it is necessary to restore an image (or frames of video sequence) and decrease a noise influence exploiting the filtering algorithms that form a part of a general image processing system. The images are corrupted by noise, in sensors employed or maybe, during signal transmission. Also, several kinds of noises are produced by natural phenomena (atmospheric, scattering, interference, etc.). Usually, real noises are described by different models, there exist impulsive, additive and multiplicative (speckle) ones. So, the image pre-processing efficient scheme should be one of important part in any vision application permitting to suppress a noise, saving the image performances, such as, edge and fine features preservation, and also the chromaticity properties for the multichannel (multispectral) images. This demands to have several efficient filtering schemes, which depend on noise type and priory information, in a pre-processing stage of image or video sequence processing system. The main objective of present chapter is to exhibit several justified approaches in restoration of the images and video sequences, which are usually affected by noise of different nature, which can be efficiently used in different applications of the multichannel (multispectral) images and sequences. Here, multispectral image is defined in such a way, where each a pixel is represented by a number of channels that carry out information about its spectral content. Multispectral images span the domain of images from conventional three-channel colour images to hyperspectral imagery with hundred of bands/channels used in remote sensing applications, medicine, spectrometry, etc. In literature, there exist a lot of algorithms that process two dimensional (2D) images (Franke et al., 2000); (Russo & Ramponi, 1996); (Schulte et al., 2006, 2007a, 2007b, 2007c); (Shaomin & Lucke, 1994); (Nie & Barner, 2006); (Morillas et al., 2005, 2006, 2008a, 2008b, 2009); (Camarena et al., 2008, 2010); (Ma et al., 2007); (Amer & Schroder, 1996); (Xu, 2009). We compare the proposed 2D fuzzy framework with recently presented 2D-INR filter based on fuzzy logic (Schulte et al., 2007b), where a noise is detected preserving the fine features and edges in an image. Also, other promising classes of 2D processing algorithms are employed as comparative ones: 2D-AMNF, 2D-AMNF2 (Adaptive Multichannel Filters)(Plataniotis & Androutsos et al., 1997); (Plataniotis & Venetsanopoulos, 2000); 2D7

Proposal of a mechanism for an electrical elbow prosthesis

This work focuses on the description of the system of mechanism for a electrical prosthesis. Considering system performance and system of transmission, both will allow to make the movements of the electrical prosthesis. The electrical prosthesis was designed to replace the function of the elbow joint of an upper limb. In the system of transmission is based on rotary motion since it is similar to the movements of the elbow joint which is flexion and extension, to carry out these movements were determined using the gears; of which were calculated to produce them, it is described in this work. On the other hand, the system performance was chosen by using the electrical actuators for high efficiency, high availability and variety of sizes. To be able to define our system of mechanism of a electrical prosthesis, it should be considered several criteria listed in this work, and finally the torque output was calculated to know how much weight can load the prosthesis designed.

Impulse Denoising Using Fuzzy and Directional Processing on a DSP

This article presents novel fuzzy and directional techniques in denoising colour images contaminated by impulsive random noise. Novel approach has demonstrated excellent performance in image denoising and feature image preservation. Real-Time analysis is realized on a Digital Signal Processor (Texas Instruments).

LSM static signs recognition using image processing

Object recognition is a widely field in artificial vision application, because now the machines are intended to become autonomous. This article presents the methodology for recognizing objects in an image, tecniques used are: segmentation, feature extraction and classification object within the image. Fuzzy c-means algorithm was used for segmentation, which is a fuzzy classification algorithm in which a data can belong to multiple groups in different degree of membership. For feature extraction were used Hu moments as a geometrical descriptors, which are a mathematical tool that provides seven moments that identify geometric features of the objects, main characteristics of Hu moments is its invariance to rotation, scaling and translation. Finally, the geometric features that provide the seven moments are used as input to a classifier, delivering results: these moments are used to identify the signs of the alphabet of the Mexican Sign Language (LSM).