Francisco J. Gallegos Funes

Denoising robust image filter with retention of small-size details in presence of complex noise mixture

In this paper, we present the implementation of the robust detail preserving filters with complex noise suppression for image processing applications. The designed filter is the consequential connection of two filters. The first filter uses the value of central pixel of the filtering window to provide the preservation of fine details and the redescending M-estimators combined with the median estimator to provide impulsive noise rejection. The second filter uses the output of the first filter as the pre-estimator for an adaptive calculation in the redescending M-estimator. We investigated various types of influence functions in the M-estimator those are similar to the ones used in the Sigma filter to provide multiplicative noise suppression. The optimal values of the parameters of designed filters in presence of different noise mixture are determined. Different simulation data are presented in the paper and shown the statistical efficiency of the filters.

Impulsive noise suppression and analysis in color imaging

We present the analysis and simulation results for some modifications of the vectorial color imaging procedures those use at the second stage of magnitude processing the different order statistics filters. The technique of non-parametric filtering is presented and investigated in this paper too. For unknown functional form of noise density estimated from the observations we use the gray scalar filters to provide the reference vectors needed to realize the calculations. The performances of the traditional order statistics algorithms such as, median, Vector Median, alfa-trimmed mean, Wilcoxon, other order statistics M KNN are analyzed in the paper. For comparison analysis of the color imaging we use the following criterions: MAE; PSNR; MCRE; NCD Numerous simulation results which characterize the impulsive noise suppression and fine detail preservation are presented in the paper using different test images) such as: Lena, Mandrill, Peppers, etc. (256x256, 24 bits, RGB space). The algorithms those demonstrated good performance results have been applied to process the video sequences: “Miss America”, “Flowers” and Foreman” corrupted by impulsive noise. The results of the simulations presented in the paper show differences in color imaging by mentioned filtering technique and help to choose the filter that can satisfy to several criterion at dependence on noise level value.

Real-time image filtering with retention of small-size details and complex noise mixture

We present the implementation of real-time image filtering with retention of small-size details by means of use of DSP TMS320C6701. The filtering scheme is given for two filters connected in cascade. The first filter uses a similar scheme to KNN filter to provide the preservation of small-size details and the redescending M-estimators combined with the median estimator to provide impulsive noise rejection. The second filter uses an M filter to provide multiplicative noise suppression. We use different types of influence functions in the M-estimator to provide complex noise suppression. The efficiency of the proposed filter has been evaluated by numerous simulations.

Real-time color imaging using the vectorial order statistics filters

Color image and video sequence restoration and improvement are complicated due to presence of various kinds of random noise. Impulsive noise is introduced by acquisition or broadcasting errors into communication channels. Non linear filters can provide good performance in terms of the signal-to-noise ratio in different levels of corruption as soon as minimum error chromaticity and minimum perceptual error. This paper presents the capability and real-time processing features of several processing techniques such as “directional processing”, “non parametric approaches” and “order statistics” filters. Some of such the filters were: Median Filter (MF), Vector Median Filter (VMF), -Trimmed Mean Filter (ATMF), Generalized Vector Directional Filter (GVDF), Adaptive Multichannel Non Parametric Filter (AMNF), Median M-type K-Nearest Neighbour (MM-KNN) filter, Wilcoxon M-type K-Nearest Neighbour (WM-KNN) filter, Ansari-Bradley-Siegel-Tukey M-Type K-Nearest Neighbor (ABSTM-KNN) filter, etc. Extensive simulations in reference color RGB images “Lena”, “Mandrill”, “Peppers” and QCIF format video sequences (Miss America, Flowers and Foreman) have demonstrated that the proposed filters consistently can outperform the known nonlinear filters. The used performance criteria in color imaging were the traditional ones: PSNR, MAE and other specific for color imaging, NCD and MCRE. The real-time implementation of image filtering was realized on the DSP TMS320C6701. The processing time of proposed filters includes the duration of data acquisition, processing and store data. We simulated impulse corrupted color image QCIF sequences to demonstrate that some of the proposed and analyzing filters potentially could provide on line processing to quality video transmission of the images.

Novel detail-preserving robust filter for multiplicative and additive noise suppression in image processing

In this paper, we present a robust image filter that provides preservation of fine details and effective suppression of intensive multiplicative noise. The filter is based on the use of M (robust maximum likelihood)-estimators and R(rank)-estimators derived from the statistical theory of rank tests. At the first stage, to provide impulsive noise rejection, the introduced image filters uses the central pixel of the filtering window and the redescending M-estimators combined with the median estimator. At the second stage, to provide multiplicative noise suppression, a modified Sigma filter that implements the calculation scheme of a redescending M-estimator, is used. Visual and analytical analysis of simulation results shows that the proposed image filter has demonstrated fine detail preservation, good multiplicative noise suppression and impulsive noise removal.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patients response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%–93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.

Audiometer controlled by brain waves

Nowadays, that people have hearing diseases are more common but how to diagnose remain the same as fifty years ago.

Design and Implementation of an Affective Computing for Recognition and Generation of Behaviors in a Robot

In this paper adapting a robot to a social approach is presented in the selection of behaviors for interaction in real environments, which represents an emotionally charged, causing attention to focus on the most relevant aspects of the surroundings for the realization of the software system; which is part of a branch called affective computing, where there is a classification for emotion exhibited by a system. In this classification, the machine is included within the application for displaying and perceiving simulated emotions. Determining the emotion is by using templates based on probability theory of Markov. In this project the user interaction function is modified for vary with respect to the emotional state of the agent, which is determined according to the environment in which it is. Based in the Kinect sensor, emotional states according to body language of people and positions is detected. The system recognizes that the compatibility of the emotional state has been entered against it by users or persons with whom the project is tested. After recognizing the emotional state; a robot mimics the movements of the human pretending to have such emotions.

Characterization of the Surface Finish of Machined Parts Using Artificial Vision and Hough Transform

The surface finish of machined parts is of the utmost importance in determining their quality. This is not only for aesthetic purposes. Since in several industrial applications the machined parts have to be in contact with other parts, surface finish is also a determining factor in defining the capacity of wear, lubrication, and resistance to fatigue (i.e. service life). To determine the quality of machined parts, the roughness is analyzed to be a representation of the surface texture. Therefore, mathematical techniques have been developed to measure this criterion; such as the roughness meter, X-ray diffraction, ultrasound, electrical resistance, and image analysis (Alabi et al., 2008; Xie, 2008; Bradley & Wong, 2001).

Method of Learning for Life Pet Artificial

The present article shows the implementation of reinforcement learning, using the equation of the emotional intensity in a virtual pet for an artificial living environment interacting with the computer user. The pet looks like a very natural because of the learning algorithm proposed, which is based on a neural network. Learning depends on motivation given to the pet by its owner. The equation of emotional intensity gives values which allow to feed the neural network and thus generating learning.