Detection and Tracking of the Regions of Skin Using the Technique HS-ab

The content of this work is the proposal of the technical HS-ab for the detection and tracking of the regions of skin in real time. First, two proposed techniques are analyzed for the modeling of skin color in images using a combination of color spaces HSV with YCbCr and HSV with CIELab. In the process of definition of the intervals of pixels is taken into account the following: non-skin color uses the components H and S, whereas skin color uses the components Cb, Cr, a, and b. The results showed that the HS-ab technique is better than the HS-CbCr technique because of the precision in detecting skin color according to the percentages C (34.8%) and CDR (67%). After, the morphologic operations are applied to debug the images of the previous segmentation and detect regions of skin using methods such as blob extraction and contour detection. Subsequently, the tracking of skin color consists of calculating the moments and positions of each frame to know the trajectory of the regions of skin. The purpose of the work is to design an easy-to-use computer vision system that will facilitate the early rehabilitation of patients before they are clinically ready to be fitted with a prosthesis

Propuesta para Detectar y Procesar la Señal Muscular para la Manipulación de una Prótesis Mioeléctrica

En el presente trabajo se describe la implementación de un circuito para detectar y acondicionar las señales mioeléctricas (SME), el circuito está formado por una etapa de pre-amplificación, seguida de una etapa de filtrado, otra etapa de amplificación y por último la etapa de rectificación. El diseño de los filtros se realizó por medio del software FilterPro Instruments (Texas Instruments-Boulevard Dallas, Texas USA); y para observar la respuesta del circuito ya construido se utilizó el Software LabVIEW (National Instruments- Austin,Texas USA). El circuito electrónico desarrollado cumple con las especificaciones para la detección de las SME según el estado del arte. Posteriormente se implementa un método TKEO en MatLAB (MathWorks- Natick, Massachusetts, USA) para procesar las SME con la finalidad de detectar si el músculo está en actividad o no, el cual resultó robusto y eficiente además de que es de fácil implementación. El interés del circuito obtenido y el algoritmo de procesamiento de la señal mioeléctrica es para aplicarla en la activación de una prótesis mioeléctrica

Thresholding Image Techniques for Plant Segmentation

There are challenges in the image-based research to obtain information from the objects in the scene. Moreover, an image is a set of data points that can be processed as an object in similarity way. In addition, the research fields can be merged to generate a method for information extraction and pixel classification. A complete method is proposed to extract information from the data and generate a classification model capable to isolate those pixels that are plant from others are not. Some quantitative and qualitative results are shown to compare methods to extract information and create the best model. Classical and threshold-based state-of-art methods are grouped in the present work for reference and application in image segmentation, obtaining acceptable results in the plant isolation

Improved preclassification non local-means (IPNLM) for filtering of grayscale images degraded with additive white Gaussian noise

Abstract In this paper, we develop an extensive research on different types of grayscale images applying standard non local (NL)-means algorithm on different search and patch windows sizes to obtain optimal parameters where the values of criteria peak signal-to-noise ratio (PSNR), mean absolute error (MAE), and structural similarity index (SSIM) would be the best possible. The research shows quantitatively the importance on the appropriate selection of the windows sizes used during the filtering process. Based on the optimal parameters of the standard NL-means, we propose the improved preclassification non local-means (IPNLM) for filtering grayscale images degraded with additive white Gaussian noise (AWGN). The proposal uses a descriptors evaluation for each search window in the noisy image to apply statistical neighborhood preclassification respect to the homogeneity of each window to distinguish whether the current noisy pixel is in a homogeneous region or it is in an edge object region. Also, two thresholds based on the standard deviation of the local region in the noisy image are proposed to classify the pixels and perform a filtering level degree providing a commitment between the image denoising and the processing time. The proposal IPNLM reveals good results outperforming other filters based on NL-means by balancing the tradeoff between the noise suppression, detail preservation, and processing time. Experimental results demonstrate that IPNLM algorithm can reduce considerably the processing time from 8 through 15 times in comparison with the standard NL-means and other analyzed filters