Robust computer vision system for marbling meat segmentation

In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment muscle fat in various marbled meat samples using images acquired with different quality devices in an uncontrolled environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of sample treatments or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed

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A tese desenvolveu-se na área de Visão Computacional, concentrando-se em visão estereoscópica. Este processo consiste na fusão de duas ou mais imagens bidimensionais, retiradas de uma mesma cena, de maneira a se obter a reconstrução tridimensional da mesma. Mais especificamente, procuramos utilizar as informações de curvatura ao longo do contorno como uma medida de similaridade para o cálculo dos ponstos conjugados em estéreo. Neste contexto, desenvolveu-se uma plataforma que gera imagens em estéreo com o objetivo de analisar e validar o estudo realizado, e comparar os resultados com outras diversas metodologias utilizadas no processamento da visão estéreo artificial. O gerador de imagens em estéreo evita ou permite diversos tipos de ruído (iluminação da cena, imprecisão no cálculo dos coeficientes de calibragem das câmeras, independência da resolução da imagem), facilitando assim as investigaçõesThis thesis develops in the area of Computer Vision, focusing in the problem of stereo vision. This process consists of matching two or more images, in a such way as to obtain the three-dimensional reconstruction of the object. More specifically, the curvature information along the contour is used as a similarity measure for the calculation of the conjugated points in stereo imaging. In this context, a platform was developed to generate stereo images. The objective of this plataform is to analyze and validate the considered methodology, and to compare it with representative alternative approaches. The stereo image generator avoids or allows several noise types, such as those related to the illumination of the scene and imprecisions in the calculation of the gauging cameras coefficients, independently of the resolution of the imag

Not available

A tese desenvolveu-se na área de Visão Computacional, concentrando-se em visão estereoscópica. Este processo consiste na fusão de duas ou mais imagens bidimensionais, retiradas de uma mesma cena, de maneira a se obter a reconstrução tridimensional da mesma. Mais especificamente, procuramos utilizar as informações de curvatura ao longo do contorno como uma medida de similaridade para o cálculo dos ponstos conjugados em estéreo. Neste contexto, desenvolveu-se uma plataforma que gera imagens em estéreo com o objetivo de analisar e validar o estudo realizado, e comparar os resultados com outras diversas metodologias utilizadas no processamento da visão estéreo artificial. O gerador de imagens em estéreo evita ou permite diversos tipos de ruído (iluminação da cena, imprecisão no cálculo dos coeficientes de calibragem das câmeras, independência da resolução da imagem), facilitando assim as investigaçõesThis thesis develops in the area of Computer Vision, focusing in the problem of stereo vision. This process consists of matching two or more images, in a such way as to obtain the three-dimensional reconstruction of the object. More specifically, the curvature information along the contour is used as a similarity measure for the calculation of the conjugated points in stereo imaging. In this context, a platform was developed to generate stereo images. The objective of this plataform is to analyze and validate the considered methodology, and to compare it with representative alternative approaches. The stereo image generator avoids or allows several noise types, such as those related to the illumination of the scene and imprecisions in the calculation of the gauging cameras coefficients, independently of the resolution of the imag

Color Energy as a Seed Descriptor for Image Segmentation with Region Growing Algorithms on Skin Wound Images

Abstract-This paper presents a seed finding method for region growing segmentation approach using color channel energy in image regions. Instead of using the RGB system separated for each pixel, the proposal uses the energy on each color channel to improve the range of the possible values, then creates a more specific seed to detail different regions. Region size used to calculate energy was adjusted to verify the proposed method. Images used were real wound photos, taken from patients undergoing treatment at the university hospital. Results showed that energy on regions presents enough information to segment, leading to a high percentage of matching with experts marks

Robust computer vision system for marbling meat segmentation

In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment muscle fat in various marbled meat samples using images acquired with different quality devices in an uncontrolled environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of sample treatments or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed