Multifractal characterisation and classification of bread crumb digital images

Adequate models of the bread crumb structure can be critical for understanding flow and transport processes in bread manufacturing, creating synthetic bread crumb images for photo-realistic rendering, evaluating similarities, and establishing quality features of different bread crumb types. In this article, multifractal analysis, employing the multifractal spectrum (MFS), has been applied to study the structure of the bread crumb in four varieties of bread (baguette, sliced, bran, and sandwich). The computed spectrum can be used to discriminate among bread crumbs from different types. Also, high correlations were found between some of these parameters and the porosity, coarseness, and heterogeneity of the samples. These results demonstrate that the MFS is an appropriate tool for characterising the internal structure of the bread crumb, and thus, it may be used to establish important quality properties it should have. The MFS has shown to provide local and global image features that are both robust and low-dimensional, leading to feature vectors that capture essential information for classification tasks. Results show that the MFS-based classification is able to distinguish different bread crumbs with very high accuracy. Multifractal modelling of the underlying structure can be an appropriate method for parameterising and simulating the appearance of different bread crumbs.Fil: Baravalle, Rodrigo Guillermo. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; ArgentinaFil: Gómez, Juan Carlos. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentin

Bread crumb classification using fractal and multifractal features

Adequate image descriptors are fundamental in image classification and object recognition. Main requirements for image features are robustness and low dimensionality which would lead to low classification errors in a variety of situations and with a reasonable computational cost. In this context, the identification of materials poses a significant challenge, since typical (geometric and/or differential) feature extraction methods are not robust enough. Texture features based on Fourier or wavelet transforms, on the other hand, do withstand geometric and illumination variations, but tend to require a high amount of descriptors to perform adequately. Recently, the theory of fractal sets has shown to provide local image features that are both robust and low-dimensional. In this work we apply fractal and multifractal feature extraction techniques for bread crumb classification based on colour scans of slices of different bread types. Preliminary results show that fractal based classification is able to distinguish different bread crumbs with very high accuracy.Fil: Baravalle, Rodrigo Guillermo. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; ArgentinaFil: Delrieux, Claudio Augusto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Investigación en Ingeniería Eléctrica; Argentina. Universidad Nacional del Sur. Departamento de Ingenieria Electrica y de Computadoras; ArgentinaFil: Gómez, Juan Carlos. Universidad Nacional de Rosario. Facultad de Ciencias Exactas, Ingeniería y Agrimensura; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentin

Multifractal analysis application to the study of fat and its infiltration in Iberian ham: Influence of racial and feeding factors and type of slicing

This paper explores the multifractal features of different commercial designations of Iberian ham (acorn 100% Iberian ham, acorn Iberian ham, feed/pasture Iberian ham and feed Iberian ham). This study has been done by taking as input the fatty infiltration patterns obtained from digital image analysis of ham cuts comparing mechanic and manual slicing. The yielded results show the multifractal nature of fatty connective tissue in Iberian ham, only when knife cutting is applied, confirming the differences between the designations according to their genetics and feeding. Thus, the multifractal parameters presented in this work could be considered as additional information for checking Iberian ham quality by using non-destructive methods based on the combination of image analysis and predictive techniques. Meat industry can take advantage of these methods to evaluate meat products, especially when fat-connective tissue with complex pattern distribution is involved

Caracterización de productos del cerdo ibérico mediante el análisis multifractal de imágenes

La presente tesis doctoral se presenta como compendio de publicaciones en las que se propone una metodología no destructiva para la obtención de indicadores que pueden ser usados en métodos predictivos de la calidad de productos cárnicos derivados del cerdo ibérico. Para ello se ha aplicado el análisis multifractal de imágenes digitales de esos productos. Dada la importancia del sector porcino en nuestro país resulta necesario la investigación de nuevas metodologías para el estudio de las características intrínsecas de la raza ibérica como es la infiltración grasa intramuscular que viene determinada por factores genéticos y de alimentación. A su vez, los competitivos precios sumados a la existencia de fraudes en las denominaciones comerciales hacen necesario que en este sector se desarrollen métodos de predicción que de manera rápida y barata ayuden a afrontar estos problemas. El análisis multifractal de imágenes cumple con estas dos características y actualmente está surgiendo como un método innovador en estudios relacionados con la determinación de la calidad y características de diferentes alimentos. Con esta premisa, en una primera publicación se aborda la naturaleza multifractal de la infiltración grasa de solomillo de cerdo blanco y solomillo de cerdo ibérico. Así, se diseñó y se desarrolló el sistema y condicionantes técnicos de obtención de imágenes, el sistema informático de procesado de las imágenes y la metodología del análisis multifractal. El procedimiento general consiste en la obtención de imágenes a color en condiciones homogéneas, tratamiento de las imágenes para obtener una muestra de la región de interés y posterior transformación a blanco (tejido grasoconectivo) y negro (magro), binarización de la imagen a un archivo txt y tratamiento mediante algoritmos para determinar los parámetros multifractales. Mediante este procedimiento, se determinaron los parámetros multifractales comprobando la existencia de autosemejanza en la distribución del tejido graso-conectivo y logrando, gracias a este hecho, la distinción de muestras de ambas razas. El contenido de la segunda publicación se centró en constatar la naturaleza multifractal de la infiltración grasa muestras de jamón de las cuatro denominaciones de cerdo ibérico cortadas a mano y a máquina. Se comprobó la naturaleza multifractal del tejido graso-conectivo de las muestras y la capacidad de distinción entre denominaciones para ambos tipos de corte. En la tercera publicación se propone una optimización en el procesado de las imágenes. Para ello se hace uso de un filtrado homomórfico de paso alto en las regiones de interés de los cortes de jamón ibérico cortado a mano mediante el uso de dos radios de filtro distintos, comparando los resultados del análisis multifractal de estas imágenes con los resultados de las imágenes sin filtro. El estudio llevado a cabo permite apreciar una notable mejora de los resultados para los cortes a mano de las cuatro denominaciones de jamón de cerdo ibérico. Los resultados obtenidos sugieren la idoneidad de la metodología propuesta para generar parámetros descriptores de la distribución caótica del tejido graso-conectivo que pueden ser usados para la predicción de la calidad de la carne del cerdo ibérico.This doctoral thesis is presented as a compendium of publications in which a non-destructive methodology is proposed to obtain indicators that can be used in predictive methods of the quality of meat products derived from Iberian pigs. For this aim, the use of multifractal analysis of images has been applied. Due to the importance of the Iberian pig sector in our country, it is necessary to investigate new methodologies for the study of the intrinsic characteristics of the Iberian breed such as intramuscular fat infiltration that is determined by genetic and feeding factors. At the same time, the competitive prices added to the existence of fraud in the commercial denominations make it necessary for this sector to develop prediction methods that quickly and cheaply help to face these problems. Multifractal image analysis meets these two characteristics and is currently emerging as an innovative method in studies related to the determination of the quality and characteristics of different foods. With this premise, the first study faces the multifractal nature of fatty infiltration of white pork tenderloin and Iberian pork tenderloin. In this study, the system and technical conditions for obtaining images, the computer system for image processing and the methodology of multifractal analysis were designed and developed. The general procedure consists in obtaining color images in homogeneous conditions, treatment of the images to obtain a sample of the region of interest and subsequent transformation to white (fatty-connective tissue) and black (lean), binarization of the image in a txt file and processing using algorithms to determine multifractal parameters. Through this procedure, the multifractal parameters for both pieces were determined by checking the existence of self-similarity in the distribution of fatty-connective tissue and achieving, thanks to this fact, the distinction of samples of both races. The content of the second publication focused on verifying the multifractal nature of the fatty infiltration of ham samples of the four denominations of Iberian pigs cut by hand and by machine. The multifractal nature and the ability to classify between denominations for both types of cut were checked. The third article discusses an optimization in image processing. For this purpose, a high-pass homomorphic filtering is used in the regions of interest of the Iberian ham cuts cut by hand using two different filter radii, comparing the results of the multifractal analysis of these images with the results of the unfiltered images The study results in a remarkable improvement of the results for the hand cuts of the four designations of Iberian pork ham. The results obtained suggest the suitability of the proposed methodology to generate parameters that describe the chaotic distribution of fatty-connective tissue that can be used to predict the quality of Iberian pig meat

Multifractal techniques for analysis and classification of emphysema images

This thesis proposes, develops and evaluates different multifractal methods for detection, segmentation and classification of medical images. This is achieved by studying the structures of the image and extracting the statistical self-similarity measures characterized by the Holder exponent, and using them to develop texture features for segmentation and classification. The theoretical framework for fulfilling these goals is based on the efficient computation of fractal dimension, which has been explored and extended in this work. This thesis investigates different ways of computing the fractal dimension of digital images and validates the accuracy of each method with fractal images with predefined fractal dimension. The box counting and the Higuchi methods are used for the estimation of fractal dimensions. A prototype system of the Higuchi fractal dimension of the computed tomography (CT) image is used to identify and detect some of the regions of the image with the presence of emphysema. The box counting method is also used for the development of the multifractal spectrum and applied to detect and identify the emphysema patterns. We propose a multifractal based approach for the classification of emphysema patterns by calculating the local singularity coefficients of an image using four multifractal intensity measures. One of the primary statistical measures of self-similarity used in the processing of tissue images is the Holder exponent (α-value) that represents the power law, which the intensity distribution satisfies in the local pixel neighbourhoods. The fractal dimension corresponding to each α-value gives a multifractal spectrum f(α) that was used as a feature descriptor for classification. A feature selection technique is introduced and implemented to extract some of the important features that could increase the discriminating capability of the descriptors and generate the maximum classification accuracy of the emphysema patterns. We propose to further improve the classification accuracy of emphysema CT patterns by combining the features extracted from the alpha-histograms and the multifractal descriptors to generate a new descriptor. The performances of the classifiers are measured by using the error matrix and the area under the receiver operating characteristic curve (AUC). The results at this stage demonstrated the proposed cascaded approach significantly improves the classification accuracy. Another multifractal based approach using a direct determination approach is investigated to demonstrate how multifractal characteristic parameters could be used for the identification of emphysema patterns in HRCT images. This further analysis reveals the multi-scale structures and characteristic properties of the emphysema images through the generalized dimensions. The results obtained confirm that this approach can also be effectively used for detecting and identifying emphysema patterns in CT images. Two new descriptors are proposed for accurate classification of emphysema patterns by hybrid concatenation of the local features extracted from the local binary patterns (LBP) and the global features obtained from the multifractal images. The proposed combined feature descriptors of the LBP and f(α) produced a very good performance with an overall classification accuracy of 98%. These performances outperform other state-of-the-art methods for emphysema pattern classification and demonstrate the discriminating power and robustness of the combined features for accurate classification of emphysema CT images. Overall, experimental results have shown that the multifractal could be effectively used for the classifications and detections of emphysema patterns in HRCT images

Caracterización fisicoquímica de pan molde blanco con sustitución parcial de harina de pajuro (erythrina edulis)

La desnutrición proteica es un síndrome nutricional importante que afecta a la población mundial, más de 170 millones de niños en edad preescolar en los países en desarrollo de Asia y África (Escott-Stump Sylvia & RD, 2010; Millis & Offiah, 2007). El Instituto Nacional de estadística e Informática (2015) y Cámara de Comercio de Lima, (2015) señala que en Perú más de 19,158 niños sufren desnutrición crónica. Algunos estudios reportan que los niños desnutridos tienen más problemas de aprendizaje y de comportamiento en comparación a los niños nutridos (Amaral et al., 2015; Feoli et al., 2006). Tirapegui, Baldi, y Ribeiro (1996) mencionan que la carencia de proteína en los niños se asocia frecuentemente al retraso del crecimiento. Por otro lado, Iqbal, Khalil, Ateeq, y Sayyar Khan (2006) señalan que para mejorar el estado nutricional de las personas es importante complementar la dieta con proteínas vegetales, en especial de las leguminosas.TesisLIMAEscuela Profesional de Ingeniería y ArquitecturaT01979TIA 2 V32 2016Procesamiento, seguridad y gestión en la Industria alimentari