Automatic marbling prediction of sliced dry-cured ham using image segmentation, texture analysis and regression

Dry-cured ham is a traditional Mediterranean meat product consumed throughout the world. This product is very variable in terms of composition and quality. Consumer’s acceptability of this product is influenced by different factors, in particular, visual intramuscular fat and its distribution across the slice, also known as marbling. On-line marbling assessment is of great interest for the industry for classification purposes. However, until now this assessment has been traditionally carried out by panels of experts and this methodology cannot be implement in industry. We propose a complete automatic system to predict marbling degree of dry-cured ham slices, which combines: (1) the color texture features of regions of interest (ROIs) extracted automatically for each muscle; and (2) machine learning models to predict the marbling. For the ROIs extraction algorithm more than the 90% of pixels of the ROI fall into the true muscle. The proposed system achieves a correlation of 0.92 using the support vector regression and a set of color texture features including statistics of each channel of RGB color image and Haralick’s coefficients of its gray-level version. The mean absolute error was 0.46, which is lower than the standard desviation (0.5) of the marbling scores evaluated by experts. This high accuracy in the marbling prediction for sliced dry-cured ham would allow to deploy its application in the dry-cured ham industryThis work has received financial support from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2020– 2023) and the European Union (European Regional Development Fund–ERDF), Project ED431G-2019/04. IRTA’s contribution was also funded by the CCLabel project (RTI-2018- 096883-R-C41) and the CERCA programme from Generalitat de CatalunyaS

Do we need hundreds of classifiers to solve real world classification problems?

We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifiers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearest-neighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large- scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classifiers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively)We would like to acknowledge support from the Spanish Ministry of Science and Innovation (MICINN), which supported this work under projects TIN2011-22935 and TIN2012-32262S

Potential Fields as an External Force and Algorithmic Improvements in Deformable Models

Deformable Models are extensively used as a Pattern Recognition technique. They are curves defined within an image domain that can be moved under the influence of internal and external forces. Some trade-offs of standard deformable models algorithms are the selection of image energy function (external force), the location of initial snake and the attraction of contour points to local energy minima when the snake is being deformed. This paper proposes a new procedure using potential fields as external forces. In addition, standard Deformable Models algorithm has been enhanced with both this new external force and algorithmic improvements. The performance of the presented approach has been successfully proved to extract muscles from Magnetic Resonance Imaging (MRI) sequences of Iberian ham at different maturation stages in order to calculate their volume change. The main conclusions of this paper are the practical viability of potential fields used as external forces, as well as the validation of the algorithmic improvements developed. The feasibility of applying Computer Vision techniques, in conjunction with MRI, for determining automatically the optimal ripening time of the Iberian ham is a practical conclusion reached with the proposed approach

An extensive experimental survey of regression methods

Regression is a very relevant problem in machine learning, with many different available approaches. The current work presents a comparison of a large collection composed by 77 popular regression models which belong to 19 families: linear and generalized linear models, generalized additive models, least squares, projection methods, LASSO and ridge regression, Bayesian models, Gaussian processes, quantile regression, nearest neighbors, regression trees and rules, random forests, bagging and boosting, neural networks, deep learning and support vector regression. These methods are evaluated using all the regression datasets of the UCI machine learning repository (83 datasets), with some exceptions due to technical reasons. The experimental work identifies several outstanding regression models: the M5 rule-based model with corrections based on nearest neighbors (cubist), the gradient boosted machine (gbm), the boosting ensemble of regression trees (bstTree) and the M5 regression tree. Cubist achieves the best squared correlation (R2) in 15.7% of datasets being very near to it, with difference below 0.2 for 89.1% of datasets, and the median of these differences over the dataset collection is very low (0.0192), compared e.g. to the classical linear regression (0.150). However, cubist is slow and fails in several large datasets, while other similar regression models as M5 never fail and its difference to the best R2 is below 0.2 for 92.8% of datasets. Other well-performing regression models are the committee of neural networks (avNNet), extremely randomized regression trees (extraTrees, which achieves the best R2 in 33.7% of datasets), random forest (rf) and ε-support vector regression (svr), but they are slower and fail in several datasets. The fastest regression model is least angle regression lars, which is 70 and 2,115 times faster than M5 and cubist, respectively. The model which requires least memory is non-negative least squares (nnls), about 2 GB, similarly to cubist, while M5 requires about 8 GB. For 97.6% of datasets there is a regression model among the 10 bests which is very near (difference below 0.1) to the best R2, which increases to 100% allowing differences of 0.2. Therefore, provided that our dataset and model collection are representative enough, the main conclusion of this study is that, for a new regression problem, some model in our top-10 should achieve R2 near to the best attainable for that problemThis work has received financial support from the Erasmus Mundus Euphrates programme [project number 2013-2540/001-001-EMA2], from the Xunta de Galicia (Centro singular de investigación de Galicia, accreditation 2016–2019) and the European Union (European Regional Development Fund — ERDF), Project MTM2016–76969–P (Spanish State Research Agency, AEI)co-funded by the European Regional Development Fund (ERDF) and IAP network from Belgian Science PolicyS

Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces

The quality of meat products is traditionally assessed by chemical or sensorial analysis, which are time consuming, need specialized technicians and destroy the products. The development of new technologies to monitor meat pieces using non-destructive methods in order to establish their quality is earning importance in the last years. An increasing number of studies have been carried out on meat pieces combining Magnetic Resonance Imaging (MRI), texture descriptors and regression techniques to predict several physico-chemical or sensorial attributes of the meat, mainly different types of pig ham and loins. In spite of the importance of the problem, the conclusions of these works are still preliminary because they only use the most classical texture descriptors and regressors instead of stronger methods, and because the methodology used to measure the performance is optimistic. In this work, we test a wide range of texture analysis techniques and regression methods using a realistic methodology to predict several physico-chemical and sensorial attributes of different meat pieces of Iberian pigs. The texture descriptors include statistical techniques, like Haralick descriptors, local binary patterns, fractal features and frequential descriptors, like Gabor or wavelet features. The regression techniques include linear regressors, neural networks, deep learning, support vector machines, regression trees, ensembles, boosting machines and random forests, among others. We developed experiments using 15 texture feature vectors, 28 regressors over 4 datasets of Iberian pig meat pieces to predict 39 physico-chemical and sensorial attributes, summarizing16,380 experiments. There is not any combination of texture vector and regressor which provides the best result for all attributes tested. Nevertheless, all these experiments provided the following conclusions: (1) the regressor performance, measured using the squared correlation (R2), is from good to excellent (above 0.5625) for 29 out of 39 attributes tested; (2) the WAPE (Weighted Absolute Percent Error) is lower than 2% for 32 out of 37 attributes; (3) the dispersion in computer predictions around the true attributes is lower or similar than the dispersion in the labeling expert’s for the majority of attributes (85%); and (4) differences between predicted and true values are not statistically significant for 29 out of 37 attributes using the Wilcoxon ranksum statistical test. We can conclude that these results provide a high reliability for an automatic system to predict the quality of meat pieces, which may operate on-line in the meat industries in the futureThe authors wish to acknowledge the funding received from the FEDER-MICCIN Infrastructure Research Project (UNEX-10-1E-402), Junta de Extremadura economic support for research group (GRU15173 and GRU15113), from the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2016–2019) and from the European Union (European Regional Development Fund — ERDF)S

Laboratorio de Programación I

Las asignaturas de Laboratorio de Programación I se imparte en el primer curso de Informática. El presente trabajo muestra el enfoque, tanto práctico como teórico de la asignatura. Además muestra el temario que se imparte, los bloques en que se divide, las prácticas que deben realizar los alumnos y los criterios de evaluación que siguen los profesores. Por último se comentan los problemas existentes debido a la masificación de las aulas y una breve conclusión

Unha enxeñeira ou científica en cada cole

Póster presentado na V XORNADA UNIVERSITARIA GALEGA EN XÉNERO. TRANSFORMANDO DENDE A UNIVERSIDADE. Vigo, 7 Xullo 2017Nesta comunicación, presentamos o proxecto Unha enxeñeira ou científica en cada cole organizado pola Oficina de Igualdade de Xénero da Universidade de Santiago de Compostela (USC) en colaboración co Concello de Santiago de Compostela. Esta iniciativa pretende incentivar a presenza de rapazas en carreiras relacionadas coas disciplinas STEM (ciencia, enxeñería, tecnoloxía e matemáticas), mediante actividades didácticas nos centros educativos que rachen cos estereotipos sexistas da nosa sociedade. A actividade didáctica consistiu na realización de dezanove obradoiros, dirixidos a nenas e nenos de 5º ou 6º de primaria e realizados nos meses de setembro e outubro de 2016. Os obradoiros foron impartidos por profesoras ou investigadoras da USC e do Centro de Supercomputación de Galicia (CESGA) para crear referentes femininos e incentivar a presenza de rapazas no ámbito científico tecnolóxico. Ademais, estes obradoiros amosaron a relación da ciencia e da tecnoloxía coa nosa vida cotiá e serviron para achegar ao alumnado a estas disciplinas dun xeito lúdicoConcello de Santiago de Compostel

Perspectiva de género en Inteligencia Artificial, una necesidad

[ES] En este artículo analizamos la transversalización de la perspectiva de género en el campo de la Inteligencia Artificial (IA), cuyas aplicaciones influyen cada vez más en nuestras actividades cotidianas. Una disciplina altamente masculinizada, donde la mayoría de los profesionales son hombres y sus experiencias conforman y dominan la creación de algoritmos. Para reconocer la existencia de sesgos discriminatorios de género en los algoritmos y limitar sus consecuencias, es necesario introducir la perspectiva de género en estos estudios. En este documento revisamos el grado de introducción de competencias en género en los grados de IA en el estado español con el fin de mejorar la formación en género del alumnado

Gender perspective in Artificial Intelligence

Presentación no Congreso TEEM 2020, na sesión Track 3. Bridging the diversity gap in STEM. https://youtu.be/KCgMjvpAQFUIn Spain, the majority of university students are women, but the gender distribution in different fields of study is uneven and there are certain disciplines, as engineering and some experimental sciences where less than 30% are women. This reduced participation of women, very low in disciplines related with technology motivates the hegemonic presence of androcentric and sexist values both in knowledge and in the products and information technologies that we can find in the market. In highly masculinized disciplines the inclusion of the gender perspective is important whenever the content, the results or the applications of a subject may affect human beings directly or indirectly. Specially, in disciplines related with technology where it is very common for men and women to be affected differently by technological developments. This paper focus in the context of Artificial Intelligence (AI) field, where the majority of professionals are men and their experiences shape and dominate algorithm creation. To recognize the existence of gender discriminatory biases in the algorithms and limit their consequences in the offline world, we need to introduce the gender perspective in these studies. In this document we analyze the degree of introduction of the gender perspective in the AI grades and how to improve the gender competences of the student