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

, , , , , , and . Eng. Appl. Artif. Intell., (2019)

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Radial Textures: A New Approach to Analyze Meat Quality by Using MRI., , , , , and . CIARP, volume 11401 of Lecture Notes in Computer Science, page 479-486. Springer, (2018)Optimization of the image acquisition procedure in low-field MRI for non-destructive analysis of loin using predictive models., , , , and . PeerJ Comput. Sci., (2021)Finding the Largest Volume Parallelepipedon of Arbitrary Orientation in a Solid., , , , , , , and . IEEE Access, (2021)Non-destructively Prediction of Quality Parameters of Dry-Cured Iberian Ham by Applying Computer Vision and Low-Field MRI., , , , and . IbPRIA (1), volume 11867 of Lecture Notes in Computer Science, page 498-507. Springer, (2019)Development of a New Fractal Algorithm to Predict Quality Traits of MRI Loins., , , , , and . CAIP (1), volume 10424 of Lecture Notes in Computer Science, page 208-218. Springer, (2017)Radial textures: a new algorithm to analyze meat quality on MRI.. Multim. Tools Appl., 79 (29-30): 21557-21578 (2020)Including 3D-textures in a Computer Vision System to Analyze Quality Traits of Loin., , , , , and . ICVS, volume 9163 of Lecture Notes in Computer Science, page 456-465. Springer, (2015)Magnetic Resonance Imaging, texture analysis and regression techniques to non-destructively predict the quality characteristics of meat pieces., , , , , , and . Eng. Appl. Artif. Intell., (2019)A Variability Model for Template Personalization in Mobile Prosumer Environments., , , , and . AINA Workshops, page 1368-1373. IEEE Computer Society, (2013)Terrain Classification for outdoor mobile robots using PCA., , , and . CCIA, volume 202 of Frontiers in Artificial Intelligence and Applications, page 19-26. IOS Press, (2009)