WO2018156004A1 - Method for measuring the quality index of meat by estimating the age of cattle by identifying connective tissue when splitting a carcass - Google Patents

Method for measuring the quality index of meat by estimating the age of cattle by identifying connective tissue when splitting a carcass Download PDF

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Publication number
WO2018156004A1
WO2018156004A1 PCT/MX2017/000021 MX2017000021W WO2018156004A1 WO 2018156004 A1 WO2018156004 A1 WO 2018156004A1 MX 2017000021 W MX2017000021 W MX 2017000021W WO 2018156004 A1 WO2018156004 A1 WO 2018156004A1
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Prior art keywords
connective tissue
capture
image
tissue
cut
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PCT/MX2017/000021
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Spanish (es)
French (fr)
Inventor
Pedro Gabriel GONZÁLEZ ESTRADA
Martín Gustavo VÁZQUEZ PALMA
Gastón Ramón TORRESCANO URRUTIA
Armida SÁNCHEZ ESCALANTE
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Gonzalez Estrada Pedro Gabriel
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Priority to PCT/MX2017/000021 priority Critical patent/WO2018156004A1/en
Publication of WO2018156004A1 publication Critical patent/WO2018156004A1/en

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/31—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry
    • G01N21/35—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light
    • G01N21/359—Investigating relative effect of material at wavelengths characteristic of specific elements or molecules, e.g. atomic absorption spectrometry using infrared light using near infrared light
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
    • G01N21/64—Fluorescence; Phosphorescence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/02—Food
    • G01N33/12—Meat; fish

Definitions

  • the present invention has its preponderant field of application in the measurement of quality of cuts of cattle carcasses, specifically by implementing methods and systems with artificial vision,
  • the basic considerations for the classification of beef is to evaluate the characteristics associated with its palatability and the expected relative desire of the meat in a cut expressed in standardized terms of quality. This situation leads to the generation of technologies that provide various tools for this purpose.
  • a desirable characteristic in this type of process is the monitoring of variables that allow to know the quality of the process of the slaughter plants, which are particularly reflected in the acidity, internal temperature, color and content percentages (marbling) of the channel, Traditionally, the classification of meats is based on the experience of long-trained people for this purpose.However, due to the implicit subjectivity of this type of evaluation, the trend in this industry aims to use technologies that lead to a situation where there is more accuracy and consistency, since a small variation in percentage points may determine a different market and cost of the product, responding to the tendency of industries to opt for modern systems such as artificial vision technology, electronic processing methods, and others, present
  • the purpose of the invention is to write a quality measurement method, especially cyclically by estimating the age of cattle by identifying and the percentage of connect
  • Patent No. CN102156129 describes a system and method of intelligent classification of meat quality in artificial vision comprising a positioning platform for the beef carcass. a dark room, a camera to capture images and a meat quality classification module. This technique is used for digital image processing of the cross section of the beef carcass and serves to analyze three indices of meat quality corresponding to marbling, fat color and red color in an effective area of Ribeye.
  • the invention AU2013264002 provides a method of standard determination of meat color classification that allows the determination of the detailed classification of meat color, comprising a camera for image capture and a color sorter of beef .
  • a method to predict the softness of the meat which allows the identification of tender meat and includes the insertion of one or more flat-bladed knives in a meat sample for the measurement of a value such as stress, strength or cutting energy and calculation of their softness factor based on a softness limit (US8225645).
  • the invention CN101706445 describes a method and an apparatus for classifying beef marbling. The method comprises the following stages: taking an image of the Ribeye cross section of the bovine; marble extraction in the Ribeye region and marble classification.
  • US Patent No. 712.3685 refers to the continuous determination of the fat content of meat where a conveyor belt is used to examine the meat when it is moving towards a radiation source that serves as a method of fat analysis.
  • Figure 1 is a schematic of the key activities for the Meat Quality Index Measurement Method through Identification of Connective Tissue and Spot Discrimination in 2D images of the present invention.
  • Figure 2 is a diagram of Sas curves of the Absorption Coefficient behavior of different types of molecules present in biological tissues through the infrared spectrum, from 900 nm to 1300 nm.
  • the method starts from the chord of the cattle channel f 101] between the 12th and 13th vertebra. S sample images of each wavelength are obtained. The first captures are made with illumination and IR reception filter at 1170 nm and 1210 nm [102] from which a first estimate of the location of connective tissue is obtained by differentiation with adipose tissue [103] present in said cut.
  • a capture with illumination and IR filtering of 900 nm wavelength is then performed [104]; From a contrast with the images generated at 1210 nm and 1170 rsm, it is differentiated where the connective tissue is segmented with muscle tissue [105], then the emission capture is generated at 440 nm [106] induced by fluorescence to ratify more precisely delineate the connective tissue [107] previously identified. Finally, the areas of each composition are calculated to obtain an average percentage of connective tissue [108] of the canal cutoff and compare with a predetermined database [109] to obtain a Quality Index [110].
  • FIG. 2 shows the behavior of the optical absorption curves in the infrared spectrum for different molecules present in a channel cut.
  • Muscle tissue contains water, elastin, collagen and other molecules.
  • the Elastin and Collagen curve is taken into account and for the fatty tissue the Lipid curve. It is illustrated that in the 900 nm wavelength the absorption of lipids and water is close to zero, so they are seen more clearly unlike the connective tissue that is observed more Dark. In the wavelength with a value of 1170 nm there is a very close absorption value for iodes and types of molecules; If starting from 1170 nm to 1210 nm, the adipose tissue can be differentiated from the other tissues with greater contrast.

Abstract

The present invention describes a method for measuring the quality of meat by identifying connective tissue and discriminating surface fat and blood stains that are present. Said method implies generating 2D images, with lighting and filtering at various wavelengths relying on fluorescence and infrared absorption. Algorithms for monochromatic segmentation by dynamic threshold value are implemented to segment the tissue in order to obtain the percentages of the content thereof.

Description

METODO DE MEDICIÓN DE ÍNDICE DE CALIDAD DE CARNE POR ESTIMACIÓN DE EDAD DE GANADO BOVINO MEDÍANTE IDENTIFICACIÓN DE TEJIDO CONECTIVO EN CORTE MEASUREMENT METHOD OF MEAT QUALITY INDEX BY ESTIMATION OF AGE OF BOVINE LIVES THROUGH IDENTIFICATION OF CONNECTIVE CUTTING FABRIC
DE CANAL CAMPO TÉCNICO DE LA INVENGÓN CHANNEL TECHNICAL FIELD OF THE INVENGON
La presente invención tiene su campo de aplicación preponderante en la medición de calidad de cortes de canales de ganado, específicamente mediante la implementación de métodos y sistemas con visión artificial,  The present invention has its preponderant field of application in the measurement of quality of cuts of cattle carcasses, specifically by implementing methods and systems with artificial vision,
ANTECEDENTES DE LA INVENCIÓN BACKGROUND OF THE INVENTION
Las consideraciones básicas para la clasificación de carne de vaca es evaluar las características asociadas con su palatabilidad y del deseo relativo esperado de ia carne en un corte expresado en términos estandarizados de calidad. Esta situación da pie a que se generen tecnologías que aportan diversas herramientas para tal fin. Una característica deseable en este tipo de procesos es el monitoreo de variables que permiten conocer la calidad del proceso de las plantas de sacrificio, mismas que son reflejadas particularmente en la acidez, temperatura interna, color y porcentajes de contenido (marmoleo} de ia canal, Tradicianalmente, la clasificación de carnes se basa en ia experiencia de personas largamente entrenadas para tal fin. Sin embargo, debido a la subjetividad implícita de este tipo de evaluaciones la tendencia en esta industria apunta a utilizar tecnologías que conlleven a una situación en que exista más exactitud y consistencia, pues una variación pequeña en puntos porcentuales podrá determinar un mercado y costo distinto del producto. Dando respuesta a la tendencia de las industrias por optar por sistemas modernos como tecnología de visión artificial, métodos de procesamiento electrónico, y otras, ia presente invención tiene como objetivo redamar un método de medición de calidad, específicamente por medio de la estimación de la edad del ganado mediante la identificación y el porcentaje de tejido conectivo y eliminación de manchas superficiales de sangre y gasa. Dicha invención con base en tecnología de visión artificial en el espectro del ultravioleta e infrarrojo, pueden identificar el tejido conectivo y sangre/grasa respectivamente. A continuación, se presenta una breve descripción de patentes actuales en relación ai tema, con el fin de resaltar ia actividad inventiva de la presente invención. La Patente No. CN102156129 describe un sistema y un método de clasificación inteligente de calidad de la carne en visión artificial que comprende una plataforma de colocación para ia canal de la res. un cuarto oscuro, una cámara para capturar imágenes y un módulo de clasificación de calidad de la carne. Esta técnica se utiliza para procesamiento de imagen digital de la sección transversal de la canal de res y sirve para análisis de tres índices de calidad de la carne correspondientes a marmoleado, color de la grasa y el color rojo en un area efectiva de Ribeye. Así mismo, la invención AU2013264002 proporciona un método de determinación estándar de clasificación del color de la carne que permite la determinación de ía clasificación detallada del color de la carne, comprendiendo una cámara para la captura de imágenes y un clasificador de color de carne de ternera. Entre la búsqueda se encuentra un aparato y un método para predecir Ía suavidad de la carne, que permite la identificación de carne tierna e incluye la inserción de una o más cuchillas de punta plana en una muestra de carne para la medición de un valor como el estrés, la fuerza o ía energía de corte y cálculo de factor de suavidad de los mismos basados en un límite de suavidad (US8225645). Similarmente, se encuentra la patente CN102608II8 que describe un dispositivo portátil de adquisición de imágenes del sistema de clasificación de calidad de carne basada en tecnología de visión artificial integrada por una carcasa, un reflector y una cámara industrial, el cual tiene el objetivo de capturar una imagen para después ser procesada. Por otro lado, la invención CN101706445 describe un método y un aparato de clasificación de marmoleado de carne de vaca. El método comprende las siguientes etapas: toma de una imagen de la sección transversal de Ribeye del bovino; extracción de marmoleado en región del Ribeye y clasificación del marmoleado. La Patente Estadounidense No. 712.3685 se refiere a la determinación continua del contenido de grasa de la carne en donde una cinta transportadora se utiliza para la examinar ia carne cuando esta avanza hacia una fuente de radiación que sirve como método de análisis de la grasa. The basic considerations for the classification of beef is to evaluate the characteristics associated with its palatability and the expected relative desire of the meat in a cut expressed in standardized terms of quality. This situation leads to the generation of technologies that provide various tools for this purpose. A desirable characteristic in this type of process is the monitoring of variables that allow to know the quality of the process of the slaughter plants, which are particularly reflected in the acidity, internal temperature, color and content percentages (marbling) of the channel, Traditionally, the classification of meats is based on the experience of long-trained people for this purpose.However, due to the implicit subjectivity of this type of evaluation, the trend in this industry aims to use technologies that lead to a situation where there is more accuracy and consistency, since a small variation in percentage points may determine a different market and cost of the product, responding to the tendency of industries to opt for modern systems such as artificial vision technology, electronic processing methods, and others, present The purpose of the invention is to write a quality measurement method, especially cyclically by estimating the age of cattle by identifying and the percentage of connective tissue and removing superficial blood and gauze spots. Said invention based on artificial vision technology in the ultraviolet and infrared spectrum, can identify connective tissue and blood / fat respectively. The following is a brief description of current patents in relation to the subject, in order to highlight the inventive activity of the present invention. Patent No. CN102156129 describes a system and method of intelligent classification of meat quality in artificial vision comprising a positioning platform for the beef carcass. a dark room, a camera to capture images and a meat quality classification module. This technique is used for digital image processing of the cross section of the beef carcass and serves to analyze three indices of meat quality corresponding to marbling, fat color and red color in an effective area of Ribeye. Likewise, the invention AU2013264002 provides a method of standard determination of meat color classification that allows the determination of the detailed classification of meat color, comprising a camera for image capture and a color sorter of beef . Among the search is an apparatus and a method to predict the softness of the meat, which allows the identification of tender meat and includes the insertion of one or more flat-bladed knives in a meat sample for the measurement of a value such as stress, strength or cutting energy and calculation of their softness factor based on a softness limit (US8225645). Similarly, there is the CN102608II8 patent that describes a portable image acquisition device of the meat quality classification system based on artificial vision technology composed of a housing, a reflector and an industrial camera, which aims to capture a Image to be processed later. On the other hand, the invention CN101706445 describes a method and an apparatus for classifying beef marbling. The method comprises the following stages: taking an image of the Ribeye cross section of the bovine; marble extraction in the Ribeye region and marble classification. US Patent No. 712.3685 refers to the continuous determination of the fat content of meat where a conveyor belt is used to examine the meat when it is moving towards a radiation source that serves as a method of fat analysis.
Como se menciona anteriormente, ninguna de las patentes considera un problema muy común en la clasificación automática de carnes que consiste en tomar manchas de grasa (causadas por ia operación de corte) y sangre como elementos profundos, siendo que deberían discriminarse al tratarse de estar solamente presentes en ía superficie. Otra consideración que no toman en cuenta las patentes anteriores es el porcentaje de tejido conectivo, ya que es un determinante en ia detección de edad del animal. A través de ia generación de imágenes en distintas longitudes de onda en el espectro UV e IR se ha podido atacar este y otros retos en esta práctica. DESCRIPCION DETALLADA DE LA INVENCIÓN As mentioned above, none of the patents considers a very common problem in the automatic classification of meats that consists of taking grease stains (caused by the cutting operation) and blood as deep elements, since they should be discriminated against when trying to be alone present on the surface. Another consideration that previous patents do not take into account is the percentage of connective tissue, since it is a determinant in the detection of the animal's age. Through the generation of images at different wavelengths in the UV and IR spectrum, this and other challenges have been attacked in this practice. DETAILED DESCRIPTION OF THE INVENTION
Los detalles característicos de la presente invención se muestran claramente en la siguiente descripción y en las figuras que se acompañan, las cuales se mencionan a manera de ejemplo por lo que no deben considerarse como una limitante para dicha invención. The characteristic details of the present invention are clearly shown in the following description and in the accompanying figures, which are mentioned by way of example and should therefore not be considered as a limitation for said invention.
Breve descripción de las figuras: Brief description of the figures:
La figura 1 es un esquema de las actividades clave para el Método de Medición de índice de Calidad de Carne a través de Identificación de Tejido Conectivo y Discriminación de Manchas en imágenes 2D de la presente invención.  Figure 1 is a schematic of the key activities for the Meat Quality Index Measurement Method through Identification of Connective Tissue and Spot Discrimination in 2D images of the present invention.
La figura 2 es un diagrama de Sas curvas del comportamiento del Coeficiente de Absorción de distintos tipos de moléculas presentes en tejidos biológicos a través del espectro infrarrojo, desde 900 nm hasta 1300 nm. Como se indica en la Figura 1, el método parte del corde de la canal de ganado f 101] entre la 12va y 13va vértebra. Se obtienen S imágenes muestra de cada longitud de onda. Las primeras capturas se realizan con iluminación y filtro de recepción IR a 1170 nm y 1210 nm [102] de donde se obtiene una primera estimación de localización de tejido conectivo mediante la diferenciación con tejido adiposo [103] presente en dicho corte. A continuación se realiza una captura con iluminación y filtrado IR de longitud de onda 900 nm [104]; a partir de un contraste con las imágenes generadas a 1210 nm y 1170 rsm, se diferencia dónde se segmenta el tejido conectivo con el tejido muscular [105], Seguidamente se genera la captura de emisión en 440 nm [106] inducida por fluorescencia para ratificar delimitar con mayor precisión el tejido conectivo [107] previamente identificado. Para finalizar, se calculan las áreas de cada composición para obtener un porcentaje de tejido conectivo [108] promedio del corte de la canal y comparar con una base de datos predeterminada [109] para obtener un Índice de calidad [110].  Figure 2 is a diagram of Sas curves of the Absorption Coefficient behavior of different types of molecules present in biological tissues through the infrared spectrum, from 900 nm to 1300 nm. As indicated in Figure 1, the method starts from the chord of the cattle channel f 101] between the 12th and 13th vertebra. S sample images of each wavelength are obtained. The first captures are made with illumination and IR reception filter at 1170 nm and 1210 nm [102] from which a first estimate of the location of connective tissue is obtained by differentiation with adipose tissue [103] present in said cut. A capture with illumination and IR filtering of 900 nm wavelength is then performed [104]; From a contrast with the images generated at 1210 nm and 1170 rsm, it is differentiated where the connective tissue is segmented with muscle tissue [105], then the emission capture is generated at 440 nm [106] induced by fluorescence to ratify more precisely delineate the connective tissue [107] previously identified. Finally, the areas of each composition are calculated to obtain an average percentage of connective tissue [108] of the canal cutoff and compare with a predetermined database [109] to obtain a Quality Index [110].
La figura 2 muestra el comportamiento de las curvas de absorción óptica en el espectro infrarrojo para distintas moléculas presentes en un corte de canal. El tejido musculoso contiene agua, elastina, colágeno y otras moléculas. Para identificar el tejido conectivo se toma en cuenta la curva de Elastina y Colágeno y para el tejido graso la curva para Lípidos. Se ilustra que en la longitud de onda de 900 nm la absorción de lípidos y agua es cercana a cero, por lo que se ven de manera más clara a diferencia del tejido conectivo que se observa más oscuro. En la longitud de onda con valor de 1170 nm se tiene un valor de absorción muy cercano para iodos ios tipos de moléculas; si se parte desde 1170 nm a 1210 nm se podrá diferenciar el tejido adiposo de los demás tejidos con un mayor contraste. Figure 2 shows the behavior of the optical absorption curves in the infrared spectrum for different molecules present in a channel cut. Muscle tissue contains water, elastin, collagen and other molecules. To identify the connective tissue, the Elastin and Collagen curve is taken into account and for the fatty tissue the Lipid curve. It is illustrated that in the 900 nm wavelength the absorption of lipids and water is close to zero, so they are seen more clearly unlike the connective tissue that is observed more Dark. In the wavelength with a value of 1170 nm there is a very close absorption value for iodes and types of molecules; If starting from 1170 nm to 1210 nm, the adipose tissue can be differentiated from the other tissues with greater contrast.

Claims

REIVINDICACIONES La presente invención reclama: CLAIMS The present invention claims:
1.- Método de medición de índice de calidad de carne por estimación de edad de ganado mediante identificación de tejido conectivo en corte de canal y eliminación de manchas superficiales de sangre y grasa, a través de análisis de absorción Infrarroja (IR) y fluorescencia en imágenes 2D con algoritmos de procesamiento de datos de colores monocromáticos, caracterizado por: a. - Sistema de iluminación compuesto por arreglo de Diodos Emisores de Luz (LEDs} Infrarroja en 3 longitudes de onda específicas con vaior pico 900 nm, 1170 nm y 1210 nm, y LEDs de emisión Ultravioleta (UV) con valor pico en longitud de onda de 380 nm. b. ~ Dos sensores ópticos, uno de ellos de captación lR en rango de 800 nm a 1300 nm, que integra mecanismo de intercambio de filtros ópticos con vaior nominal de 900 nm, 1170 nm y 1210 nm, el otro de ellos de captación RGB con filtro de 440 nm, c- Generación de imágenes de captura IR a 900 nm, 1170 nm y 1210 nm mediante LEDs y filtros IR, y de imagen de fluorescencia a través de iluminación UV de 380 nm y filtrado de captación de luz RGB en 440 nm, de corte de canal entre 12va y 13va vértebra a 24 horas post mortem; en total 5 muestras de 25 cm2 con localizaciones específicas, para cada longitud de onda. d.- Unidad de procesamiento recibe datos de imagen de sensores ópticos IR y RGB, ejecuta grupo de algoritmos de segmentación monocromática por valor umbral dinámico de 8 bits que compensa el vaior de límites característicos para cada segmentación con referencia del supuesto tejido muscular para distintos ángulos de captura de imagen. e.- Procesamiento de Imágenes a través de dicho grupo de algoritmos referente a identificación de tejido conectiva presente en corte de canal con exclusión de manchas superficiales con profundidad menor a 2 mm, basado en coeficientes de reflectividad y absorción IR de cada tipo de tejido en longitudes de onda: i) 1170 nm, donde se reconocen secciones para contrastar pixel por pixel con imagen obtenida a ii) 1210 nm para diferenciar tejido adiposo con tejido conectivo. Un contraste final se realiza con imágenes obtenidas a i) 1170 nm y lii} 900 nm para diferenciación de tejido conectivo con tejido muscular, Una vez identificado tejido conectivo según absorción IR se localiza en imagen, se compara con imagen obtenida de fluorescencia para ratificar la identidad de tejido conectivo y delimitar sus márgenes con mayor precisión. f.- Algoritmo para cálculo de áreas de tejido conectivo identificado y resto del corte para obtener porcentajes de contenido y estimar índice de calidad por edad de ganado mediante comparación con valores preestablecidos en la memoria del procesador, 1.- Method of measurement of meat quality index by estimation of age of cattle through identification of connective tissue in canal cut and removal of superficial blood and fat spots, through infrared (IR) absorption analysis and fluorescence in 2D images with monochromatic color data processing algorithms, characterized by: a. - Lighting system composed by arrangement of Light Emitting Diodes (LEDs) Infrared in 3 specific wavelengths with a peak peak 900 nm, 1170 nm and 1210 nm, and Ultraviolet (UV) emission LEDs with peak wavelength value of 380 nm b. ~ Two optical sensors, one of them with lR uptake in the range of 800 nm to 1300 nm, which integrates an optical filter exchange mechanism with a nominal value of 900 nm, 1170 nm and 1210 nm, the other one RGB capture with 440 nm filter, c- Generation of IR capture images at 900 nm, 1170 nm and 1210 nm using LEDs and IR filters, and fluorescence image through 380 nm UV illumination and capture capture of RGB light at 440 nm, cut-off channel between 12va and 13th vertebra at 24 hours post mortem; in total 5 samples of 25 cm 2 with specific locations, for each wavelength d.- Processing unit receives image data from IR and RGB optical sensors, execute group of segmentation algorithms Monochromatic tion by 8-bit dynamic threshold value that compensates for the value of characteristic limits for each segmentation with reference to the supposed muscle tissue for different angles of image capture. e.- Image Processing through said group of algorithms referring to identification of connective tissue present in the cut of the canal excluding surface spots with depth less than 2 mm, based on reflectivity coefficients and IR absorption of each type of tissue in wavelengths: i) 1170 nm, where sections are recognized to contrast pixel by pixel with image obtained at ii) 1210 nm to differentiate adipose tissue with connective tissue. A final contrast is made with images obtained ai) 1170 nm and lii} 900 nm for differentiation of connective tissue with muscle tissue. Once connective tissue has been identified according to IR absorption, it is located in an image, compared with an image. obtained from fluorescence to ratify the identity of connective tissue and delimit its margins with greater precision. f.- Algorithm for calculation of areas of identified connective tissue and rest of the cut to obtain percentages of content and estimate quality index by age of cattle by comparison with pre-established values in the processor memory,
2. - Método como el especificado en reivindicación 1, donde el sistema de iluminación cuenta con filtros polarizadores tanto en la emisión como en la recepción para eliminar capturas de reflejos especulares en la superficie del corte de la canal. 2. - Method as specified in claim 1, wherein the lighting system has polarizing filters both in the emission and in the reception to eliminate captures of mirror reflections on the surface of the cut of the channel.
3. - Método como el especificado en reivindicación 2, donde la captura de imágenes se realiza en un espacio encerrado que evitar la entrada de ruido luminoso por fuentes externas de luz. 3. - Method as specified in claim 2, wherein the capture of images is performed in an enclosed space that prevents the entry of light noise from external light sources.
4.- Método como el especificado en reivindicación 3, donde ía segmentación de los distintos tipos de tejidos ejecutada por la unidad de procesamiento es complementada a través de la retroalimentación de un usuario mediante una pantalla táctil. 4. Method as specified in claim 3, wherein the segmentation of the different types of tissues executed by the processing unit is complemented by the feedback of a user through a touch screen.
PCT/MX2017/000021 2017-02-24 2017-02-24 Method for measuring the quality index of meat by estimating the age of cattle by identifying connective tissue when splitting a carcass WO2018156004A1 (en)

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