CL2020000547A1 - Foam segmentation in flotation cells. - Google Patents

Foam segmentation in flotation cells.

Info

Publication number
CL2020000547A1
CL2020000547A1 CL2020000547A CL2020000547A CL2020000547A1 CL 2020000547 A1 CL2020000547 A1 CL 2020000547A1 CL 2020000547 A CL2020000547 A CL 2020000547A CL 2020000547 A CL2020000547 A CL 2020000547A CL 2020000547 A1 CL2020000547 A1 CL 2020000547A1
Authority
CL
Chile
Prior art keywords
image
foam
bubble
segmentation
images
Prior art date
Application number
CL2020000547A
Other languages
Spanish (es)
Inventor
Der Bijl Leendert Van
Shaun George Irwin
Kristo Botha
Original Assignee
Stone Three Digital Pty Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Stone Three Digital Pty Ltd filed Critical Stone Three Digital Pty Ltd
Publication of CL2020000547A1 publication Critical patent/CL2020000547A1/en

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
    • B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
    • B03D1/00—Flotation
    • B03D1/02—Froth-flotation processes
    • B03D1/028—Control and monitoring of flotation processes; computer models therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/12—Edge-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Computing Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Image Analysis (AREA)
  • Investigating Or Analysing Biological Materials (AREA)

Abstract

SISTEMA (100) Y MÉTODO PARA GENERAR UNA IMAGEN DE SEGMENTACIÓN DE BURBUJAS A PARTIR DE UNA IMAGEN DIGITAL DE UNA FASE DE ESPUMA DE UNA CÉLULA DE FLOTACIÓN (107). EL MÉTODO COMPRENDE LA RECEPCIÓN DE LA IMAGEN DE LA ESPUMA, LA APLICACIÓN DE UNA O MÁS REDES DE APRENDIZAJE EN PROFUNDIDAD A LA IMAGEN DE LA ESPUMA, Y LA CAPACITACIÓN DE LAS REDES DE APRENDIZAJE EN PROFUNDIDAD CON UNO O MÁS CONJUNTOS DE DATOS DE IMÁGENES DE CAPACITACIÓN DE IMÁGENES DE SEGMENTACIÓN DE BURBUJAS ETIQUETADAS Y PREDETERMINADAS PARA APRENDER A IDENTIFICAR CARACTERÍSTICAS ÚTILES PARA IDENTIFICAR AUTOMÁTICAMENTE LOS LÍMITES DE LAS BURBUJAS. EL MÉTODO COMPRENDE ADEMÁS LA GENERACIÓN DE LA IMAGEN DE SEGMENTACIÓN DE LA BURBUJA UTILIZANDO LAS REDES DE APRENDIZAJE EN PROFUNDIDAD, DE MODO QUE LA IMAGEN DE SEGMENTACIÓN DE LA BURBUJA INCLUYA DATOS DE LÍMITES IDENTIFICADOS QUE REPRESENTEN LOS LÍMITES ENTRE LAS BURBUJAS PRESENTES EN LA IMAGEN DE LA ESPUMA, Y LA SALIDA DE LA IMAGEN DE SEGMENTACIÓN DE LA BURBUJA.SYSTEM (100) AND METHOD TO GENERATE A BUBBLE SEGMENTATION IMAGE FROM A DIGITAL IMAGE OF A FOAM PHASE OF A FLOAT CELL (107). THE METHOD INCLUDES THE RECEPTION OF THE FOAM IMAGE, THE APPLICATION OF ONE OR MORE DEPTH LEARNING NETWORKS TO THE FOAM IMAGE, AND THE TRAINING OF DEEP LEARNING NETWORKS WITH ONE OR MORE SETS OF IMAGE DATA TRAINING OF LABELED AND DEFAULT BUBBLE SEGMENTATION IMAGES TO LEARN TO IDENTIFY USEFUL CHARACTERISTICS TO AUTOMATICALLY IDENTIFY THE BOUNDARIES OF BUBBLES. THE METHOD ALSO INCLUDES THE GENERATION OF THE SEGMENTATION IMAGE OF THE BUBBLE USING THE DEPTH LEARNING NETWORKS, SO THAT THE SEGMENTATION IMAGE OF THE BUBBLE INCLUDES DATA OF THE IDENTIFIED BOUNDARIES THAT THE IMAGES PRESENT IN THE IMAGES PRESENT IN THE BIMAGES PRESENTED IN THE IMAGES FOAM, AND THE BUBBLE SEGMENTATION IMAGE OUTPUT.

CL2020000547A 2017-09-08 2020-03-05 Foam segmentation in flotation cells. CL2020000547A1 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
ZA201706114 2017-09-08

Publications (1)

Publication Number Publication Date
CL2020000547A1 true CL2020000547A1 (en) 2020-09-04

Family

ID=65634915

Family Applications (1)

Application Number Title Priority Date Filing Date
CL2020000547A CL2020000547A1 (en) 2017-09-08 2020-03-05 Foam segmentation in flotation cells.

Country Status (4)

Country Link
AU (1) AU2018327270A1 (en)
CL (1) CL2020000547A1 (en)
WO (1) WO2019049060A1 (en)
ZA (1) ZA202001710B (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110689020A (en) * 2019-10-10 2020-01-14 湖南师范大学 Segmentation method of mineral flotation froth image and electronic equipment
CN111259972B (en) * 2020-01-20 2023-08-11 北矿机电科技有限责任公司 Flotation bubble identification method based on cascade classifier
CN111325281B (en) * 2020-03-05 2023-10-27 新希望六和股份有限公司 Training method and device for deep learning network, computer equipment and storage medium
CN113837193B (en) * 2021-09-23 2023-09-01 中南大学 Zinc flotation froth image segmentation method based on improved U-Net network

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2257158A1 (en) * 1996-05-31 1997-12-04 Baker Hughes Incorporated Method and apparatus for controlling froth flotation machines
CN101036904A (en) * 2007-04-30 2007-09-19 中南大学 Flotation froth image recognition device based on machine vision and the mine concentration grade forecast method
CN104331714B (en) * 2014-11-28 2018-03-16 福州大学 Platinum flotation grade evaluation method based on image data extraction and neural net model establishing

Also Published As

Publication number Publication date
ZA202001710B (en) 2021-04-28
AU2018327270A1 (en) 2020-04-09
BR112020004522A2 (en) 2020-09-08
WO2019049060A1 (en) 2019-03-14

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