Nothing Special   »   [go: up one dir, main page]

Jayakody et al., 2017 - Google Patents

Microscope image based fully automated stomata detection and pore measurement method for grapevines

Jayakody et al., 2017

View HTML @Full View
Document ID
15299187085559724244
Author
Jayakody H
Liu S
Whitty M
Petrie P
Publication year
Publication venue
Plant methods

External Links

Snippet

Background Stomatal behavior in grapevines has been identified as a good indicator of the water stress level and overall health of the plant. Microscope images are often used to analyze stomatal behavior in plants. However, most of the current approaches involve …
Continue reading at link.springer.com (HTML) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/00147—Matching; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00127—Acquiring and recognising microscopic objects, e.g. biological cells and cellular parts
    • G06K9/0014—Pre-processing, e.g. image segmentation ; Feature extraction
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1468—Electro-optical investigation, e.g. flow cytometers with spatial resolution of the texture or inner structure of the particle
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Electro-optical investigation, e.g. flow cytometers
    • G01N15/1456—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals
    • G01N15/1459—Electro-optical investigation, e.g. flow cytometers without spatial resolution of the texture or inner structure of the particle, e.g. processing of pulse signals the analysis being performed on a sample stream
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/40—Analysis of texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
Jayakody et al. Microscope image based fully automated stomata detection and pore measurement method for grapevines
US10671833B2 (en) Analyzing digital holographic microscopy data for hematology applications
Tomari et al. Computer aided system for red blood cell classification in blood smear image
CN108596038B (en) Method for identifying red blood cells in excrement by combining morphological segmentation and neural network
Hortinela et al. Identification of abnormal red blood cells and diagnosing specific types of anemia using image processing and support vector machine
Shirazi et al. Extreme learning machine based microscopic red blood cells classification
CN113658174B (en) Microkernel histology image detection method based on deep learning and image processing algorithm
CN111860459B (en) Gramineae plant leaf pore index measurement method based on microscopic image
Parab et al. Red blood cell classification using image processing and CNN
US10769432B2 (en) Automated parameterization image pattern recognition method
Liang et al. StomataScorer: a portable and high‐throughput leaf stomata trait scorer combined with deep learning and an improved CV model
CN112784767A (en) Cell example segmentation algorithm based on leukocyte microscopic image
Jayakody et al. A generalised approach for high-throughput instance segmentation of stomata in microscope images
CN103984939A (en) Sample visible component classification method and system
García-Olalla et al. Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors
Suhail et al. A review on various methods for recognition of urine particles using digital microscopic images of urine sediments
Rahman et al. Automatic detection of white blood cells from microscopic images for malignancy classification of acute lymphoblastic leukemia
CN113902669A (en) Method and system for reading urine exfoliative cell fluid-based smear
Sapna et al. Techniques for segmentation and classification of leukocytes in blood smear images-a review
Jahani et al. Automatic identification of overlapping/touching chromosomes in microscopic images using morphological operators
López Flórez et al. Automatic Cell Counting With YOLOv5: A Fluorescence Microscopy Approach
Bose et al. Leaf diseases detection of medicinal plants based on image processing and machine learning processes
Abrol et al. An automated segmentation of leukocytes using modified watershed algorithm on peripheral blood smear images
Ergun Segmentation of Rays in Wood Microscopy Images Using the U-Net Model.
CN116757998A (en) Screening method and device for CTC cells and CTC-like cells based on AI