Hussain et al., 2022 - Google Patents
A Simple and Efficient Deep Learning‐Based Framework for Automatic Fruit RecognitionHussain et al., 2022
View PDF- Document ID
- 16028360182134925760
- Author
- Hussain D
- Hussain I
- Ismail M
- Alabrah A
- Ullah S
- Alaghbari H
- Publication year
- Publication venue
- Computational Intelligence and Neuroscience
External Links
Snippet
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as lighting and …
- 235000013399 edible fruits 0 title abstract description 62
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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
-
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hussain et al. | A Simple and Efficient Deep Learning‐Based Framework for Automatic Fruit Recognition | |
Sousa et al. | Automation of waste sorting with deep learning | |
Kim et al. | Transfer learning for automated optical inspection | |
Ruz et al. | Automated visual inspection system for wood defect classification using computational intelligence techniques | |
Xiao et al. | Apple ripeness identification using deep learning | |
CN109886295A (en) | A kind of butterfly recognition methods neural network based and relevant device | |
Kamal et al. | FCN Network‐Based Weed and Crop Segmentation for IoT‐Aided Agriculture Applications | |
Ashok et al. | A novel fusion of deep learning and android application for real-time mango fruits disease detection | |
Praveen et al. | To detect plant disease identification on leaf using machine learning algorithms | |
Shashank et al. | Identifying epiphytes in drones photos with a conditional generative adversarial network (C-GAN) | |
Guo et al. | Varied channels region proposal and classification network for wildlife image classification under complex environment | |
Talasila et al. | [Retracted] Deep Learning‐Based Leaf Region Segmentation Using High‐Resolution Super HAD CCD and ISOCELL GW1 Sensors | |
Wang et al. | Hyperspectral target detection via deep multiple instance self-attention neural network | |
Kajabad et al. | YOLOv4 for urban object detection: Case of electronic inventory in St. Petersburg | |
Yaman et al. | Image processing and machine learning‐based classification method for hyperspectral images | |
Nur Alam et al. | Apple defect detection based on deep convolutional neural network | |
Ding et al. | Human activity recognition and location based on temporal analysis | |
More et al. | Agrosearch: A web based search tool for pomegranate diseases and pests detection using image processing | |
Hasanat et al. | Performance evaluation of transfer learning based deep convolutional neural network with limited fused spectrotemporal data for land cover classification. | |
Trinh et al. | Mangosteen Fruit Detection Using Improved Faster R-CNN | |
Hussain et al. | Research Article A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition | |
Setyawan et al. | AVIG: A Real-Time Visual Inspection for Guava Grading System Using Computer Vision and XGBoost | |
CN113850167A (en) | Commodity identification method and system based on edge calculation and machine deep learning | |
Marcelo et al. | Improving Vision-Based Detection of Fruits in a Camouflaged Environment with Deep Neural Networks | |
Almola et al. | Citrus diseases recognition by using CNN |