GB2585933A8 - System and method for processing images - Google Patents
System and method for processing images Download PDFInfo
- Publication number
- GB2585933A8 GB2585933A8 GB1910639.2A GB201910639A GB2585933A8 GB 2585933 A8 GB2585933 A8 GB 2585933A8 GB 201910639 A GB201910639 A GB 201910639A GB 2585933 A8 GB2585933 A8 GB 2585933A8
- Authority
- GB
- United Kingdom
- Prior art keywords
- sub
- clusters
- sample
- generation parameters
- cluster
- Prior art date
- Legal status (The legal status 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 status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/211—Selection of the most significant subset of features
- G06F18/2113—Selection of the most significant subset of features by ranking or filtering the set of features, e.g. using a measure of variance or of feature cross-correlation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/176—Urban or other man-made structures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
-
- 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
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/24—Character recognition characterised by the processing or recognition method
- G06V30/248—Character recognition characterised by the processing or recognition method involving plural approaches, e.g. verification by template match; Resolving confusion among similar patterns, e.g. "O" versus "Q"
- G06V30/2528—Combination of methods, e.g. classifiers, working on the same input data
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
A method for detecting objects in video comprising: determining sample generation parameters S102; applying sample generation parameters to an image to generate sample sub-sections S104, (44, 46, 48, Fig. 1c); analysing sample sub-sections to identify clusters S108; combining overlapping clusters to form a plot (S114); and classifying the plot to identify a represented object S118. Clusters may be identified by: using a back-detection algorithm on sub-sections to obtain grayscale images; and thresholding the grayscale images S106. A likelihood value may be determined for each cluster by a machine learning algorithm. The estimation value may be based on cluster metrics e.g. size; sample sub-section generation parameters; and cluster overlap extent S110. Cluster metrics may be used to determine subsequent sample generation parameters. Clusters may be filtered based on likelihood value before overlapping clusters are combined S112. Plots may be categorised by a feature thereof (e.g. real-world size) S116 before a classifier trained for that category performs identification S118. Plots may be iteratively divided into sub-plots and each sub-plot classified until a classification corresponding to an object of interest is awarded S120, S122. The method may be repeated for subsequent images to track the movement of an object (Fig. 3).
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1910639.2A GB2585933B8 (en) | 2019-07-25 | 2019-07-25 | System and method for processing images |
| PCT/GB2020/051652 WO2021014120A1 (en) | 2019-07-25 | 2020-07-09 | System and method for processing images |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| GB1910639.2A GB2585933B8 (en) | 2019-07-25 | 2019-07-25 | System and method for processing images |
Publications (5)
| Publication Number | Publication Date |
|---|---|
| GB201910639D0 GB201910639D0 (en) | 2019-09-11 |
| GB2585933A GB2585933A (en) | 2021-01-27 |
| GB2585933B GB2585933B (en) | 2023-07-19 |
| GB2585933B8 GB2585933B8 (en) | 2023-08-16 |
| GB2585933A8 true GB2585933A8 (en) | 2023-08-16 |
Family
ID=67990486
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB1910639.2A Active GB2585933B8 (en) | 2019-07-25 | 2019-07-25 | System and method for processing images |
Country Status (1)
| Country | Link |
|---|---|
| GB (1) | GB2585933B8 (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110874825B (en) * | 2019-10-29 | 2023-05-30 | 南昌大学 | Method for extracting binary image of water trace on surface of composite insulator |
| CN111275107A (en) * | 2020-01-20 | 2020-06-12 | 西安奥卡云数据科技有限公司 | Multi-label scene image classification method and device based on transfer learning |
| CN111860436A (en) * | 2020-07-31 | 2020-10-30 | 济南浪潮高新科技投资发展有限公司 | Method for improving detection reliability of detection system |
| CN112419231A (en) * | 2020-10-15 | 2021-02-26 | 上海眼控科技股份有限公司 | Visibility determination method and device, computer equipment and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10417523B2 (en) * | 2016-11-07 | 2019-09-17 | Ayasdi Ai Llc | Dimension grouping and reduction for model generation, testing, and documentation |
| CN108717539A (en) * | 2018-06-11 | 2018-10-30 | 北京航空航天大学 | A kind of small size Ship Detection |
| CN109448015B (en) * | 2018-10-30 | 2021-03-30 | 河北工业大学 | Image collaborative segmentation method based on saliency map fusion |
| CN109741341B (en) * | 2018-12-20 | 2022-11-01 | 华东师范大学 | Image segmentation method based on super-pixel and long-and-short-term memory network |
-
2019
- 2019-07-25 GB GB1910639.2A patent/GB2585933B8/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| GB2585933A (en) | 2021-01-27 |
| GB2585933B8 (en) | 2023-08-16 |
| GB2585933B (en) | 2023-07-19 |
| GB201910639D0 (en) | 2019-09-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| GB2585933A8 (en) | System and method for processing images | |
| Li et al. | Can micro-expression be recognized based on single apex frame? | |
| CN108562589B (en) | Method for detecting surface defects of magnetic circuit material | |
| EP3501002A1 (en) | Fine-grained object recognition in robotic systems | |
| CN106325485B (en) | A kind of gestures detection recognition methods and system | |
| Chen et al. | Is overfeat useful for image-based surface defect classification tasks? | |
| CN105044122A (en) | Copper part surface defect visual inspection system and inspection method based on semi-supervised learning model | |
| CN104198497A (en) | Surface defect detection method based on visual saliency map and support vector machine | |
| CN111524164B (en) | Target tracking method and device and electronic equipment | |
| CN103413149B (en) | Method for detecting and identifying static target in complicated background | |
| WO2013075295A1 (en) | Clothing identification method and system for low-resolution video | |
| US20140233841A1 (en) | Method of chekcing the appearance of the surface of a tyre | |
| Pratap et al. | Development of Ann based efficient fruit recognition technique | |
| CN106446870B (en) | A kind of human body contour outline feature extracting method and device | |
| Mishne et al. | Multi-channel wafer defect detection using diffusion maps | |
| Sanin et al. | K-tangent spaces on Riemannian manifolds for improved pedestrian detection | |
| CN109508701B (en) | Face recognition and tracking method | |
| Aydin et al. | A vision based inspection system using gaussian mixture model based interactive segmentation | |
| Asha et al. | Automatic detection of defects on periodically patterned textures | |
| Mohamed et al. | A simple evaluation of face detection algorithms using unpublished static images | |
| Sjarif et al. | Human action invarianceness for human action recognition | |
| MX2015012377A (en) | Automated processing and interpretation of mastographic images for level assignment in the bi-rads scale. | |
| Morozov et al. | Background subtraction using a convolutional neural network | |
| Carvajal-González et al. | Feature selection by relevance analysis for abandoned object classification | |
| CN110298210B (en) | Method and device for judging abnormity based on vision |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| S117 | Correction of errors in patents and applications (sect. 117/patents act 1977) |
Free format text: REQUEST FILED; REQUEST FOR CORRECTION UNDER SECTION 117 FILED ON 20 JULY 2023 |
|
| S117 | Correction of errors in patents and applications (sect. 117/patents act 1977) |
Free format text: CORRECTIONS ALLOWED; REQUEST FOR CORRECTION UNDER SECTION 117 FILED ON 20 JULY 2023 WAS ALLOWED ON 8 AUGUST 2023 |