Cheng et al., 2023 - Google Patents
A survey on image semantic segmentation using deep learning techniquesCheng et al., 2023
View PDF- Document ID
- 6706226248104748348
- Author
- Cheng J
- Li H
- Li D
- Hua S
- Sheng V
- Publication year
External Links
Snippet
Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer …
- 230000011218 segmentation 0 title abstract description 24
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
- G06K9/62—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
- G06K9/36—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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- G06K9/62—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
- 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
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
-
- 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
- G06K9/20—Image acquisition
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems | |
| Li et al. | Deep learning-based object detection techniques for remote sensing images: A survey | |
| Cheng et al. | A survey on image semantic segmentation using deep learning techniques | |
| Xing et al. | An Encoder‐Decoder Network Based FCN Architecture for Semantic Segmentation | |
| Wang et al. | Hybrid CNN-transformer features for visual place recognition | |
| Liu et al. | Cascade saccade machine learning network with hierarchical classes for traffic sign detection | |
| CN110458844A (en) | A Semantic Segmentation Method for Low Light Scenes | |
| Liu et al. | Survey of road extraction methods in remote sensing images based on deep learning | |
| CN116485860A (en) | Monocular depth prediction algorithm based on multi-scale progressive interaction and aggregation cross attention features | |
| Liu et al. | A review of deep Learning-Based methods for road extraction from High-Resolution remote sensing images | |
| CN115775316A (en) | Image semantic segmentation method based on multi-scale attention mechanism | |
| Wang et al. | TF-SOD: A novel transformer framework for salient object detection | |
| Yu et al. | Long-range correlation supervision for land-cover classification from remote sensing images | |
| Ji et al. | Domain adaptive and interactive differential attention network for remote sensing image change detection | |
| Ma et al. | Capsule-based object tracking with natural language specification | |
| Chuang et al. | Deep learning‐based panoptic segmentation: Recent advances and perspectives | |
| Zhu et al. | Changevit: Unleashing plain vision transformers for change detection | |
| Li et al. | DSPCANet: Dual-channel scale-aware segmentation network with position and channel attentions for high-resolution aerial images | |
| Du et al. | SRH-Net: Stacked recurrent hourglass network for stereo matching | |
| CN116051752A (en) | Binocular stereo matching algorithm based on multi-scale feature fusion cavity convolution ResNet | |
| CN117392676A (en) | Street view image semantic segmentation method based on improved U, net network | |
| Feng et al. | FTransDeepLab: Multimodal Fusion Transformer-Based DeepLabv3+ for Remote Sensing Semantic Segmentation | |
| Chen et al. | Hi-ResNet: Edge detail enhancement for high-resolution remote sensing segmentation | |
| Zhao et al. | RFE-LinkNet: LinkNet with receptive field enhancement for road extraction from high spatial resolution imagery | |
| Zheng et al. | DCU-NET: Self-supervised monocular depth estimation based on densely connected U-shaped convolutional neural networks |