[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

You et al., 2023 - Google Patents

Dynamic dense CRF inference for video segmentation and semantic SLAM

You et al., 2023

Document ID
15052313905541027037
Author
You M
Luo C
Zhou H
Zhu S
Publication year
Publication venue
Pattern Recognition

External Links

Snippet

The dense conditional random field (dense CRF) is an effective post-processing tool for image/video segmentation and semantic SLAM. In this paper, we extend the traditional dense CRF inference algorithm to incremental sensor data modelling. The algorithm …
Continue reading at www.sciencedirect.com (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
    • G06K9/62—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
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20112—Image segmentation details
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • 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
    • G06N5/00—Computer systems utilising knowledge based models
    • 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

Similar Documents

Publication Publication Date Title
Sahu et al. Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Von Stumberg et al. Gn-net: The gauss-newton loss for multi-weather relocalization
Kestur et al. UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle
Wang et al. Towards unified depth and semantic prediction from a single image
Ochs et al. Sdnet: Semantically guided depth estimation network
Liu et al. Multi-objective convolutional learning for face labeling
Zhang et al. Long-range terrain perception using convolutional neural networks
Lin et al. Deep global-relative networks for end-to-end 6-dof visual localization and odometry
You et al. Dynamic dense CRF inference for video segmentation and semantic SLAM
Yang et al. Real-time 3D scene layout from a single image using convolutional neural networks
Haering et al. Visual event detection
Wang et al. Face mask extraction in video sequence
Grigorev et al. Depth estimation from single monocular images using deep hybrid network
Pavel et al. Object class segmentation of RGB-D video using recurrent convolutional neural networks
Güçlü et al. End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
De Bem et al. Deep fully-connected part-based models for human pose estimation
Szemenyei et al. Real-time scene understanding using deep neural networks for RoboCup SPL
Rowghanian et al. Object tracking by mean shift and radial basis function neural networks
Chan et al. Unseen salient object discovery for monocular robot vision
Osuna-Coutiño et al. Structure extraction in urbanized aerial images from a single view using a CNN-based approach
Kolbeinsson et al. UCorr: Wire Detection and Depth Estimation for Autonomous Drones
Meng et al. Un-VDNet: unsupervised network for visual odometry and depth estimation
de Jesús Osuna-Coutiño et al. Volumetric structure extraction in a single image
Zhao et al. Knowledge graphs meet geometry for semi-supervised monocular depth estimation
Maia et al. Visual object tracking by an evolutionary self-organizing neural network