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

Liu et al., 2023 - Google Patents

Attention-embedding mesh saliency

Liu et al., 2023

Document ID
15499713198708115785
Author
Liu C
Luan W
Fu R
Pang H
Li Y
Publication year
Publication venue
The Visual Computer

External Links

Snippet

Recently, the learning method is gradually penetrating into the field of 3D saliency, but the ground truth annotation is too insufficient to directly train a 3D saliency network. Here, we propose a novel attention-embedding strategy for 3D saliency estimation by directly …
Continue reading at link.springer.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
    • 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
    • G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
    • G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
    • G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
    • 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
    • G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
    • 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
    • G06K9/36—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
    • G06K9/4671—Extracting features based on salient regional features, e.g. Scale Invariant Feature Transform [SIFT] keypoints
    • 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
    • G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • 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
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/50—Computer-aided design
    • 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
    • G06T7/00—Image analysis
    • 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
    • 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
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

Similar Documents

Publication Publication Date Title
Zou et al. Manhattan Room Layout Reconstruction from a Single 360^ ∘ 360∘ Image: A Comparative Study of State-of-the-Art Methods
Shao et al. An interactive approach to semantic modeling of indoor scenes with an rgbd camera
Fooladgar et al. A survey on indoor RGB-D semantic segmentation: from hand-crafted features to deep convolutional neural networks
Bu et al. 3D shape recognition and retrieval based on multi-modality deep learning
Wen et al. CF-SIS: Semantic-instance segmentation of 3D point clouds by context fusion with self-attention
Wang et al. Accurate fine-grained object recognition with structure-driven relation graph networks
CN108985298B (en) Human body clothing segmentation method based on semantic consistency
Ning et al. Conditional generative adversarial networks based on the principle of homologycontinuity for face aging
Zhou et al. Discriminative attention-augmented feature learning for facial expression recognition in the wild
Huang et al. Segment3d: Learning fine-grained class-agnostic 3d segmentation without manual labels
Liu et al. Attention-embedding mesh saliency
Gao et al. Occluded person re-identification based on feature fusion and sparse reconstruction
Chen et al. A simplified ICA-based local similarity stereo matching
Khoreva et al. Improved image boundaries for better video segmentation
Qi et al. Saliency detection via joint modeling global shape and local consistency
Ma et al. A novel 3D shape recognition method based on double-channel attention residual network
Sun et al. RGB-T saliency detection via robust graph learning and collaborative manifold ranking
Ullah et al. DSFMA: Deeply supervised fully convolutional neural networks based on multi-level aggregation for saliency detection
Mei et al. Unsupervised Point Cloud Representation Learning by Clustering and Neural Rendering
Jiang et al. Modeling with prejudice: Small-sample learning via adversary for semantic segmentation
Li et al. A review of advances in image inpainting research
Xia et al. Semantic features and high-order physical features fusion for action recognition
Lyu et al. Rigid pairwise 3D point cloud registration: a survey
Jian et al. PointMTL: Multi-transform learning for effective 3D point cloud representations
Zeng et al. Deep Stereo Network with MRF-based Cost Aggregation