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

Aladem et al., 2019 - Google Patents

A comparative study of different cnn encoders for monocular depth prediction

Aladem et al., 2019

View PDF
Document ID
5386924612228785069
Author
Aladem M
Chennupati S
El-Shair Z
Rawashdeh S
Publication year
Publication venue
2019 IEEE National Aerospace and Electronics Conference (NAECON)

External Links

Snippet

Depth estimation of an observed scene is an important task for many domains such as mobile robotics, autonomous driving, and augmented reality. Traditionally, specialized sensors such as stereo cameras and structured light (RGB-D) ones are used to obtain depth …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10016Video; Image sequence
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20112Image segmentation details
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00221Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
    • G06K9/00268Feature extraction; Face representation
    • G06K9/00281Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/0002Inspection of images, e.g. flaw detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/40Analysis of texture
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models

Similar Documents

Publication Publication Date Title
Liu et al. Adaptive learning attention network for underwater image enhancement
Xu et al. CoBEVT: Cooperative bird's eye view semantic segmentation with sparse transformers
Alonso et al. 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation
Li et al. Undeepvo: Monocular visual odometry through unsupervised deep learning
Guo et al. Learning monocular depth by distilling cross-domain stereo networks
Höft et al. Fast semantic segmentation of RGB-D scenes with GPU-accelerated deep neural networks
US20210049371A1 (en) Localisation, mapping and network training
DE112019005671T5 (en) DETERMINING ASSOCIATIONS BETWEEN OBJECTS AND PERSONS USING MACHINE LEARNING MODELS
US20230080133A1 (en) 6d pose and shape estimation method
Qu et al. Depth completion via deep basis fitting
Erçelik et al. 3d object detection with a self-supervised lidar scene flow backbone
Zeng et al. Enabling efficient deep convolutional neural network-based sensor fusion for autonomous driving
Medina et al. Comparison of CNN and MLP classifiers for algae detection in underwater pipelines
Liu et al. Using unsupervised deep learning technique for monocular visual odometry
CN115661246A (en) A Pose Estimation Method Based on Self-Supervised Learning
Ubina et al. Intelligent underwater stereo camera design for fish metric estimation using reliable object matching
Lee et al. Learning residual flow as dynamic motion from stereo videos
Chen et al. Lenfusion: a joint low-light enhancement and fusion network for nighttime infrared and visible image fusion
Lu et al. Multi-task learning for single image depth estimation and segmentation based on unsupervised network
Burkov et al. Multi-neus: 3d head portraits from single image with neural implicit functions
Aladem et al. A comparative study of different cnn encoders for monocular depth prediction
Zhang et al. Self-supervised monocular depth estimation with self-perceptual anomaly handling
Chanduri et al. Camlessmonodepth: Monocular depth estimation with unknown camera parameters
Hui et al. WSA-YOLO: Weak-supervised and adaptive object detection in the low-light environment for YOLOV7
Gao et al. CI-Net: Contextual information for joint semantic segmentation and depth estimation