Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

653
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
653

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

MonSter++: Unified Stereo Matching, Multi-View Stereo, and Real-Time Stereo With Monodepth Priors.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Dataset Pruning: Reducing Training Data by Examining SGD-Influence.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Spatial-Temporal Self-Compensating Graph Convolutional Network for Skeleton-Based Action Recognition Under Data Constraints.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

COMBINER: Composed Image Retrieval Guided by Attribute-Based Neighbor Relations.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Multimodal detection of microplastics in human kidney stones and multi-omics exploration of renal cell metaflammation.

Journal of hazardous materials·2026
Same author

Long&short Exposures Guided Diffusion Model for Realistic Local Motion Deblurring.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Hyperbolic Cycle Alignment for Infrared-Visible Image Fusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Learning Gaze Synthesizer via 3D-eye Controlled Diffusion and Cross-domain Feature Alignment.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Underlying Semantic Diffusion for Effective and Efficient In-Context Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

DiffRES: Unleashing Text-to-Image Diffusion Models for Generative Referring Expression Segmentation without Information Leakage.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Location Matters: Frequency-Spatial Dual Space Adaptation for Cross-Domain Few-Shot Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BayeTopo: Bayesian-based Topology-guided Learning for Vascular Imaging Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

对于弱监督的语义分割的空间结构约束.

Tao Chen, Yazhou Yao, Xingguo Huang

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |February 1, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究引入了空间结构约束 (SSC),以改善弱监督的语义细分. 该方法通过防止注意力扩展包括背景区域来完善对象本地化,提高准确性.

    更多相关视频

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    405
    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.8K

    相关实验视频

    Last Updated: Jul 4, 2025

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K
    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
    04:48

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    405
    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
    11:38

    Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

    Published on: August 23, 2017

    9.8K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 弱监督的语义细分依赖于易于获得的图像级标签.
    • 类激活地图 (CAM) 提供对象位置线索,但仅突出区分部分.
    • 现有的CAM扩张战略往往导致背景地区的过度活化.

    研究的目的:

    • 提出空间结构约束 (SSC),以减轻对语义细分的注意力扩展的过度激活.
    • 为了提高对象定位在弱监督的语义细分任务中的准确性.
    • 开发一种改进对象本地化的方法,而不依赖外部突出模型.

    主要方法:

    • 提出了一个CAM驱动的重建模块,通过保留图像空间结构来限制注意力扩散.
    • 引入了一个激活自我调节模块,以使用区域一致性来改进CAM,以获得更细致的细节.
    • 开发了一种新的方法,用于使用空间结构约束的弱监督语义分割.

    主要成果:

    • 在 PASCAL VOC 2012 上实现了 72.7% 的 mIoU.
    • 在COCO数据集上实现了47.0%的mIoU.
    • 在没有外部突出模型的情况下证明了卓越的性能.

    结论:

    • 空间结构的约束有效地减轻了注意力扩展中的过度激活.
    • 拟议的方法在弱监督的语义细分中提高了对象定位的准确性.
    • 该方法为语义细分提供了一个强大的解决方案,使用易于使用的图像级标签.