CN113870286A - 一种基于多级特征和掩码融合的前景分割方法 - Google Patents
一种基于多级特征和掩码融合的前景分割方法 Download PDFInfo
- Publication number
- CN113870286A CN113870286A CN202111162124.6A CN202111162124A CN113870286A CN 113870286 A CN113870286 A CN 113870286A CN 202111162124 A CN202111162124 A CN 202111162124A CN 113870286 A CN113870286 A CN 113870286A
- Authority
- CN
- China
- Prior art keywords
- fusion
- feature
- level
- features
- mask
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000011218 segmentation Effects 0.000 title claims abstract description 107
- 230000004927 fusion Effects 0.000 title claims abstract description 106
- 238000000034 method Methods 0.000 title claims abstract description 56
- 238000012545 processing Methods 0.000 claims abstract description 8
- 238000012549 training Methods 0.000 claims description 33
- 208000037170 Delayed Emergence from Anesthesia Diseases 0.000 claims description 14
- 238000005070 sampling Methods 0.000 claims description 11
- 238000011176 pooling Methods 0.000 claims description 8
- 238000010606 normalization Methods 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 9
- 238000000605 extraction Methods 0.000 abstract description 9
- 238000002474 experimental method Methods 0.000 description 10
- 238000013135 deep learning Methods 0.000 description 8
- 230000008569 process Effects 0.000 description 8
- 238000012360 testing method Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 101100269850 Caenorhabditis elegans mask-1 gene Proteins 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/048—Activation functions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Biology (AREA)
- Image Analysis (AREA)
Abstract
Description
Claims (10)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111162124.6A CN113870286A (zh) | 2021-09-30 | 2021-09-30 | 一种基于多级特征和掩码融合的前景分割方法 |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202111162124.6A CN113870286A (zh) | 2021-09-30 | 2021-09-30 | 一种基于多级特征和掩码融合的前景分割方法 |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113870286A true CN113870286A (zh) | 2021-12-31 |
Family
ID=79001419
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202111162124.6A Pending CN113870286A (zh) | 2021-09-30 | 2021-09-30 | 一种基于多级特征和掩码融合的前景分割方法 |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113870286A (zh) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114387523A (zh) * | 2022-03-23 | 2022-04-22 | 成都理工大学 | 基于dcnn边界引导的遥感图像建筑物提取方法 |
CN114821068A (zh) * | 2022-05-25 | 2022-07-29 | 北京地平线机器人技术研发有限公司 | 全景分割和深度确定的处理方法、装置、设备和介质 |
CN117152441A (zh) * | 2023-10-19 | 2023-12-01 | 中国科学院空间应用工程与技术中心 | 一种基于跨尺度解码的生物图像实例分割方法 |
CN118015287A (zh) * | 2024-04-09 | 2024-05-10 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | 一种基于域纠正适应器的跨域小样本分割方法 |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108805889A (zh) * | 2018-05-07 | 2018-11-13 | 中国科学院自动化研究所 | 边缘引导的精细化显著性物体分割方法与系统、设备 |
CN112365496A (zh) * | 2020-12-02 | 2021-02-12 | 中北大学 | 基于深度学习和多引导的多模态mr影像脑肿瘤分割方法 |
CN112507777A (zh) * | 2020-10-10 | 2021-03-16 | 厦门大学 | 一种基于深度学习的光学遥感图像舰船检测与分割方法 |
CN112906706A (zh) * | 2021-03-31 | 2021-06-04 | 西南科技大学 | 一种改进的基于编解码器的图像语义分割方法 |
-
2021
- 2021-09-30 CN CN202111162124.6A patent/CN113870286A/zh active Pending
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108805889A (zh) * | 2018-05-07 | 2018-11-13 | 中国科学院自动化研究所 | 边缘引导的精细化显著性物体分割方法与系统、设备 |
CN112507777A (zh) * | 2020-10-10 | 2021-03-16 | 厦门大学 | 一种基于深度学习的光学遥感图像舰船检测与分割方法 |
CN112365496A (zh) * | 2020-12-02 | 2021-02-12 | 中北大学 | 基于深度学习和多引导的多模态mr影像脑肿瘤分割方法 |
CN112906706A (zh) * | 2021-03-31 | 2021-06-04 | 西南科技大学 | 一种改进的基于编解码器的图像语义分割方法 |
Non-Patent Citations (3)
Title |
---|
HENGSHUANG ZHAO ET AL.: "ICNet for Real-Time Semantic Segmentation on High-Resolution Images", 《ARXIV:1704.08545[CS.CV]》, 20 August 2018 (2018-08-20) * |
WEI WANG ET AL.: "An Improved Boundary-Aware U-Net for Ore Image Semantic Segmentation", 《SENSORS》, vol. 21, no. 8, 8 April 2021 (2021-04-08) * |
李志远 等: "基于深度学习的视频图像实时背景替换方法", 《小型微型计算机系统》, vol. 42, no. 12, 23 September 2021 (2021-09-23) * |
Cited By (5)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114387523A (zh) * | 2022-03-23 | 2022-04-22 | 成都理工大学 | 基于dcnn边界引导的遥感图像建筑物提取方法 |
CN114821068A (zh) * | 2022-05-25 | 2022-07-29 | 北京地平线机器人技术研发有限公司 | 全景分割和深度确定的处理方法、装置、设备和介质 |
CN117152441A (zh) * | 2023-10-19 | 2023-12-01 | 中国科学院空间应用工程与技术中心 | 一种基于跨尺度解码的生物图像实例分割方法 |
CN117152441B (zh) * | 2023-10-19 | 2024-05-07 | 中国科学院空间应用工程与技术中心 | 一种基于跨尺度解码的生物图像实例分割方法 |
CN118015287A (zh) * | 2024-04-09 | 2024-05-10 | 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) | 一种基于域纠正适应器的跨域小样本分割方法 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111325751B (zh) | 基于注意力卷积神经网络的ct图像分割系统 | |
CN109840556B (zh) | 一种基于孪生网络的图像分类识别方法 | |
CN114120102A (zh) | 边界优化的遥感图像语义分割方法、装置、设备及介质 | |
CN113642390B (zh) | 一种基于局部注意力网络的街景图像语义分割方法 | |
CN113870286A (zh) | 一种基于多级特征和掩码融合的前景分割方法 | |
CN112651940B (zh) | 基于双编码器生成式对抗网络的协同视觉显著性检测方法 | |
CN115619743A (zh) | Oled新型显示器件表面缺陷检测模型的构建方法及其应用 | |
CN112257766A (zh) | 一种基于频域滤波处理的自然场景下阴影识别检测方法 | |
CN112784756B (zh) | 人体识别跟踪方法 | |
CN111914950B (zh) | 基于深度对偶变分哈希的无监督跨模态检索模型训练方法 | |
CN112215100B (zh) | 一种不平衡训练样本下针对退化图像的目标检测方法 | |
CN111612789A (zh) | 一种基于改进的U-net网络的缺陷检测方法 | |
CN114332133A (zh) | 基于改进CE-Net的新冠肺炎CT图像感染区分割方法及系统 | |
CN111833282B (zh) | 一种基于改进的DDcGAN模型的图像融合方法 | |
CN109766918A (zh) | 基于多层次上下文信息融合的显著性物体检测方法 | |
TWI803243B (zh) | 圖像擴增方法、電腦設備及儲存介質 | |
Zhao et al. | Detecting deepfake video by learning two-level features with two-stream convolutional neural network | |
CN116612283A (zh) | 一种基于大卷积核骨干网络的图像语义分割方法 | |
CN111723852A (zh) | 针对目标检测网络的鲁棒训练方法 | |
CN114155165A (zh) | 一种基于半监督的图像去雾方法 | |
Ding et al. | Rethinking click embedding for deep interactive image segmentation | |
CN111612803B (zh) | 一种基于图像清晰度的车辆图像语义分割方法 | |
CN111209886B (zh) | 一种基于深度神经网络的快速行人再识别方法 | |
CN112861911A (zh) | 一种基于深度特征选择融合的rgb-d语义分割方法 | |
CN116778346A (zh) | 一种基于改进自注意力机制的管线识别方法及系统 |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
CB03 | Change of inventor or designer information |
Inventor after: Li Gang Inventor after: Wang Ying Inventor after: Zheng Yu Inventor after: Gao Wenjian Inventor after: Liu Huan Inventor after: Xu Chuanyun Inventor after: Li Tenghui Inventor after: Zhang Yang Inventor after: Li Tian Inventor after: Song Zhiyao Inventor after: Zhang Qing Inventor after: Xu Hao Inventor before: Xu Chuanyun Inventor before: Wang Ying Inventor before: Zheng Yu Inventor before: Gao Wenjian Inventor before: Liu Huan Inventor before: Li Gang Inventor before: Li Tenghui Inventor before: Zhang Yang Inventor before: Li Tian Inventor before: Song Zhiyao Inventor before: Zhang Qing Inventor before: Xu Hao |
|
CB03 | Change of inventor or designer information |