GB202403824D0 - RGB-D salient object detection method - Google Patents

RGB-D salient object detection method

Info

Publication number
GB202403824D0
GB202403824D0 GBGB2403824.2A GB202403824A GB202403824D0 GB 202403824 D0 GB202403824 D0 GB 202403824D0 GB 202403824 A GB202403824 A GB 202403824A GB 202403824 D0 GB202403824 D0 GB 202403824D0
Authority
GB
United Kingdom
Prior art keywords
rgb
detection method
object detection
salient object
salient
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
Application number
GBGB2403824.2A
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong University of Science and Technology
Original Assignee
Shandong University of Science and Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Shandong University of Science and Technology filed Critical Shandong University of Science and Technology
Publication of GB202403824D0 publication Critical patent/GB202403824D0/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • 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
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G06V10/765—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects using rules for classification or partitioning the feature space
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/80—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level
    • G06V10/806—Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level of extracted features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/07—Target detection
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • Multimedia (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computing Systems (AREA)
  • General Health & Medical Sciences (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Image Analysis (AREA)
GBGB2403824.2A 2023-04-25 2024-03-18 RGB-D salient object detection method Pending GB202403824D0 (en)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310450234.5A CN116206133B (en) 2023-04-25 2023-04-25 RGB-D significance target detection method

Publications (1)

Publication Number Publication Date
GB202403824D0 true GB202403824D0 (en) 2024-05-01

Family

ID=86513158

Family Applications (1)

Application Number Title Priority Date Filing Date
GBGB2403824.2A Pending GB202403824D0 (en) 2023-04-25 2024-03-18 RGB-D salient object detection method

Country Status (2)

Country Link
CN (1) CN116206133B (en)
GB (1) GB202403824D0 (en)

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116935052B (en) * 2023-07-24 2024-03-01 北京中科睿途科技有限公司 Semantic segmentation method and related equipment in intelligent cabin environment
CN117173394B (en) * 2023-08-07 2024-04-02 山东大学 Weak supervision salient object detection method and system for unmanned aerial vehicle video data
CN117036891B (en) * 2023-08-22 2024-03-29 睿尔曼智能科技（北京）有限公司 Cross-modal feature fusion-based image recognition method and system
CN117409214A (en) * 2023-12-14 2024-01-16 南开大学 Saliency target detection method and system based on self-adaptive interaction network

Family Cites Families (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120113133A1 (en) * 2010-11-04 2012-05-10 Shpigelblat Shai System, device, and method for multiplying multi-dimensional data arrays
US11734545B2 (en) * 2017-11-14 2023-08-22 Google Llc Highly efficient convolutional neural networks
CN110956094B (en) * 2019-11-09 2023-12-01 北京工业大学 RGB-D multi-mode fusion personnel detection method based on asymmetric double-flow network
CN111582316B (en) * 2020-04-10 2022-06-28 天津大学 RGB-D significance target detection method
CN112906770A (en) * 2021-02-04 2021-06-04 浙江师范大学 Cross-modal fusion-based deep clustering method and system
CN113763422B (en) * 2021-07-30 2023-10-03 北京交通大学 RGB-D image saliency target detection method
CN113486865B (en) * 2021-09-03 2022-03-11 国网江西省电力有限公司电力科学研究院 Power transmission line suspended foreign object target detection method based on deep learning
CN113935433A (en) * 2021-11-02 2022-01-14 齐齐哈尔大学 Hyperspectral image classification method based on depth spectrum space inverse residual error network
CN115410046A (en) * 2022-09-22 2022-11-29 河南科技大学 Skin disease tongue picture classification model based on deep learning, establishing method and application
CN115908789A (en) * 2022-12-09 2023-04-04 大连民族大学 Cross-modal feature fusion and asymptotic decoding saliency target detection method and device

Also Published As

Publication number Publication date
CN116206133B (en) 2023-09-05
CN116206133A (en) 2023-06-02

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