Xu et al., 2024 - Google Patents
Sonar image segmentation using a multi-spatial information constraint fuzzy C-means clustering algorithm based on KL divergenceXu et al., 2024
- Document ID
- 12809714523341908690
- Author
- Xu H
- Li Y
- Zhang M
- Tong P
- Publication year
- Publication venue
- International Journal of Machine Learning and Cybernetics
External Links
Snippet
Sonar image segmentation is an important task in the field of underwater detection, and the realization of accurate segmentation of targets and shadows is the key to subsequent image processing. However, due to the influence of various marine environments, the formation of …
- 238000004422 calculation algorithm 0 title abstract description 96
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- 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
- 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
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting 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
-
- 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
- G06T2207/20048—Transform domain processing
-
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
- G06K9/4609—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections by matching or filtering
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- 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
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Brain tumor segmentation based on hybrid clustering and morphological operations | |
Miao et al. | Local segmentation of images using an improved fuzzy C-means clustering algorithm based on self-adaptive dictionary learning | |
Sharma et al. | A review on image segmentation with its clustering techniques | |
US5933524A (en) | Method for segmentation of digital color images | |
CN108154519A (en) | Dividing method, device and the storage medium of eye fundus image medium vessels | |
Zhang et al. | Geometric loss for deep multiple sclerosis lesion segmentation | |
Babu et al. | Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms | |
Song et al. | Brain tissue segmentation and bias field correction of MR image based on spatially coherent FCM with nonlocal constraints | |
Zhang et al. | Multi-task dictionary learning based convolutional neural network for computer aided diagnosis with longitudinal images | |
Annavarapu et al. | An adaptive watershed segmentation based medical image denoising using deep convolutional neural networks | |
Wu et al. | Full-parameter adaptive fuzzy clustering for noise image segmentation based on non-local and local spatial information | |
Lohit et al. | Modified total Bregman divergence driven picture fuzzy clustering with local information for brain MRI image segmentation | |
CN117196963A (en) | Point cloud denoising method based on noise reduction self-encoder | |
Bandyopadhyay et al. | A hybrid fuzzy filtering-fuzzy thresholding technique for region of interest detection in noisy images | |
CN115205308A (en) | Fundus image blood vessel segmentation method based on linear filtering and deep learning | |
Sivanandan et al. | A new CNN architecture for efficient classification of ultrasound breast tumor images with activation map clustering based prediction validation | |
CN114240990A (en) | SAR image point target segmentation method | |
CN113962968A (en) | Multi-source mixed interference radar image target detection system oriented to complex electromagnetic environment | |
Cheng et al. | Multi-attention mechanism medical image segmentation combined with word embedding technology | |
CN113781465A (en) | Grad-CAM-based medical image segmentation model visualization method | |
Li et al. | A level set image segmentation method based on a cloud model as the priori contour | |
Xu et al. | Sonar image segmentation using a multi-spatial information constraint fuzzy C-means clustering algorithm based on KL divergence | |
Xu et al. | Filtering level-set model based on saliency and gradient information for sonar image segmentation | |
Narasimha et al. | Integrating Taylor–Krill herd‐based SVM to fuzzy‐based adaptive filter for medical image denoising | |
Wang et al. | Sonar Objective Detection Based on Dilated Separable Densely Connected CNNs and Quantum‐Behaved PSO Algorithm |