Hosang et al., 2017 - Google Patents
Learning non-maximum suppressionHosang et al., 2017
View PDF- Document ID
- 74136061828806591
- Author
- Hosang J
- Benenson R
- Schiele B
- Publication year
- Publication venue
- Proceedings of the IEEE conference on computer vision and pattern recognition
External Links
Snippet
Object detectors have hugely profited from moving towards an end-to-end learning paradigm: proposals, fea tures, and the classifier becoming one neural network improved results two-fold on general object detection. One indispensable component is non-maximum …
- 230000001629 suppression 0 title abstract description 21
Classifications
-
- 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
- G06K9/627—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches based on distances between the pattern to be recognised and training or reference patterns
-
- 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
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
- G06K9/6203—Shifting or otherwise transforming the patterns to accommodate for positional errors
-
- 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
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/6261—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation partitioning the feature space
-
- 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
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
- G06K9/6292—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion of classification results, e.g. of classification results related to same input data
-
- 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
- G06K9/6296—Graphical models, e.g. Bayesian networks
-
- 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
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hosang et al. | Learning non-maximum suppression | |
Fang et al. | Efficient learning interpretable shapelets for accurate time series classification | |
Tang et al. | Learning to compose dynamic tree structures for visual contexts | |
Stewart et al. | End-to-end people detection in crowded scenes | |
US11715281B2 (en) | System and method for detecting objects in a digital image, and system and method for rescoring object detections | |
Saito et al. | Asymmetric tri-training for unsupervised domain adaptation | |
Zhang et al. | Improving crowdsourced label quality using noise correction | |
McCann et al. | Local naive bayes nearest neighbor for image classification | |
JP3088171B2 (en) | Self-organizing pattern classification system and classification method | |
Samuel et al. | From generalized zero-shot learning to long-tail with class descriptors | |
US8885943B2 (en) | Face detection method and apparatus | |
US20200143209A1 (en) | Task dependent adaptive metric for classifying pieces of data | |
CN109711473A (en) | Item identification method, equipment and system | |
Sitawarin et al. | Minimum-norm adversarial examples on KNN and KNN based models | |
Lu et al. | Pixel-wise spatial pyramid-based hybrid tracking | |
Sinha et al. | An improved deep learning approach for product recognition on racks in retail stores | |
US20250104187A1 (en) | Method and System For Accelerating Rapid Class Augmentation for Object Detection in Deep Neural Networks | |
Lee et al. | Reinforced adaboost learning for object detection with local pattern representations | |
Loeff et al. | Efficient unsupervised learning for localization and detection in object categories | |
Kauschke et al. | Towards neural network patching: Evaluating engagement-layers and patch-architectures | |
Company-Corcoles et al. | Towards robust loop closure detection in weakly textured environments using points and lines | |
Xu et al. | Handwritten Bangla digit recognition using hierarchical Bayesian network | |
Ohashi et al. | Unbiased scene graph generation using predicate similarities | |
Sasao et al. | A design method for multiclass classifiers | |
Verikas et al. | Selecting neural networks for a committee decision |