Jain et al., 2021 - Google Patents
Ensembled neural network for static hand gesture recognitionJain et al., 2021
- Document ID
- 17855120866599757314
- Author
- Jain A
- Sethi A
- Vishwakarma D
- Jain A
- Publication year
- Publication venue
- 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)
External Links
Snippet
Sign language was originally created to fulfil the gap of communication between the speech impaired. However, with recent advances, we can see the applications of sign language in a variety of different fields such as automated vehicle movements, assistant systems, human …
- 230000001537 neural 0 title description 11
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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
-
- 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
- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
-
- 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
- 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
- G06K9/00362—Recognising human body or animal bodies, e.g. vehicle occupant, pedestrian; Recognising body parts, e.g. hand
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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 |
|---|---|---|
| Mohandes et al. | Image-based and sensor-based approaches to Arabic sign language recognition | |
| Rioux-Maldague et al. | Sign language fingerspelling classification from depth and color images using a deep belief network | |
| Agrawal et al. | Recognition of Indian Sign Language using feature fusion | |
| Daroya et al. | Alphabet sign language image classification using deep learning | |
| Chen et al. | Efficient spatial temporal convolutional features for audiovisual continuous affect recognition | |
| Atkar et al. | Speech emotion recognition using dialogue emotion decoder and cnn classifier | |
| Angona et al. | Automated Bangla sign language translation system for alphabets by means of MobileNet | |
| Patel et al. | Hand gesture recognition system using convolutional neural networks | |
| Agha et al. | A comprehensive study on sign languages recognition systems using (SVM, KNN, CNN and ANN) | |
| Silanon | Thai Finger‐Spelling Recognition Using a Cascaded Classifier Based on Histogram of Orientation Gradient Features | |
| Aly et al. | Arabic sign language recognition using spatio-temporal local binary patterns and support vector machine | |
| Shetty et al. | Real-time translation of sign language for speech impaired | |
| Kumaragurubaran et al. | Unlocking Sign Language Communication: A Deep Learning Paradigm for Overcoming Accessibility Challenges | |
| Jaiswal et al. | An efficient binarized neural network for recognizing two hands indian sign language gestures in real-time environment | |
| Jain et al. | Ensembled neural network for static hand gesture recognition | |
| Tolba et al. | A proposed graph matching technique for Arabic sign language continuous sentences recognition | |
| Srininvas et al. | A framework to recognize the sign language system for deaf and dumb using mining techniques | |
| Praneel et al. | Malayalam sign language character recognition system | |
| Al-Obaidi et al. | Interpreting arabic sign alphabet by using the deep learning | |
| Pariselvam | An interaction system using speech and gesture based on cnn | |
| Kakade et al. | Enhancing Sign Language Interpretation with Multiheaded CNN, Hand Landmarks and Large Language Model (LLM) | |
| Nouisser et al. | Deep learning based mobilenet and multi-head attention model for facial expression recognition. | |
| Bhavsar et al. | Performance comparison of svm, cnn, hmm and neuro-fuzzy approach for indian sign language recognition | |
| Jadhav et al. | GoogLeNet application towards gesture recognition for ASL character identification | |
| Ma et al. | Dynamic sign language recognition based on improved residual-lstm network |