Chanda, 2024 - Google Patents
Improving Static Hand Gesture Recognition with Semantic Segmentation and Fine-Tuned Convolutional Neural NetworkChanda, 2024
- Document ID
- 9581213625443511283
- Author
- Chanda B
- Publication year
- Publication venue
- 2024 27th International Conference on Computer and Information Technology (ICCIT)
External Links
Snippet
The study aims to develop an effective static gesture recognition system for RGB datasets, enabling hand-based interaction between users and virtual environments. Using the U-Net architecture, ROI is first extracted from the RGB input data. Multilevel thresholding …
Classifications
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- 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
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- G—PHYSICS
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- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
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- 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
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- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
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- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- G—PHYSICS
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- G06K9/00335—Recognising movements or behaviour, e.g. recognition of gestures, dynamic facial expressions; Lip-reading
- G06K9/00355—Recognition of hand or arm movements, e.g. recognition of deaf sign language
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