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Penny pincher: a blazing fast, highly accurate $-family recognizer
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-642-23771-3_9openalex-snapshot
Estimating the Perceived Difficulty of Pen Gestures
inferredcites →https://doi.org/10.1007/978-3-642-37149-3_27openalex-snapshot
A Fast Pen Gesture Matching Method Based on Nonlinear Embedding
inferredcites →https://doi.org/10.1037//1076-898x.6.4.322openalex-snapshot
Milliseconds matter: An introduction to microstrategies and to their use in describing and predicting interactive behavior.
inferredcites →https://doi.org/10.1109/cvpr.2012.6248036openalex-snapshot
A fast nearest neighbor search algorithm by nonlinear embedding
inferredcites →https://doi.org/10.1109/icfhr.2010.37openalex-snapshot
Personalizable Pen-Based Interface Using Lifelong Learning
inferredcites →https://doi.org/10.1145/1294211.1294238openalex-snapshot
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
inferredcites →https://doi.org/10.63317/27g7q8ydf7gbopenalex-snapshot
The UJIpenchars Database: a Pen-Based Database of Isolated Handwritten Characters
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-20518-8_41openalex-snapshot
Ensemble of Attractor Networks for 2D Gesture Retrieval
inferred← citeshttps://doi.org/10.1007/978-3-030-33720-9_2openalex-snapshot
DeepGRU: Deep Gesture Recognition Utility
inferred← citeshttps://doi.org/10.1016/j.cag.2015.10.011openalex-snapshot
Streamlined and accurate gesture recognition with Penny Pincher
inferred← citeshttps://doi.org/10.1016/j.cag.2015.12.002openalex-snapshot
Foreword to special section on Graphics Interface 2015
inferred← citeshttps://doi.org/10.1016/j.cag.2017.08.016openalex-snapshot
Sketch recognition with few examples
inferred← citeshttps://doi.org/10.1016/j.ijhcs.2020.102466openalex-snapshot
Omnis Prædictio: Estimating the full spectrum of human performance with stroke gestures
inferred← citeshttps://doi.org/10.1145/2856767.2856808openalex-snapshot
A $-Family Friendly Approach to Prototype Selection
inferred← citeshttps://doi.org/10.1145/3170427.3188452openalex-snapshot
Comparing Some Distances in Template-based 2D Gesture Recognition
inferred← citeshttps://doi.org/10.1145/3242969.3243032openalex-snapshot
!FTL, an Articulation-Invariant Stroke Gesture Recognizer with Controllable Position, Scale, and Rotation Invariances
inferred← citeshttps://doi.org/10.1145/3290605.3300445openalex-snapshot
Stroke-Gesture Input for People with Motor Impairments
inferred← citeshttps://doi.org/10.1145/3313831.3376417openalex-snapshot
Moving Toward an Ecologically Valid Data Collection Protocol for 2D Gestures In Video Games
inferred← citeshttps://doi.org/10.1145/3397481.3450675openalex-snapshot
DeepNAG: Deep Non-Adversarial Gesture Generation
inferred← citeshttps://doi.org/10.18293/dmsviva2019openalex-snapshot
Proceedings of the 25th International Conference on Distributed Multimedia Systems
inferred← citeshttps://doi.org/10.18293/dmsviva2019-013openalex-snapshot
Vector-based, Structure Preserving Stroke Gesture Recognition (P)
inferred← citeshttps://doi.org/10.18517/ijaseit.10.2.10807openalex-snapshot
Algorithmic Efficiency of Stroke Gesture Recognizers: a Comparative Analysis
inferred← citeshttps://doi.org/10.5555/3141475.3141497openalex-snapshot
Multiwave: Complex Hand Gesture Recognition Using the Doppler Effect
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