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Sketch recognition

About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.


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Journal ArticleDOI
TL;DR: This work proposes a method to enable natural video interactions through hand gesture recognition between users and a video meeting system by combining a signal similarity study with a data-mining tool for dynamic gesture recognition.
Abstract: In the context of immersive communications that aim to enable natural experiences and interactions among people, objects, and the environment, we propose a method to enable natural video interactions through hand gesture recognition between users and a video meeting system. An end-to-end study was performed: we started with the development of specific gesture recognition algorithms and concluded with a user evaluation to validate our results. Gestures and their associated functionalities were identified via a user survey which focused on distinguishing two concepts which are often confused: hand posture and hand gesture (i.e., static versus dynamic). Our recognition process was composed of two main tasks: hand posture recognition (i.e., skin segmentation, background subtraction, region combination, feature extraction, and classification) and hand gesture recognition (tracking and recognition). Our approach combined a signal similarity study with a data-mining tool for dynamic gesture recognition. We focused on the experimentation and user evaluation to improve our approach, taking into account user feedback and analyzing performance in different environments and for different users.

8 citations

Proceedings ArticleDOI
29 Oct 2012
TL;DR: Sketch2Tag is a general sketch recognition system, towards recognizing any semantically meaningful object that a child can recognize, and a web-scale clipart image collection is leveraged as the knowledge base of the recognition system.
Abstract: In this work, we introduce the Sketch2Tag system for hand-drawn sketch recognition. Due to large variations presented in hand-drawn sketches, most of existing work was limited to a particular domain or limited predefined classes. Different from existing work, Sketch2Tag is a general sketch recognition system, towards recognizing any semantically meaningful object that a child can recognize. This system enables a user to draw a sketch on the query panel, and then provides real-time recognition results. To increase the recognition coverage, a web-scale clipart image collection is leveraged as the knowledge base of the recognition system. Better understanding a user's drawing will be of great value to a variety of applications, such as, improving the sketch-based image search by combining the recognition results as textual queries.

8 citations

Journal ArticleDOI
30 May 2016
TL;DR: This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition in HCI and to identify future directions on this topic, and focuses on different application domains that use hand gestures for efficient interaction.
Abstract: Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified in two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human–computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.

8 citations

Proceedings ArticleDOI
30 Aug 1992
TL;DR: This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition.
Abstract: Handwritten character recognition is typically classified as online or offline depending on the nature of the input data. Online data consists of a temporal sequence of instrument positions while offline data is in the form of a 2D image of the writing sample. Online recognition techniques have been relatively successful but have the disadvantage of requiring the data to be gathered during the writing process. This paper presents work on the extraction of temporal information from static images of handwriting and its implications for character recognition. >

8 citations

01 Jan 1997
TL;DR: A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime.
Abstract: A technique is presented which combines rule-based and neural network pattern recognition methods in an integrated system in order to perform learning and recognition of hand-written characters and gestures in realtime. The GesRec system is introduced which provides a framework for data acquisition, training, recognition, and gesture-to-speech transcription in a Windows environment. A recognition accuracy of 92.5% was obtained for the hybrid system, compared to 89.6% for the neural network only and 82.7% for rules only. Training and recognition times are given for an able-bodied and a disabled user.

8 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202326
202271
202130
202029
201946
201827