Topic
Sketch recognition
About: Sketch recognition is a research topic. Over the lifetime, 1611 publications have been published within this topic receiving 40284 citations.
Papers published on a yearly basis
Papers
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20 Nov 2011TL;DR: A novel gesture spotting and recognition method is proposed, which combines the information of hand motion parameter, the matching result of HMM models and the recognition result based on geometrical features of hand trajectory to spot and recognize the gesture.
Abstract: Hand gesture recognition is receiving more and more attentions due to its potential use in many applications. In this
paper, we propose a novel gesture spotting and recognition method, which combines the information of hand motion
parameter, the matching result of HMM models and the recognition result based on geometrical features of hand
trajectory to spot and recognize the gesture. Besides, we also study the method of adjusting classifiers to make the
gesture recognition system adapt to specific users. Experimental results have proved the effectiveness of the proposed
method.
01 Jan 2012
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22 Jun 2016TL;DR: The proposed method and designed prototype for motion analysis and classification for human-computer interaction based on pattern recognition techniques of artificial vision without applying any markers or special sensors as well as utilizing low resolution cameras and simple hardware specifications.
Abstract: The recognition of human gestures in real time is still open problem due to low success rate of systems recently reported in scientific literature. This paper presents the proposed method and designed prototype for motion analysis and classification for human-computer interaction. The method is based on pattern recognition techniques of artificial vision without applying any markers or special sensors as well as utilizing low resolution cameras and simple hardware specifications. The proposed method provides interaction of user with computer via gestures in habitual and normal manner in order to activate system events (up, left and right) in real time. The proposed heuristic classifier recognizes specified gestures with an appropriate system context precision of 91.25 %. Comparing the obtained results with recent reports, the proposed approach provides satisfactory gesture recognition in real time with low resolution cameras.
01 Jan 2013
TL;DR: This thesis proposes a novel method for learning and pattern recognition which relies entirely on memory arranged in a custom hierarchical data structure which shifts the workload from the processor to memory.
Abstract: This thesis proposes a novel method for learning and pattern recognition. The algorithm presented
relies entirely on memory arranged in a custom hierarchical data structure which shifts
the workload from the processor to memory. The structure and functionality draw on biology
and neuroscience for inspiration while not losing sight of the inherent strengths and limitations
of modern computers. A hierarchy of learned nodes is built, stored, and used for recognition
without the need for complicated math or statistics. Recognition and prediction are inherent to
the hierarchy and require little additional computation, even for matching of partial patterns.
The experiments and results presented empirically demonstrate the robustness of memory-based
recognition of images.
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01 Sep 2014
TL;DR: A novel patch-based sparse representation (PSR) for describing sketch image is presented and it is evaluated under a sketch recognition framework and the effectiveness of the proposed method is demonstrated.
Abstract: Categorizing free-hand human sketches has profound implications in applications such as human computer interaction and image retrieval. The task is non-trivial due to the iconic nature of sketches, signified by large variances in both appearance and structure when compared with photographs. One of the most fundamental problems is how to effectively describe a sketch image. Many existing descriptors, such as histogram of oriented gradients (HOG) and shape context (SC), have achieved great success. Moreover, some works have attempted to design features specifically engineered for sketches, such as symmetric-aware flip invariant sketch histogram (SYM-FISH). We present a novel patch-based sparse representation (PSR) for describing sketch image and it is evaluated under a sketch recognition framework. Extensive experiments on a large scale human drawn sketch dataset demonstrate the effectiveness of the proposed method.