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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.


Papers
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Book ChapterDOI
21 Sep 2010
TL;DR: This paper presents a system that fuses and interprets the outputs of several computer vision components as well as speech recognition to obtain a high-level understanding of the perceived scene.
Abstract: Most approaches to the visual perception of humans do not include high-level activity recognitition. This paper presents a system that fuses and interprets the outputs of several computer vision components as well as speech recognition to obtain a high-level understanding of the perceived scene. Our laboratory for investigating new ways of human-machine interaction and teamwork support, is equipped with an assemblage of cameras, some close-talking microphones, and a videowall as main interaction device. Here, we develop state of the art real-time computer vision systems to track and identify users, and estimate their visual focus of attention and gesture activity. We also monitor the users' speech activity in real time. This paper explains our approach to highlevel activity recognition based on these perceptual components and a temporal logic engine.

29 citations

Proceedings Article
05 Jul 2010
TL;DR: A real-time sketch recognition interface that recognizes 485 freely-drawn military course-of-action symbols that achieves an accuracy of 90% when considering the top 3 interpretations and requiring every aspect of the shape to be correct.
Abstract: Military course-of-action (COA) diagrams are used to depict battle scenarios and include thousands of unique symbols, complete with additional textual and designator modifiers. We have created a real-time sketch recognition interface that recognizes 485 freely-drawn military course-of-action symbols. When the variations (not allowable by other systems) are factored in, our system is several orders of magnitude larger than the next biggest system. On 5,900 hand-drawn symbols, the system achieves an accuracy of 90% when considering the top 3 interpretations and requiring every aspect of the shape (variations, text, symbol, location, orientation) to be correct.

29 citations

Proceedings ArticleDOI
03 Mar 2013
TL;DR: A gesture recognition algorithm, based on dynamic time warping, was implemented with a use-case scenario of natural interaction with a mobile robot and the experimental results show that the proposed modifications of the standard gesture recognition algorithms improve the robustness of the recognition.
Abstract: To achieve an improved human-robot interaction it is necessary to allow the human participant to interact with the robot in a natural way. In this work, a gesture recognition algorithm, based on dynamic time warping, was implemented with a use-case scenario of natural interaction with a mobile robot. Inputs are gesture trajectories obtained using a Microsoft Kinect sensor. Trajectories are stored in the person's frame of reference. Furthermore, the recognition is position-invariant, meaning that only one learned sample is needed to recognize the same gesture performed at another position in the gestural space. In experiments, a set of gestures for a robot waiter was used to train the gesture recognition algorithm. The experimental results show that the proposed modifications of the standard gesture recognition algorithm improve the robustness of the recognition.

28 citations

Posted Content
TL;DR: This paper introduces a freehand sketch recognition framework based on "deep" features extracted from CNNs, and provides a preliminary glimpse of how such features can help identify crucial attributes of the sketched objects.
Abstract: Freehand sketches often contain sparse visual detail. In spite of the sparsity, they are easily and consistently recognized by humans across cultures, languages and age groups. Therefore, analyzing such sparse sketches can aid our understanding of the neuro-cognitive processes involved in visual representation and recognition. In the recent past, Convolutional Neural Networks (CNNs) have emerged as a powerful framework for feature representation and recognition for a variety of image domains. However, the domain of sketch images has not been explored. This paper introduces a freehand sketch recognition framework based on "deep" features extracted from CNNs. We use two popular CNNs for our experiments -- Imagenet CNN and a modified version of LeNet CNN. We evaluate our recognition framework on a publicly available benchmark database containing thousands of freehand sketches depicting everyday objects. Our results are an improvement over the existing state-of-the-art accuracies by 3% - 11%. The effectiveness and relative compactness of our deep features also make them an ideal candidate for related problems such as sketch-based image retrieval. In addition, we provide a preliminary glimpse of how such features can help identify crucial attributes (e.g. object-parts) of the sketched objects.

28 citations

Proceedings ArticleDOI
04 Dec 1990
TL;DR: BONSAI identifies and localizes 3-D objects in range images of one or more parts which have been designed on a CAD system via constrained search of the interpretation tree, using unary and binary constraints to prune the search space.
Abstract: A description is presented of BONSAI, a model-based 3-D object recognition system, which identifies and localizes 3-D objects in range images of one or more parts which have been designed on a CAD system. Recognition is performed via constrained search of the interpretation tree, using unary and binary constraints (derived automatically from the CAD models) to prune the search space. Experiments with over 200 images of 20 different parts demonstrate that the constrained search approach to 3-D object recognition has comparable accuracy to other existing systems. >

28 citations


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