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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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Journal ArticleDOI
TL;DR: Three methods of machine learning are compared for adaptive sketch recognition with some experiments based on various feature representations of sketches and collected samples of multi-users, which reveal elementally some important matters of sketch recognition based on machine learning methods.

9 citations

Journal ArticleDOI
TL;DR: A system for automatic understanding of sketch maps and the underlying algorithms for all steps is presented and it is shown how these algorithms can deal with the major problems of sketch map understanding, such as vagueness in interpretation.
Abstract: We present the first comprehensive system for offline classifying sketch map objects.We created a database of labeled sketch maps for training and evaluation purposes.Context-awareness improves the classification of sketch map objects greatly. Sketching is a natural and easy way for humans to express visual information in everyday life. Despite a number of approaches to understand online sketch maps, the automatic understanding of offline, hand-drawn sketch maps still poses a problem. This paper presents a new approach for novel sketch map understanding. To our knowledge, this is the first comprehensive work dealing with this task in an offline way. This paper presents a system for automatic understanding of sketch maps and the underlying algorithms for all steps. Major parts are a region-growing segmentation for sketch map objects, a classification for isolated objects, and a context-aware classification. The context-aware classification uses probabilistic relaxation labeling to integrate dependencies between objects into the recognition. We show how these algorithms can deal with the major problems of sketch map understanding, such as vagueness in interpretation. Our experiments demonstrate the importance of context-aware classification for sketch map understanding. In addition, a new database of annotated sketch maps was developed and is made publicly available. This can be used for training and evaluation of sketch map understanding algorithms.

9 citations

Proceedings ArticleDOI
01 Oct 2014
TL;DR: A method to address the efficiency and robustness of dynamic hand gesture recognition for human robot interaction by using on-board monocular camera and specialized gesture detection algorithms and an efficient adaptive DMP learning method is proposed.
Abstract: In this paper a method to address the efficiency and robustness of dynamic hand gesture recognition for human robot interaction is proposed. By using on-board monocular camera and specialized gesture detection algorithms, the humanoid robot is able to detect gestures fast. To model the dynamics of gestures, the dynamic movement primitives (DMP) model is employed, which well characterizes both spatial and temporal evolutions of gestures. The invariance properties of the DMP model against different spatiotemporal scales also offer expected robustness to handle the variances in gestures. To cope with the diversity and noise of gestures, an efficient adaptive DMP learning method is further proposed. Since the learnt weights of the DMP compactly represent the original gestures, they serve as ideal feature vectors for building a classifier to recognize new gestures. To evaluate the proposed method, a nine-class human gestures recognition task on a real humanoid robot is performed and 98.06% accuracy is obtained. Experimental results demonstrate the effectiveness of our method.

9 citations

Proceedings ArticleDOI
23 Aug 2004
TL;DR: A group of classifiers arranged hierarchically is used to achieve robust recognition of the large number of symbols appearing in expressions to achieve high recognition accuracy.
Abstract: This paper deals with recognition of printed mathematical symbols. A group of classifiers arranged hierarchically is used to achieve robust recognition of the large number of symbols appearing in expressions. The classifier used at the top level employs stroke-based classification technique to recognize some of the frequently occurring symbols. The second level uses three classifiers to recognize the rest of the expression symbols. Different combination techniques have been attempted to integrate the second level classifiers to achieve high recognition accuracy. Experiment shows that the proposed approach is quite robust for recognition of a large number of symbols appearing in various expressions.

9 citations

Proceedings ArticleDOI
01 Dec 2019
TL;DR: Experimental results on sketch recognition and sketch-based image retrieval demonstrate the effectiveness of the approach for learning an image-to-sketch translation network via unpaired examples.
Abstract: Image-to-sketch translation is to learn the mapping between an image and a corresponding human drawn sketch. Machine can be trained to mimic the human drawing process using a training set of aligned image-sketch pairs. However, to collect such paired data is quite expensive or even unavailable for many cases since sketches exhibit various level of abstractness and drawing preferences. Hence we present an approach for learning an image-to-sketch translation network via unpaired examples. A translation network, which can translate the representation in image latent space to sketch domain, is trained in unsupervised setting. To prevent the problem of representation shifting in cross-domain translation, a novel cycle+ consistency loss is explored. Experimental results on sketch recognition and sketch-based image retrieval demonstrate the effectiveness of our approach.

9 citations


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