Topic
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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Papers
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05 Dec 1994TL;DR: This paper presents a methodology for robust 3D object recognition using uncertain image data capable of achieving acceptable performance in the presence of both segmentation problems and sensor uncertainty, thus eliminating the need for ad hoc heuristics.
Abstract: A successful 3D object recognition system must take into account imperfections in the input data, due for example to fragmentation or sensor noise. In this paper we propose a methodology for robust 3D object recognition using uncertain image data. In particular, we present a method capable of achieving acceptable performance in the presence of both segmentation problems and sensor uncertainty, thus eliminating the need for ad hoc heuristics. The proposed method is based upon the use of probabilistic models suggested by the underlying physics processes. These models are statistically validated and tested under controlled experimentation.
4 citations
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31 Aug 1995
TL;DR: A new distance between two representations called the elastic distance is presented based on the dynamic programming technique and it is shown that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes.
Abstract: Vector comparison is essential in pattern recognition. Numerous methods based on distance computation are available to carry out such comparison. Unfortunately most of them are applicable only if the vectors are of the same length or do not take into account components misalignment. This paper presents a new distance between two representations called the elastic distance and based on the dynamic programming technique. Properties are studied. We show that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes. A new centroid computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an online numerical handwritten character recognition problem using a previously computed centroid of a set of prototypes.
4 citations
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30 Aug 1992
TL;DR: The application of the structural learning technique known as error correcting grammatical inference to planar shape recognition is discussed and illustrated with a non-trivial printed digit recognition task.
Abstract: The application of the structural learning technique known as error correcting grammatical inference to planar shape recognition is discussed and illustrated with a non-trivial printed digit recognition task. Experimental results are presented and compared with those of other more conventional (non-structural) techniques, showing the new technique to provide significantly improved performance. >
4 citations
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15 Sep 2016TL;DR: This paper proposes a CNN training on contours that performs well on sketch recognition over different databases of the sketch images and makes some adjustments to the contours for training to reach higher recognition accuracy.
Abstract: The paper presents a deep Convolutional Neural Network (CNN) framework for free-hand sketch recognition. One of the main challenges in free-hand sketch recognition is to increase the recognition accuracy on sketches drawn by different people. To overcome this problem, we use deep Convolutional Neural Networks (CNNs) that have dominated top results in the field of image recognition. And we use the contours of natural images for training, because sketches drawn by different people may be very different and databases of the sketch images for training are very limited. We propose a CNN training on contours that performs well on sketch recognition over different databases of the sketch images. And we make some adjustments to the contours for training and reach higher recognition accuracy. Experimental results show the effectiveness of the proposed approach.
4 citations
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20 Sep 2005TL;DR: This paper proposes an approach for constructing sketch parsers whose recognition accuracy and speed is significantly improved by acquiring information on the user's sketching style during a training phase.
Abstract: In this paper we propose an approach for constructing sketch parsers whose recognition accuracy and speed is significantly improved by acquiring information on the user's sketching style during a training phase. The construction process consists in specifying a sketch grammar description of the language syntax, automatically generating a parser from such specification, and let the user train the recognition system on a set of sketch sentences.
4 citations