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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
22 Nov 2010
TL;DR: The application range of early recognition to multiple people based on the co-occurrence of gesture patterns is expanded and associative memory based approach learns the relationship between cooccurring gestures.
Abstract: We propose an approach to achieve early recognition of gesture patterns. We assume that there are two people who interact with a machine, a robot or something. In such a situation, a gesture of a person often has a relationship with a gesture of another person. We exploit such a relationship to realize early recognition of gesture patterns. Early recognition is a method to recognize sequential patterns at their beginning parts. Therefore, in the case of gesture recognition, we can get a recognition result of human gestures before the gestures have finished. Recent years, some approaches have been proposed. In this paper, we expand the application range of early recognition to multiple people based on the co-occurrence of gesture patterns. In our approach, we use Self-Organizing Map to represent gesture patterns of each person, and associative memory based approach learns the relationship between cooccurring gestures. In the experiments, we have found that our proposed method achieved the early recognition more accurately and earlier than the traditional approach.

2 citations

Book ChapterDOI
21 Sep 2010
TL;DR: A novel framework for collaborative object recognition is introduced, which expands the applicability and improves the accuracy of object recognition, and can provide a useful, easy-to-use tool.
Abstract: This paper introduces a novel framework for collaborative object recognition, which expands the applicability and improves the accuracy of object recognition. In this framework, a system not only recognizes targets but also detects and evaluates conditions that may make recognition difficult, and tries to resolve the situation by presenting the user with information on how to alter the conditions. The user can see how to make improvements, leading to correct recognition with little effort. The system can provide a useful, easy-to-use tool. In this research, a prototype system for kitchen scenes is designed, which can achieve situation evaluation and human-computer collaboration to improve recognition. We verified the framework by observing improvements in recognition accuracy and behavior of users in our experiments.

2 citations

Posted Content
TL;DR: An active learning framework is proposed that combines the strengths of these methods, while addressing their weaknesses, and allows reaching top accuracy figures with up to 30% savings in annotation cost.
Abstract: Sketch recognition allows natural and efficient interaction in pen-based interfaces. A key obstacle to building accurate sketch recognizers has been the difficulty of creating large amounts of annotated training data. Several authors have attempted to address this issue by creating synthetic data, and by building tools that support efficient annotation. Two prominent sets of approaches stand out from the rest of the crowd. They use interim classifiers trained with a small set of labeled data to aid the labeling of the remainder of the data. The first set of approaches uses a classifier trained with a partially labeled dataset to automatically label unlabeled instances. The others, based on active learning, save annotation effort by giving priority to labeling informative data instances. The former is sub-optimal since it doesn't prioritize the order of labeling to favor informative instances, while the latter makes the strong assumption that unlabeled data comes in an already segmented form (i.e. the ink in the training data is already assembled into groups forming isolated object instances). In this paper, we propose an active learning framework that combines the strengths of these methods, while addressing their weaknesses. In particular, we propose two methods for deciding how batches of unsegmented sketch scenes should be labeled. The first method, scene-wise selection, assesses the informativeness of each drawing (sketch scene) as a whole, and asks the user to annotate all objects in the drawing. The latter, segment-wise selection, attempts more precise targeting to locate informative fragments of drawings for user labeling. We show that both selection schemes outperform random selection. Furthermore, we demonstrate that precise targeting yields superior performance. Overall, our approach allows reaching top accuracy figures with up to 30% savings in annotation cost.

2 citations

Proceedings ArticleDOI
01 Jul 2020
TL;DR: The implementation of cGANs for synthesizing the images from hand-drawn sketches gives a remarkable output and the performance of the proposed sketch to image translation network was excellent and appreciable.
Abstract: Today, Technology have remarkable charm in the area of Computer Graphics and Vision. Producing absolute images from the poor hand-drawn sketches is a very demanding and laborious task in this area. Hand-drawn sketch recognition is widely used in sketch based image and video retrieval, manipulations and reorganizations. In S ketch to image synthesis, the sketches are translated to realistic images with the use of a generative model. An image is put forward to image translation network that involves in producing a synthesized image from the input sketch via an adversarial process. A novel Conditional Generative Adversarial Network (cGANs) which is an extension of Generative Adversarial Networks (GANs) is used to produce the images with some sort of conditions or attributes. In this work, the implementation of cGANs for synthesizing the images from hand-drawn sketches gives a remarkable output. The performance of the proposed sketch to image translation network was excellent and appreciable.

2 citations


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