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首页> 外文期刊>Machine Vision and Applications >Learning class-specific dictionaries for digit recognition from spherical surface of a 3D ball
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Learning class-specific dictionaries for digit recognition from spherical surface of a 3D ball

机译:学习特定于类的词典以从3D球的球面识别数字

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摘要

In the literature, very few researches have addressed the problem of recognizing the digits placed on spherical surfaces, even though digit recognition has already attracted extensive attentions and been attacked from various directions. As a particular example of recognizing this kind of digits, in this paper, we introduce a digit ball detection and recognition system to recognize the digit appearing on a 3D ball. The so-called digit ball is the ball carrying Arabic number on its spherical surface. Our system works under weakly controlled environment to detect and recognize the digit balls for practical application, which requires the system to keep on working without recognition errors in a real-time manner. Two main challenges confront our system, one is how to accurately detect the balls and the other is how to deal with the arbitrary rotation of the balls. For the first one, we develop a novel method to detect the balls appearing in a single image and demonstrate its effectiveness even when the balls are densely placed. To circumvent the other challenge, we use spin image and polar image for the representation of the balls to achieve rotation-invariance advantage. Finally, we adopt a dictionary learning-based method for the recognition task. To evaluate our system, a series of experiments are performed on real-world digit ball images, and the results validate the effectiveness of our system, which achieves 100 % accuracy in the experiments.
机译:在文献中,尽管数字识别已经引起了广泛的关注并且受到了来自各个方向的攻击,但是很少有研究解决识别放置在球形表面上的数字的问题。作为识别此类数字的一个特定示例,在本文中,我们介绍了一种数字球检测和识别系统,用于识别3D球上出现的数字。所谓数字球是指在其球面上带有阿拉伯数字的球。我们的系统在弱控制环境下工作,以检测和识别数字球以进行实际应用,这要求系统保持实时运行而不会出现识别错误。我们的系统面临两个主要挑战,一个是如何准确检测球,另一个是如何应对球的任意旋转。对于第一个,我们开发了一种新颖的方法来检测出现在单个图像中的球,并证明了即使将球密集放置时其有效性。为了避免其他挑战,我们使用旋转图像和极坐标图像表示球,以实现旋转不变性优势。最后,我们采用基于字典学习的方法进行识别任务。为了评估我们的系统,对真实世界的数字球图像进行了一系列实验,结果验证了我们系统的有效性,该系统在实验中达到了100%的准确性。

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