Structure from motion (SfM) is a fundamental task in computervision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in ...
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Understanding the cognitive aspects of motor learning is crucial for novices to enhance learning and performance. One approach is mental rehearsal—"the cognitive rehearsal of a task prior to performance"—w...
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We propose an approach for Open-World Instance Segmentation (OWIS), a task that aims to segment arbitrary unknown objects in images by generalizing from a limited set of annotated object classes during training. Our S...
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In today's industrial landscape, automation has become increasingly vital, particularly in the deployment of robots for tasks such as sorting machine components. The use of robotic systems enhances process accurac...
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While character recognition systems applications with restrictions on writing conditions, such as zip code recognition and ledger sheet recognition, are in practical use, the accuracy of free handwritten character str...
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ISBN:
(数字)9798350373332
ISBN:
(纸本)9798350373349
While character recognition systems applications with restrictions on writing conditions, such as zip code recognition and ledger sheet recognition, are in practical use, the accuracy of free handwritten character string recognition is still low and there are many challenges. Japanese free handwritten character strings are difficult to segment because some Japanese characters consist of multiple connected components and there are no spaces between words. Therefore, in this study, we propose a method for handwritten character string recognition method that does not require character segmentation using Transformer and CNN features. Transformer-based models usually require a large amount of annotated training data. However, our method can be trained with a small amount of annotated training data. Comparative experiments confirm the effectiveness of the proposed method by achieving CER=O.127 while CRNN [1] and TrOCR [2] achieve CER=O.562 and CER=O.174, respectively. The results show that the proposed method is effective for Japanese character string recognition, where the variety of character strings is large and it is difficult to secure a sufficient number of training samples for each character string.
Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explor...
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This paper reports on a field study of the WavData Lamp: an interactive lamp that can physically visualize people's music listening data by changing light colors and outstretching its form enclosure. We deployed f...
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Autonomous robotic grasping has emerged as a valuable capability for automating everyday household tasks like table setting (bossing), cleaning, and meal preparation. Still, accurately detecting and manipulating diver...
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Time Delay Control (TDC) is recognized as a simple yet effective alternative to model-based or intelligent-based control due to its simplicity and robustness. Proportional-Integral-Derivative (PID) control is one of t...
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In this paper, we propose a transfer learning-based approach for road sign classification using pre-trained CNN models. We evaluate the performance of our fine-tuned VGG-16, VGG-19, ResNet50 and EfficientNetB0 models ...
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