Pilot shortages are predicted to increase over the next decades, resulting in a need to increase the number of qualified pilots. A challenge in meeting this need is maintaining the quality of the training while increa...
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This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available s...
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This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available s...
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In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replac...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
In statistical classification and machine learning, classification error is an important performance measure, which is minimized by the Bayes decision rule. In practice, the unknown true distribution is usually replaced with a model distribution estimated from the training data in the Bayes decision rule. This substitution introduces a mismatch between the Bayes error and the model-based classification error. In this work, we apply classification error bounds to study the relationship between the error mismatch and the Kullback-Leibler divergence in machine learning. Motivated by recent observations of low model-based classification errors in many machine learning tasks, bounding the Bayes error to be lower, we propose a linear approximation of the classification error bound for low Bayes error conditions. Then, the bound for class priors are discussed. Moreover, we extend the classification error bound for sequences. Using automatic speech recognition as a representative example of machine learning applications, this work analytically discusses the correlations among different performance measures with extended bounds, including cross-entropy loss, language model perplexity, and word error rate.
We introduce to VR a novel imperceptible gaze guidance technique from a recent discovery that human gaze can be attracted to a cue that contrasts from the background in its perceptually non-distinctive ocularity, defi...
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We introduce to VR a novel imperceptible gaze guidance technique from a recent discovery that human gaze can be attracted to a cue that contrasts from the background in its perceptually non-distinctive ocularity, defined as the relative difference between inputs to the two eyes. This cue pops out in the saliency map in the primary visual cortex without being overtly visible. We tested this method in an odd-one-out visual search task using eye tracking with 31 participants in VR. When the target was rendered as an ocularity singleton, participants' gaze was drawn to the target faster. Conversely, when a background object served as the ocularity singleton, it distracted gaze from the target. Since ocularity is nearly imperceptible, our method maintains user immersion while guiding attention without noticeable scene alterations and can render object's depth in 3D scenes, creating new possibilities for immersive user experience across diverse VR applications.
Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous fa...
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