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Titlebook: Image and Graphics; 9th International Co Yao Zhao,Xiangwei Kong,David Taubman Conference proceedings 2017 Springer Nature Switzerland AG 20

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發(fā)表于 2025-3-26 23:30:43 | 只看該作者
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發(fā)表于 2025-3-27 04:35:40 | 只看該作者
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發(fā)表于 2025-3-27 08:49:37 | 只看該作者
Image Captioning with Object Detection and Localizationulti-model neural network method closely related to the human visual system that automatically learns to describe the content of images. Our model consists of two sub-models: an object detection and localization model, which extracts the information of objects and their spatial relationship in image
34#
發(fā)表于 2025-3-27 09:32:22 | 只看該作者
Image Set Representation with ,-Norm Optimal Mean Robust Principal Component Analysislly contains various kinds of noises and outliers which usually make the recognition/learning tasks of image set more challengeable. In this paper, we propose a new . norm optimal Mean Principal Component Analysis (L1-MPCA) to learn an optimal low-rank representation for image set. Comparing with or
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發(fā)表于 2025-3-27 14:44:13 | 只看該作者
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發(fā)表于 2025-3-27 20:33:24 | 只看該作者
Hardness Prediction for Object Detection Inspired by Human Visionn path and (2) peaks of heat to better define and understand human vision. In this paper, these features are used to describe the eye movements of a person when he/she is watching an image and looking for the target object in it. Based on these features, a new image complexity called . is defined. E
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發(fā)表于 2025-3-28 01:03:37 | 只看該作者
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發(fā)表于 2025-3-28 07:42:26 | 只看該作者
A Dim Small Target Detection Method Based on Spatial-Frequency Domain Features Spacerveillance. Due to the complexity of the imaging environment, the detection of dim small targets in star images faces many difficulties, including low SNR and rare unstable features. This paper proposes a dim small target detection method based on the high dimensional spatial-frequency domain featur
40#
發(fā)表于 2025-3-28 13:50:18 | 只看該作者
An Algorithm for Tight Frame Grouplet to Compute Association Fieldslity property, multiscale association fields become more flexible to construct grouplets which can adapt the different geometry structure in different scales. Grouplet transform uses the block matching algorithm to compute association field coefficients, which needs more operations than the computat
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