Results 1 to 10 of about 160,762 (267)
DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition [PDF]
Frontiers in Neuroscience, 2016 Benchmarks have played a vital role in the advancement of visual object recognition and other fields of computer vision (LeCun et al., 1998; Deng et al., 2009;). The challenges posed by these standard datasets have helped identify and overcome the shortcomings of existing approaches, and have led to great advances of the state of the art.
Yuhuang Hu +3 more
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Beyond core object recognition: Recurrent processes account for object recognition under occlusion.
PLoS Computational Biology, 2019 Core object recognition, the ability to rapidly recognize objects despite variations in their appearance, is largely solved through the feedforward processing of visual information.
Karim Rajaei +3 more
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A Web Application for Watermark Recognition [PDF]
Journal of Data Mining and Digital Humanities, 2020 The study of watermarks is a key step for archivists and historians as it enables them to reveal the origin of paper. Although highly practical, automatic watermark recognition comes with many difficultiesand is still considered an unsolved challenge ...
Oumayma Bounou +10 more
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IET Biometrics, 2022 Breast cancer accounts for the largest number of patients among all cancers in the world. Intervention treatment for early breast cancer can dramatically extend a woman's 5‐year survival rate.
Lilei Sun +6 more
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Two‐view attention‐guided convolutional neural network for mammographic image classification
CAAI Transactions on Intelligence Technology, 2023 Deep learning has been widely used in the field of mammographic image classification owing to its superiority in automatic feature extraction. However, general deep learning models cannot achieve very satisfactory classification results on mammographic ...
Lilei Sun +6 more
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An Introduction to Object Recognition [PDF]
Zeitschrift für Naturforschung C, 1998 Abstract In this report we present a general introduction to object recognition. We begin with brief discussions of the terminology used in the object recognition literature and the psychophysi cal tasks that are used to investigate object recognition. We then discuss models of shape representation. We dispense with the idea that shape
Liter, J., Bülthoff, H.
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Journal of Open Archaeology Data, 2021 These data were used to build an object detection model to locate Relict Charcoal Hearths (RCH) as described in the paper “When Computers Dream of Charcoal: Using Deep Learning, Open Tools and Open Data to Identify Relict Charcoal Hearths in and around ...
Weston Conner +2 more
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Multiview-Learning-Based Generic Palmprint Recognition: A Literature Review
Mathematics, 2023 Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture.
Shuping Zhao, Lunke Fei, Jie Wen
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Object Recognition with and without Objects [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 While recent deep neural networks have achieved a promising performance on object recognition, they rely implicitly on the visual contents of the whole image. In this paper, we train deep neural networks on the foreground (object) and background (context) regions of images respectively.
Zhuotun Zhu, Lingxi Xie, Alan L. Yuille
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Some Reiteration Theorems for R, L, RR, RL, LR, and LL Limiting Interpolation Spaces
Journal of Function Spaces, 2021 We consider the K-interpolation methods involving slowly varying functions. We establish some reiteration formulae including so-called L or R limiting interpolation spaces as well as the RR, RL, LR, and LL extremal interpolation spaces.
Leo R. Ya. Doktorski
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