Stock price dynamics prediction based on multi-scale fractals and deep learning. [PDF]
Du Y, Tian Y.
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The highly anisotropic Fermi surface of bismuth results in variations in magnetotransport properties across different crystallographic directions, which can be characterized by studying microcrystals. To avoid the observed surface melting under room temperature Focused Ion Beam (FIB) irradiation, two low‐temperature FIB fabrication methods are proposed
Amaia Sáenz‐Hernández +6 more
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Cross perspective AFF-CNN-HTransformer target recognition matching of satellite and UAV aerial images. [PDF]
Cheng L, Xu J.
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Learning Domain-Invariant Representations for Event-Based Motion Segmentation: An Unsupervised Domain Adaptation Approach. [PDF]
Jeryo M, Harati A.
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Enhanced image-splicing classification: A resilient and scale-invariant approach utilizing edge-weighted local texture features. [PDF]
Akram A +4 more
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Evaluating Feature-Based Homography Pipelines for Dual-Camera Registration in Acupoint Annotation. [PDF]
Nanayakkara T +5 more
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An intelligent MRI data fusion framework for optimized diagnosis of spinal tumors. [PDF]
Shi Z, Jiang J, Li M, Zhao X.
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UAV-TIRVis: A Benchmark Dataset for Thermal-Visible Image Registration from Aerial Platforms. [PDF]
Vasile CE, Bîră C, Hobincu R.
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Scale Invariant Feature Transform using oriented pattern
2014 IEEE 27th Canadian Conference on Electrical and Computer Engineering (CCECE), 2014Image matching plays an important role in many aspects of computer vision. Our proposed method is based on Scale Invariant Feature Transform (SIFT) which is one of the popular image matching methods. The main ideas behind our method are removing the excess keypoints, adding oriented patterns to descriptor, and decreasing the size of the descriptors. By
Mohammad Baghery Daneshvar +2 more
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Keyboard recognition from scale-invariant feature transform
2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), 2017Based on the scale-invariant feature transform, this paper presents an approach to keyboard recognition. Not only the skewed keyboard can be corrected, but also the keys in the keyboard can be located. Experimental results confirm the feasibility of the proposed method.
Ming-Te Chao, Yung-Sheng Chen
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