A Temporal Scheme for Fast Learning of Image-Patch Correspondences in Realistic Multi-camera Setups

2013 ◽  
Author(s):  
Jens Eisenbach ◽  
Christian Conrad ◽  
Rudolf Mester
Keyword(s):  
Image Patch ◽  
Fast Learning

Fast Learning of Generalized Minimum Enclosing Ball for Large Datasets

2012 ◽  
Vol 38 (11) ◽  
pp. 1831
Author(s):  
Wen-Jun HU ◽  
Shi-Tong WANG ◽  
Juan WANG ◽  
Wen-Hao YING
Keyword(s):  
Large Datasets ◽  
Fast Learning ◽  
Minimum Enclosing Ball

scholarly journals Pansharpening of WorldView-2 Data via Graph Regularized Sparse Coding and Adaptive Coupled Dictionary

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3586
Author(s):  
Wenqing Wang ◽  
Han Liu ◽  
Guo Xie
Keyword(s):  
High Resolution ◽  
Sparse Representation ◽  
Sparse Coding ◽  
Spectral Response ◽  
Objective Evaluation ◽  
Coefficient Matrix ◽  
Image Patch ◽  
Subjective Analysis ◽  
High Resolution Image ◽  
Sparse Coefficient

The spectral mismatch between a multispectral (MS) image and its corresponding panchromatic (PAN) image affects the pansharpening quality, especially for WorldView-2 data. To handle this problem, a pansharpening method based on graph regularized sparse coding (GRSC) and adaptive coupled dictionary is proposed in this paper. Firstly, the pansharpening process is divided into three tasks according to the degree of correlation among the MS and PAN channels and the relative spectral response of WorldView-2 sensor. Then, for each task, the image patch set from the MS channels is clustered into several subsets, and the sparse representation of each subset is estimated through the GRSC algorithm. Besides, an adaptive coupled dictionary pair for each task is constructed to effectively represent the subsets. Finally, the high-resolution image subsets for each task are obtained by multiplying the estimated sparse coefficient matrix by the corresponding dictionary. A variety of experiments are conducted on the WorldView-2 data, and the experimental results demonstrate that the proposed method achieves better performance than the existing pansharpening algorithms in both subjective analysis and objective evaluation.

scholarly journals Unsupervised natural image patch learning

2019 ◽  
Vol 5 (3) ◽  
pp. 229-237 ◽  
Author(s):  
Dov Danon ◽  
Hadar Averbuch-Elor ◽  
Ohad Fried ◽  
Daniel Cohen-Or
Keyword(s):  
Natural Image ◽  
Image Patch

Error Analysis of Neural Network with Rational Spline Weight Function

2014 ◽  
Vol 644-650 ◽  
pp. 2407-2410
Author(s):  
Dai Yuan Zhang ◽  
Jia Kai Wang
Keyword(s):  
Neural Network ◽  
Weight Function ◽  
Topological Structure ◽  
High Accuracy ◽  
Local Minima ◽  
Slow Convergence ◽  
Rational Spline ◽  
Fast Learning ◽  
Learning Speed ◽  
Training Neural Network

Training neural network by spline weight function (SWF) has overcomed many defects of traditional neural networks (such as local minima, slow convergence and so on). It becomes more important because of its simply topological structure, fast learning speed and high accuracy. To generalize the SWF algorithm, this paper introduces a kind of rational spline weight function neural network and analyzes the performance of approximation for this neural network.

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