scholarly journals CraterIDNet: An End-to-End Fully Convolutional Neural Network for Crater Detection and Identification in Remotely Sensed Planetary Images

2018 ◽  
Vol 10 (7) ◽  
pp. 1067 ◽  
Author(s):  
Hao Wang ◽  
Jie Jiang ◽  
Guangjun Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Remotely Sensed ◽  
Crater Detection ◽  
Detection And Identification ◽  
End To End

scholarly journals Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

2021 ◽  
Vol 13 (2) ◽  
pp. 274
Author(s):  
Guobiao Yao ◽  
Alper Yilmaz ◽  
Li Zhang ◽  
Fei Meng ◽  
Haibin Ai ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Stereo Matching ◽  
Least Square ◽  
Affine Invariant ◽  
Stereo Images ◽  
Distance Ratio ◽  
Matching Algorithm ◽  
End To End

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

End-to-end Convolutional Neural Network Design for Automatic Detection of Influenza Virus

2021 ◽  
Vol 10 (1) ◽  
pp. 31-36
Author(s):  
Junghwan Lee ◽  
Heesang Eom ◽  
Yuli Sun Hariyani ◽  
Cheonjung Kim ◽  
Yongkyoung Yoo ◽  
...  
Keyword(s):  
Neural Network ◽  
Influenza Virus ◽  
Convolutional Neural Network ◽  
Network Design ◽  
Automatic Detection ◽  
End To End

scholarly journals Saliency Contour Extraction Based on Multi-level Feature Channel Optimization Coding

2021 ◽  
Vol 7 (4) ◽  
pp. 117
Author(s):  
Linling Fang ◽  
Yingle Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Detection Method ◽  
Gabor Filter ◽  
Contour Detection ◽  
Frequency Separation ◽  
Experimental Sample ◽  
Multi Level ◽  
End To End

<p>A biomimetic vision computing model based on multi-level feature channel optimization coding is proposed and applied to image contour detection, combining the end-to-end detection method of full convolutional neural network and the traditional contour detection method based on biological vision mechanism. Considering the effectiveness of the Gabor filter in perceiving the scale and direction of the image target, the Gabor filter is introduced to simulate the multi-level feature response on the visual path. The optimal scale and direction of the Gabor filter are obtained based on the similarity index, and they are used as the frequency separation parameter of the NSCT transform. The contour sub-image obtained by the NSCT transform is combined with the original image for feature enhancement and fusion to realize the primary contour response. The low-dimensional and low-redundancy primary contour response is used as the input sample of the network model to relieve network pressure and reduce computational complexity. A fully improved convolutional neural network model is constructed for multi-scale training, through feature encoder to feature decoder, to achieve end-to-end pixel prediction, and obtain a complete and continuous detection image of the subject contour. Using the BSDS500 atlas as the experimental sample, the average accuracy index is 0.85, which runs on the device CPU at a detection rate of 20+ FPS to achieve a good balance between training efficiency and detection effect.</p>

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