Softmax regression algorithm was used for face classification and recognition,and improved Softmax was used for face classification in the output layer of convolution neural network. The experimental results show that the recognition rate of the method is close to 100%,which is better than the ...
The concatenated feature is later reduced to the standard dimension using Principal Component Analysis (PCA) algorithm, generating the refined CNN feature. When applied in image classification and retrieval tasks, the PPC feature consistently outperforms the conventional CNN feature, regardless of the ...
Face Recognition Algorithm Based on Sparse Representation of DAE Convolution Neural NetworkFace recognitionconvolution neural networkdenoising autoencodersparse representationconvolution kernelrecognition effectIn order to extract detailed facial features, we build a face aging effect simulation model based on ...
Secondly, an adaptive convolution based neural network is designed to classify and validate the segmentation. The Direct Weighted MRI(DW-MRI) imaging is taken into consideration which gives better information than CT imaging with single modality. The proposed algorithm is experimentally tested using AU...
In terms of the problems of five categories of nonweld seam stripes, including inclusion, oil-spot, silk-spot, and water-spot, which interfere with weld seam recognition during robotic welding, a convolutional neural network (CNN) algorithm, combined with a multistage training strategy, is used ...
(integration becomes mere multiplication). Convolution in the frequency domain can be faster than in the time domain by using the Fast Fourier Transform (FFT) algorithm. Some of the fastestGPUimplementations of convolutions (for example some implementations in the NVIDIAcuDNNlibrary) currently make ...
CNNs have several layers, the most common of which are convolution, ReLu, and pooling. Layers in a convolutional neural network (CNN). Convolution layers act as filters—each layer applies a filter and extracts specific features from the image. These filter values are learned by the network wh...
and interpreting data in a mechanism that imitates the human brain33,34,35. Deep learning can form an abstract high-level representation by combining low-level features to discover the rules of data. Therefore, in this paper, we use deep learning convolution neural network algorithm to extract ...
In this paper, we propose the algorithm for stroke lesion segmentation based on a deep convolutional neural network (CNN). The model is based on U-shaped CNN, which has been applied successfully to other medical image segmentation tasks. The network architecture was derived from the model present...
et al. SRCNN-PIL: Side Road Convolution Neural Network Based on Pseudoinverse Learning Algorithm. Neural Process Lett 53, 4225–4237 (2021). https://doi.org/10.1007/s11063-021-10595-7 Download citation Accepted12 July 2021 Published28 July 2021 Issue DateDecember 2021 DOIhttps://doi.org/...