Asymmetric normalized correlation layer for deep neural network feature matchingA method includes obtaining a first image of a scene using a first image sensor of an electronic device and a second image of the scene using a second image sensor of the electronic device. The method also includes ...
The coefficient of Correlation (CoC) is a unique feature for finding the probability of a linear relationship between two parametric quantities. In contrast, Discrete Wavelet Transform (DWT) focuses on the coefficient of a small number of signal-based images. Table 17 illustrates the outcome of th...
CNNs consist of three components: input layer, hidden layer, and output layer. The main structure of the hidden layer alternates between linear convolution and non-linear activation functions, primarily serving to map features from the input. In the domain of image denoising, the advantage of CNN...
aGene–gene correlation matrix of candidate feature genes (high correlation range score).b–dResiduals from stepwise regression on the gene–gene correlation matrix.eUMAP visualization of cells in an optimal feature space, colored by cell line.f–hSame UMAP, colored by expression of genes regressed...
Mahesh J, Sumit S (2014) Gradient local auto-correlation for handwritten Devanagari character recognition. Proceedings of the international conference on high performance computing and applications (ICHPCA), 1–5. https://doi.org/10.1109/ICHPCA.2014.7045339 Mahesh J, Sumit S (2016) Similar handwritt...
However, the deep and complicated characteristic representations of lncRNA-disease associations were failed to be extracted, and the discriminative contributions of the interactions, correlations, and similarities among miRNAs diseases, and lncRNAs for the correlation predictions were ignored. In this paper...
(AlexNet,ResNet, VGGNet, etc.) were pre-trained on the CASIA database. DepressNet is constructed by changing the softmax layer into a regression layer, followed by a globalaverage pooling(GAP) layer, as shown inFig. 17. Specifically, the DepressNet consists of four bottleneck blocks, ...
To do this, we introduce the concept of "layers" to describe the correlation of pixels through computing each pixel weight, and construct the orientation fields to capture the inherent direction and variation of texture features through computing the dominant local orientation angle and the degree ...
Correlation-based feature selection (CFS): This multivariate filter algorithm ranks feature subsets based on a heuristic evaluation function based on a correlation. The bias function evaluates subsets that correlate with the class and are not correlated with other features. Non-relevant features are dis...
The proposed approach evaluates the impact/importance of processes features by using information theoretic measures to measure the correlation between the process features and the modelling performance. Crucially, the proposed method acts during the training of the process model; hence it is an embedded...