falsehood
发表于 2025-3-23 10:00:16
https://doi.org/10.1007/978-3-030-20111-1rocessing of the pose and illumination variations is a vital key for improving the recognition rate. This paper proposes a novel method that can synthesize images with different head poses and lighting conditions by using a modified 3D CANDIDE model, linear vertex interpolation and NURBS curve surfa
Criteria
发表于 2025-3-23 16:54:56
In Flight Thrust Reverse Actuationlder detection algorithm based on wavelet decomposition technique and support vector machine (SVM) is proposed. Wavelet decomposition is used to extract features from real images, and linear SVM and non-linear SVM are trained for detection. Non-head-and-shoulder images can be removed by the linear S
朦胧
发表于 2025-3-23 20:40:09
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BYRE
发表于 2025-3-23 22:56:36
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ATRIA
发表于 2025-3-24 05:35:13
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形上升才刺激
发表于 2025-3-24 07:09:27
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Optimum
发表于 2025-3-24 11:57:58
Commercial Banking Risk ManagementZernike invariant moments and some dynamic features is built with a BP (back-propagation) network. In another scheme, 40 values computed with SVD(singular value decomposition) on thinned signature image and thinned high-density image compose the feature vector and another BP network is built for eve
Indelible
发表于 2025-3-24 16:41:57
Commercial Banking Risk Management such as illumination and pose variations. To overcome this disadvantage, many researchers have introduced multiple eigenspaces into face recognition field. But most of these methods require that both the number of eignspaces and dimensionality of the PCA subspaces are a priori given. In this paper,
effrontery
发表于 2025-3-24 22:37:54
https://doi.org/10.1057/978-1-137-59442-6on of face images are proposed in this paper. In the first approach, the high-dimensional face images are first mapped into the range space of total scatter matrix using principle component analysis (PCA). Then CCA is performed to extract the linear optimal discriminant features without losing Fishe
olfction
发表于 2025-3-25 02:35:01
Commercial Banking Risk Management Kohonen clustering Network (IFKCN) is proposed for texture segmentation. The method adjusts fuzzy factors to accelerate the speed of convergence. It intensifies the biggest membership and suppresses the other. By using this network in Brodatz texture segmentation, its iteration is fewer and the spe