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Titlebook: Intelligent Computing: Image Processing Based Applications; J. K. Mandal,Soumen Banerjee Book 2020 Springer Nature Singapore Pte Ltd. 2020

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樓主: hexagon
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發(fā)表于 2025-3-25 04:45:23 | 只看該作者
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發(fā)表于 2025-3-25 16:41:50 | 只看該作者
A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition,ndant features and assigning weights to the features left after elimination. Finally, the performance of the said feature descriptors on three standard benchmark video datasets . KTH, HMDB51, and UCF11 has been analyzed.
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發(fā)表于 2025-3-25 22:24:35 | 只看該作者
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發(fā)表于 2025-3-26 02:43:50 | 只看該作者
Image Denoising Using Generative Adversarial Network,g techniques are highlighted. Then, we state the underlying architecture of GAN and its modifications. Then, we discuss the way GANs are applied in the area of image denoising. We survey all recent works of GANs in image denoising and categories those work according to the type of input images. In t
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發(fā)表于 2025-3-26 07:33:52 | 只看該作者
28#
發(fā)表于 2025-3-26 11:58:22 | 只看該作者
An Optimized Intelligent Dermatologic Disease Classification Framework Based on IoT, vital role. In this work, an optimized classification method is proposed that can be useful in performing automated classification job in the limited infrastructure of the IoT environment. The input features are optimized in such a way so that it can be useful in faster and accurate classification
29#
發(fā)表于 2025-3-26 12:38:19 | 只看該作者
Transfer Learning Coupled Convolution Neural Networks in Detecting Retinal Diseases Using OCT Imageous diseases. The training accuracies obtained for the six architectures, viz. four convolutional layer deep CNNs, VGG (VGG-16 and VGG-19) and Google’s Inception [Google’s Inception v3 (with or without transfer learning)], and Google’s Inception v4, are, respectively, 87.15%, 91.40%, 93.32%, 85.31%,
30#
發(fā)表于 2025-3-26 19:07:09 | 只看該作者
Intelligent Computing: Image Processing Based Applications
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