Search - AREA BASED IMAGE FUSION

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[Special Effects] dimensional-smoothed-deformation DL : 0: 　数字图象变形是图象处理领域中的一个热点, 具有较大的实用价值. 通过对基于特征变形算法的分析, 在三维体素数据模型上, 提出了一种基于特征的散乱点三维变形算法. 在算法中首先采用移动平滑插值函数实现对变形扭曲的拟合, 其次采用融合等方法提高变形的精度和效果, 最后通过试验表明该算法是可行的. 该算法不但可以实现三维变形, 而且可以用于对二维影像的处理.-Image processing digital image deformation is a hot area, with great practical value. By deformation algorithm based on feature analysis of the data in three-dimensional voxel model, a feature-based deformation algorithm for three-dimensional scattered points . In this algorithm, the first move smooth interpolation function is used to achieve the deformation of the fitting twist, followed by fusion method to improve the accuracy and effectiveness of deformation, and finally the experiment show that the algorithm is feasible. The algorithm can not only three-dimensional deformation, and can for two-dimensional image processing.
Date : 2025-12-20 Size : 188kb User : 陈东尧
[Special Effects] Wavelet-Image-Fusion DL : 0: 基于低频融合策略的小波图像融合算法可分为三个细节：高频带的融合、低频融合的对象、低频融合策略的程序代码。其中对最后一部分中一些细节问题要花心思处理，比如区域大小的确定、区域边界与图像边界的关系、区域中心与区域中各点的权值确定、区域中心在原始图像中的具体位置等等。-Fusion strategy based on low-frequency wavelet image fusion algorithm can be divided into three details: integration, low-fusion target, the low-frequency fusion strategy program code the high frequency band. Where some of the details of the last part of the effort is needed to deal with issues such as the relationship between the size of the defined area, the boundary of the image area boundary, regional center and the right value of each point in the area to determine the specific location of regional centers in the original image, etc. .
Date : 2025-12-20 Size : 2kb User : 陶陶
[Special Effects] Saliency-Detection DL : 0: 提出一种新的显着性检测方法，通过将区域级显着性估计和像素级显着性预测与CNN（表示为CRPSD）相结合。对于像素级显着性预测，通过修改VGGNet体系结构来执行完全卷积神经网络（称为像素级CNN）以执行多尺度特征学习，基于该学习进行图像到图像预测以完成像素级显着性检测。对于区域级显着性估计，首先设计基于自适应超像素的区域生成技术以将图像分割成区域，基于该区域通过使用CNN模型（称为区域级CNN）来估计区域级显着性。通过使用另一CNN（称为融合CNN）融合像素级和区域级显着性以形成nal显着图，并且联合学习像素级CNN和融合CNN。对四个公共基准数据集的大量定量和定性实验表明，所提出的方法大大优于最先进的显着性检测方法。-A new saliency detection method by significant regional level estimates and forecast significant pixel level and CNN (expressed as CRPSD) combined. For pixel-level significant prediction to perform a full convolution neural network by modifying VGGNet architecture (called pixel-level CNN) learning to perform multi-scale features, image to image prediction to complete the pixel level detection based on the significant learning . For regional levels significantly estimate, the first generation technology to design image is divided into regions based on adaptive super-pixel area, based on the model of the region through the use of CNN (CNN called regional level) to estimate regional levels significantly. By using another CNN (CNN called fusion) Fusion pixel level and regional level to form nal significant saliency map, and the Joint Learning pixel level fusion CNN and CNN. Four common reference data set of a large number of quantitative and qualitative experiments show that the proposed m
Date : 2025-12-20 Size : 4.22mb User : 祖祖-