Description: 计算两幅图像联合直方图的matlab小程序。可以用于图像分割及图像互信息的计算-calculated two joint histogram of the image of small Matlab procedures. Can be used for image segmentation and image mutual information calculation Platform: |
Size: 1179 |
Author:zhyancheng72 |
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Description: 计算两幅图像联合直方图的matlab小程序。可以用于图像分割及图像互信息的计算-calculated two joint histogram of the image of small Matlab procedures. Can be used for image segmentation and image mutual information calculation Platform: |
Size: 1024 |
Author:zhyancheng72 |
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Description: 计算两幅图像的互信息和联合直方图，适于图像处理初学者，-Calculation of two images of the mutual information and joint histogram, image processing is suitable for beginners, Platform: |
Size: 1024 |
Author:zhangji |
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Description: The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented.-The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. The term image registration is most commonly used to denote the process of alignment of images , that is of transforming them to the common coordinate system. This is done by optimizing a similarity measure between the two images. A widely used measure is Mutual Information (MI). This method requires estimating joint histogram of the two images. Experiments are presented that demonstrate the approach. The technique is intensity-based rather than feature-based. As a comparative assessment the performance based on normalized mutual information and cross correlation as metric have also been presented. Platform: |
Size: 98304 |
Author:Harry |
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Description: -The existence of numerous imaging modalities makes it possible to present different data present in different modalities together thus forming multimodal images. Component images forming multimodal images should be aligned, or registered so that all the data, coming from the different modalities, are displayed in proper locations. Mutual Information is the similarity measure used in this case for optimizing the two images. This method requires estimating joint histogram of the two images. The fusion of images is the process of combining two or more images into a single image retaining important features from each. The Discrete Wavelet Transform (DWT) has become an attractive tool for fusing multimodal images. In this work it has been used to segment the features of the input images to produce a region map. Features of each region are calculated and a region based approach is used to fuse the images in the wavelet domain. Platform: |
Size: 67584 |
Author:Harry |
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Description: 首先读入图像，得到RGB3个分量，然后对三个分量分别作直方图均衡化，最后合成三个分量返回到RGB图，但是这种方法割裂了RGB3种颜色的相互关系-First read in the image, to get RGB3 components, then the three components of histogram equalization, the final synthesis of the three components of return to RGB chart, but this method separates the mutual relations of RGB3 colors.
Platform: |
Size: 29696 |
Author:henry424 |
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Description: A function to estimate the mutual information (MI) between pairs of features and target classes. A histogram approach is used in this implementation. This function can be used in feature selection. Platform: |
Size: 1024 |
Author:Ahmed |
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Description: 这是色彩空间交叉变换后的直方图均衡化方法，通过对比结果可以看出直方图均衡化在色彩空间上的相互影响！-This is the cross after conversion of the color space histogram equalization method, it can be seen by comparing the results in mutual influence on the color space histogram equalization! Platform: |
Size: 1024 |
Author:贞跃 |
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Description: 分别通过帧平均法、直方图平均法、互信息量以及视觉内容匹配等方法提取视频中的关键帧-Respectively, through the frame averaging, histogram average method, mutual information and visual content matching method to extract the video key frame Platform: |
Size: 187392 |
Author:苏琪 |
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Description: 针对基于最大互信息图像配 准的不足 研究 了基于 H a r r ls角点算子的多模态医学图像配准。在计算互信 息的时候,采用部分体积插值法计算联合灰度直方 图-Based on the maximum mutual information for image registration based on the lack of H arr ls corner operator multimodal medical image registration. In calculating the mutual information when the partial volume interpolation method using joint histogram Platform: |
Size: 352256 |
Author:高嘉瑜 |
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Description: 反复试验，通过PV插值法，求得两幅图像的联合直方图和互信息-Trial and error, by PV interpolation, and seek joint histogram of two images and mutual information Platform: |
Size: 1024 |
Author:王羽 |
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Description: Functions for aligning images by rotation and translation:
im_reg_MI.m
MI2 - calculating Mutual information
joint_h - calculating Joint histogram
Mutual information is calculated using joint histogram calculation between two images. Platform: |
Size: 2048 |
Author:keyvan |
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Description: 求两幅图像的联合直方图，联合信息熵以及它们的互信息。直接运行即可。-takes two images of equal size and pixel value ranges and returns the joint histogram, joint entropy, and mutual information. Platform: |
Size: 2048 |
Author:zuoyujia |
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Description: 基于交叉累计剩余熵的图像配准中插值方法的改进
交叉累计剩余熵（CCRE）比传统互信息在配准强噪声图像时更具优势,但采用部分体积（PV）插值的CCRE在网格点容易产生局部极值,不利于变换参数的优化。针对该问题,研究基于3阶B样条函数的PV插值（BPV）、哈宁窗sinc函数的PV插值（HPV）和Blackman-Harris窗sinc函数的PV插值（BHPV）方法在CCRE中的应用,提出一种新的插值方法。该方法采用灵活的邻域中心,将插值点对联合直方图贡献的权重分散到临近的9个点上,并使用高斯函数作为PV插值的核函数,避免权重突变。实验结果表明,与BPV,HPV和BHPV插值方法相比,
-Improved cross cumulative residual entropy (CCRE) image registration interpolation method based on cross cumulative residual entropy of information advantage when strong noise image registration than traditional mutual, but with partial volume (PV) grid point interpolation CCRE prone to local extreme, is not conducive to transformation parameter optimization. To solve this problem, based on 3-order B-spline interpolation function PV (BPV), Hanning window sinc interpolation function PV (HPV) and Blackman-Harris window sinc interpolation function PV (BHPV) Application of CCRE, a new interpolation method. The method uses a flexible center of the neighborhood, the interpolation point joint histogram contribution weights dispersed to nine points nearby and use the Gaussian function as a PV interpolation kernel function, avoid weight changes. Experimental results show that compared with BPV, HPV and BHPV interpolation method, Platform: |
Size: 326656 |
Author:fangsm |
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Description: 计算互信息非常有用的一组程序,可以提取一幅图中想要的目标，计算时间和二维直方图。- Mutual information is useful to calculate a set of procedures, Target can be extracted in a picture you want, Computing time and two-dimensional histogram. Platform: |
Size: 5120 |
Author:fangleiyie |
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Search - mutual histogram