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The structural similarity index measure (SSIM) is a method for predicting the perceived quality of digital television and cinematic pictures, as well as other kinds of digital images and videos. It is also used for measuring the similarity between two images.
DVC can be performed using volumetric image datasets. These images can be obtained using confocal microscopy, X-ray computed tomography, Magnetic Resonance Imaging or other techniques. Similar to the other DIC techniques, the images must exhibit a distinct, high-contrast 3D "speckle pattern" to ensure accurate displacement measurement. [10]
Similarity learning can often outperform predefined similarity measures. Broadly speaking, these approaches build a statistical model of documents, and use it to estimate similarity. LSA (latent semantic analysis): [44] [45] (+) vector-based, adds vectors to measure multi-word terms; (−) non-incremental vocabulary, long pre-processing times
These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition, the generation of disparity maps for stereo images, and motion estimation for video ...
A similarity measure can take many different forms depending on the type of data being clustered and the specific problem being solved. One of the most commonly used similarity measures is the Euclidean distance, which is used in many clustering techniques including K-means clustering and Hierarchical clustering. The Euclidean distance is a ...
In other contexts, where 0 and 1 carry equivalent information (symmetry), the SMC is a better measure of similarity. For example, vectors of demographic variables stored in dummy variables , such as gender, would be better compared with the SMC than with the Jaccard index since the impact of gender on similarity should be equal, independently ...