The model used for establishing the correspondences, based on the calculus of variations, is itself considered robust.
Several methods, including random sample consensus (RANSAC) [44], Hough transform [28, 45], and graph transform matching (GTM) [30] refine initial correspondences based on SIFT.
To cope with this problem, built upon the research of CRIPAC-MCT [51], Javed et al. [53] further adopt kernel density estimator to estimate the inter-camera space-time probabilities through computing the (e.g., walking) transition time values between pairs of correct correspondences based on the difference between the entry and exit time stamps.
This decision is made through the analysis of several criteria assessing the skull-face anatomical correspondence based on the resulting skull-face overlay.
This correspondence, based on the equivalences above, is at the heart of the correspondence theory between modal and classical logic, which offers a systematic treatment of various aspects of temporal logic such as expressiveness, definability, and model theory with the tools and techniques of classical logic.
Briefly, to estimate the expression similarity between orthologs A and B, we compute the expression-similarity-vector E(A) representing similarity of A's expression with all other genes in the same species (likewise E(B)) and compute the correlation between E(A) and E B) where the indices of the two vectors have 1-to-1 correspondence based on orthology relationships.
We computed correspondence based on (1) full ATC code agreement, that is, correspondence of the entire ATC code, and (2) therapeutic subgroup agreement, defined as correspondence of the first three ATC code positions (eg, A02).
The results of a statistical test of QTL correspondence based on the hypergeometric probability function were not significant, however, suggesting that the co-location of QTL controlling these two traits may merely be due to chance.
Semantic matching techniques search for correspondences based not only on the textual information associated to a concept (e.g. description) but also on the associative relationships between concepts (e.g. subclass, 'is-a') (7).
Correspondences were based on lexical similarity (i.e. same name or label of the class, or of its synonyms when present) with additional evidence to avoid blindly matching homonyms (see 'Discussion' section), physical/structural similarity, evidence from definitions, evidence from figures in referenced texts and, sometimes, based on class relations such as subsumption or property restrictions.
