'A modern approach set' diaporamas de présentation

Feature Detection

Feature Detection. Image features. Global features global properties of an image, including intensity histogram, frequency domain descriptors, covariance matrix and high order statistics, etc. Local features

1.22k views • 107 slides

Clustering Techniques K-Means & EM

Clustering Techniques K-Means & EM. Mario Haddad. What is Clustering?. Cluster: a collection of data objects Similar to one another within the same cluster Dissimilar to the objects in other clusters Clustering is unsupervised classification : no predefined classes Typical applications

720 views • 56 slides

Missing variable problems

Missing variable problems. In many vision problems, if some variables were known the maximum likelihood inference problem would be easy fitting; if we knew which line each token came from, it would be easy to determine line parameters

489 views • 33 slides

Gradients and edges

Points of sharp change in an image are interesting: change in reflectance change in object change in illumination noise Sometimes called edge points. General strategy determine image gradient

484 views • 33 slides

Radiometry

463 views • 20 slides