Given an ensemble with multiple object classes, the authors propose an approach to solve two problems automatically and simultaneously, image alignment and clustering.
Joint alignment for an image ensemble can rectify images in the spatial domain such that the aligned images are as similar to each other as possible. This important technology has been applied to various object classes and medical applications; however, previous approaches to joint alignment work on an ensemble of a single object class. In the authors’ approach, both the alignment parameters and clustering parameters are formulated into a unified objective function, whose optimization leads to an unsupervised joint estimation approach. It is further extended to semi-supervised simultaneous estimation where a few labeled images are provided. Extensive experiments on diverse real-world databases demonstrated the capabilities of the authors’ work on this challenging problem. (Publisher abstract provided)
Popular TopicsCluster analysis
- Chemometric Processing of DART-HRMS-derived Dark Matter for the Identification of New Psychoactive Substances
- Pixel-wise structured light calibration method with a color calibration target
- Revealing the presence of tryptamine new psychoactive substances using fused "neutral loss" spectra derived from DART high-resolution mass spectra