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Full-Text Articles in Computer Sciences
Unsupervised Joint Alignment, Clustering And Feature Learning, Mohamed Marwan Mattar
Unsupervised Joint Alignment, Clustering And Feature Learning, Mohamed Marwan Mattar
Doctoral Dissertations
Joint alignment is the process of transforming instances in a data set to make them more similar based on a pre-defined measure of joint similarity. This process has great utility and applicability in many scientific disciplines including radiology, psychology, linguistics, vision, and biology. Most alignment algorithms suffer from two shortcomings. First, they typically fail when presented with complex data sets arising from multiple modalities such as a data set of normal and abnormal heart signals. Second, they require hand-picking appropriate feature representations for each data set, which may be time-consuming and ineffective, or outside the domain of expertise for practitioners. …
Incorporating Boltzmann Machine Priors For Semantic Labeling In Images And Videos, Andrew Kae
Incorporating Boltzmann Machine Priors For Semantic Labeling In Images And Videos, Andrew Kae
Doctoral Dissertations
Semantic labeling is the task of assigning category labels to regions in an image. For example, a scene may consist of regions corresponding to categories such as sky, water, and ground, or parts of a face such as eyes, nose, and mouth. Semantic labeling is an important mid-level vision task for grouping and organizing image regions into coherent parts. Labeling these regions allows us to better understand the scene itself as well as properties of the objects in the scene, such as their parts, location, and interaction within the scene. Typical approaches for this task include the conditional random field …