Global ETD Search

Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.

Results

Showing 1 to 4 of 4 for “"Co-segmentation"”.

  1. Image Co-saliency Detection and Co-segmentation from The Perspective of Commonalities

    Image co-saliency detection and image co-segmentation aim to identify the common salient objects and extract them in a group of images. Image co-saliency detection and image co-segmentation are important for many content-based applications such as image retrieval, image editing, and content aware …

    uts Repository record for Image Co-saliency Detection and Co-segmentation from The Perspective of Commonalities (opens in a new tab)

  2. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming

    … for solving the discrete optimization problem in computer vision. Much of the work is inspired by the study of the image co-segmentation problem. It is through the research on this topic that the author has become very familiar with the graphical model and energy minimization point of view in …

    uiuc Repository record for From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming (opens in a new tab)

  3. Bayesian Latent Class Models

    … variables. The main advantage of LCM is that conditional on latent variables, the manifest variables are mutually independent of each other. In some scenarios, the LCM makes the modeling or computation feasible. In some other scenarios, the latent variables themselves are key. In the past a …

    uiuc Repository record for Bayesian Latent Class Models (opens in a new tab)

  4. Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics

    … this thesis, temporal and spatial dependence are considered within nonparametric priors to help infer patterns, clusters or segments in data. In traditional nonparametric mixture models, observations are usually assumed exchangeable, even though dependence often exists associated with the space or …

    duke Repository record for Modeling Temporal and Spatial Data Dependence with Bayesian Nonparametrics (opens in a new tab)