University of Cambridge > > Cambridge Image Analysis Seminars > Dimension reduction into superpixels for image analysis and processing

Dimension reduction into superpixels for image analysis and processing

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact AI Aviles-Rivero.

Superpixels locally group pixels into regions and have become very popular in image processing and computer vision applications. The aim is to exploit the local redundancy of information to lower the computational burden and to potentially improve the performances by reducing the noise of a processing at the pixel level. Superpixels can also be considered as a multi-resolution app roach that preserves image contours, contrary to standard regular down-sampling methods. The presentation will first focus on the superpixel decomposition process itself. We answer to the limitations of state-of-the-art methods that may still highly fail to produce accurate pixel clustering in the presence of object contours, noise, or textures. Then, we propose a new framework adapting standard regular patch-based approaches, e.g., non-local means, to these irregular regions. Results on several applications such as segmentation, classification and color transfer will be presented.

This talk is part of the Cambridge Image Analysis Seminars series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2023, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity