'Gradient orientation' diaporamas de présentation

Edward J. Delp

The Analysis of Digital Mammograms: Spiculated Tumor Detection and Normal Mammogram Characterization. Edward J. Delp. Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory ( VIPER) West Lafayette, Indiana, USA ace@ecn.purdue.edu

756 views • 61 slides

Edward J. Delp

The Analysis of Digital Mammograms: Spiculated Tumor Detection and Normal Mammogram Characterization. Edward J. Delp. Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory ( VIPER) West Lafayette, Indiana, USA ace@ecn.purdue.edu

618 views • 61 slides

Learning Local Affine Representations for Texture and Object Recognition

Learning Local Affine Representations for Texture and Object Recognition. Svetlana Lazebnik Beckman Institute, University of Illinois at Urbana-Champaign (joint work with Cordelia Schmid, Jean Ponce). Overview. Goal: Recognition of 3D textured surfaces, object classes Our contribution:

659 views • 49 slides

Fitting: The Hough transform

520 views • 39 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

479 views • 33 slides

Multiple Object Class Detection with a Generative Model

Multiple Object Class Detection with a Generative Model. K. Mikolajczyk, B. Leibe and B. Schiele. Carolina Galleguillos. Goal. Simultaneous recognition and localization of multiple object classes using a generative model.

478 views • 21 slides