Image Understanding and Processing

Image Understanding and Processing

Details

An artificial vision system should be able to understand the content of an image. From a lower level, image content includes different types of textures, shapes/forms, and illumination in the scene, while at a higher level objects, scene type (outdoor, natural, etc.) and their relations need to be considered. Beyond human level understanding of visual data, artificial vision systems should be able to process and synthesize visual information in order to improve the productivity of artists, architects, engineers, and humans in general. This includes many important tasks including deblurring/denoising pictures, assisting in the completion or enhancement of photos, and keeping track of building construction progress to name  a few.​ Below is a selected set of publications: 
 

Selected Publications



 


 On the Relationship between Visual Attributes and Convolutional Networks




 

 

 L0TV : A New Method for Image Restoration in the Presence of Impulse Noise





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Publications

  1. Ganzhao Yuan and Bernard Ghanem, “L0TV : A New Method for Image Restoration in the Presence of Impulse Noise”, Conference on Computer Vision and Pattern Recognition (CVPR 2015)
  2. Victor Castillo, Juan Carlos Niebles, and Bernard Ghanem, “On the Relationship between Visual Attributes and Convolutional Networks”, Conference on Computer Vision and Pattern Recognition (CVPR 2015)
  3. Alexandre Heili, Jagannadan Varadarajan, Bernard Ghanem, Narendra Ahuja, Jean-Marc Odobez, "Improving Head and Body Pose Estimation through Semi-Supervised Manifold Alignment", International Conference on Image Processing (ICIP 2014)
  4. Tianzhu Zhang, Bernard Ghanem, Si Liu, and Narendra Ahuja, "Low-Rank Sparse Coding for Image Classification", International Conference on Computer Vision (ICCV 2013)