Now showing items 1-4 of 4

  • Dense segmentation-aware descriptors 

    Trulls Fortuny, Eduard; Kokkinos, Iasonas; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (2013)
    Conference report
    Restricted access - publisher's policy
    In this work we exploit segmentation to construct appearance descriptors that can robustly deal with occlusion and background changes. For this, we downplay measurements coming from areas that are unlikely to belong to the ...
  • Dense segmentation-aware descriptors 

    Trulls Fortuny, Eduard; Kokkinos, Iasonas; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (Springer, 2016)
    Part of book or chapter of book
    Restricted access - publisher's policy
    Dense descriptors are becoming increasingly popular in a host of tasks, such as dense image correspondence, bag-of-words image classification, and label transfer. However, the extraction of descriptors on generic image ...
  • Discriminative learning of deep convolutional feature point descriptors 

    Simó Serra, Edgar; Trulls Fortuny, Eduard; Ferraz, Luis; Kokkinos, Iasonas; Fua, Pascal; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2015)
    Conference report
    Open Access
    Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e.g. SIFT. In this paper we use Convolutional Neural Networks ...
  • Segmentation-aware deformable part models 

    Tsogkas, Stavros; Kokkinos, Iasonas; Trulls Fortuny, Eduard; Sanfeliu Cortés, Alberto; Moreno-Noguer, Francesc (Institute of Electrical and Electronics Engineers (IEEE), 2014)
    Conference report
    Open Access
    In this work we propose a technique to combine bottom- up segmentation, coming in the form of SLIC superpixels, with sliding window detectors, such as Deformable Part Models (DPMs). The merit of our approach lies in ...