LAMP

Category: Publications

  • Learning Illuminant Estimation from Object Recognition

    Marco Buzzelli, Joost van de Weijer, Raimondo Schettini Read Full Paper → In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, […]

  • Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification

    Rao Muhammad Anwer, Fahad Shahbaz Khan, Joost van de Weijer, Matthieu Molinier, Jorma Laaksonen Read Full Paper → Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless […]

  • Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

    Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images , we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same […]

  • On the Duality Between Retinex and Image Dehazing

    Adrian Galdran, Aitor Alvarez-Gila, Alessandro Bria, Javier Vazquez-Corral, Marcelo Bertalmio Read Full Paper → Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different […]

  • Objects as context for detecting their semantic parts

    Abel Gonzalez-Garcia, Davide Modolo, Vittorio Ferrari Read Full Paper → We present a semantic part detection approach that effectively leverages object information.We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a […]

  • Beyond Eleven Color Names for Image Understanding

    Lu Yu, Lichao Zhang, Joost van de Weijer, Fahad Shahbaz Khan, Yongmei Cheng & C. Alejandro Parraga Read Full Paper → Color description is one of the fundamental problems of image understanding. One of the popular ways to represent colors is by means of color names. Most existing work on color names focuses on only the eleven basic color terms of […]

  • Review on computer vision techniques in emergency situations

    Laura Lopez-Fuentes, Joost van de Weijer, Manuel González-Hidalgo, Harald Skinnemoen & Andrew D. Bagdanov Read Full Paper → In emergency situations, actions that save lives and limit the impact of hazards are crucial. In order to act, situational awareness is needed to decide what to do. Geolocalized photos and video of the situations as they evolve can be crucial in […]

  • Adversarial Networks for Spatial Context-Aware Spectral Image Reconstruction from RGB

    Aitor Alvarez-Gila, Joost van de Weijer, Estibaliz Garrote Read Full Paper → Hyperspectral signal reconstruction aims at recovering the original spectral input that produced a certain trichromatic (RGB) response from a capturing device or observer. Given the heavily underconstrained, non-linear nature of the problem, traditional techniques leverage different statistical properties of the spectral signal in order to […]

  • RankIQA: Learning from Rankings for No-reference Image Quality Assessment

    Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov Read Full Paper → We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative […]

  • Domain-adaptive deep network compression

    Marc Masana, Joost van de Weijer, Luis Herranz, Andrew D. Bagdanov, Jose M Alvarez Read Full Paper → Deep Neural Networks trained on large datasets can be easily transferred to new domains with far fewer labeled examples by a process called fine-tuning. This has the advantage that representations learned in the large source domain can be exploited on smaller […]