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Author Xavier Otazu; Maria Vanrell; C. Alejandro Parraga edit  openurl
  Title Multiresolution Wavelet Framework Models Brightness Induction Effects Type Journal
  Year 2008 Publication (down) Vision Research Abbreviated Journal VR  
  Volume 48 Issue 5 Pages 733–751  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ OVP2008a Serial 927  
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Author Ivet Rafegas; Maria Vanrell edit   pdf
url  doi
openurl 
  Title Color encoding in biologically-inspired convolutional neural networks Type Journal Article
  Year 2018 Publication (down) Vision Research Abbreviated Journal VR  
  Volume 151 Issue Pages 7-17  
  Keywords Color coding; Computer vision; Deep learning; Convolutional neural networks  
  Abstract Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations.  
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  Notes CIC; 600.051; 600.087 Approved no  
  Call Number Admin @ si @RaV2018 Serial 3114  
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Author Yasuko Sugito; Trevor Canham; Javier Vazquez; Marcelo Bertalmio edit  url
doi  openurl
  Title A Study of Objective Quality Metrics for HLG-Based HDR/WCG Image Coding Type Journal
  Year 2021 Publication (down) SMPTE Motion Imaging Journal Abbreviated Journal SMPTE  
  Volume 130 Issue 4 Pages 53 - 65  
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  Abstract In this work, we study the suitability of high dynamic range, wide color gamut (HDR/WCG) objective quality metrics to assess the perceived deterioration of compressed images encoded using the hybrid log-gamma (HLG) method, which is the standard for HDR television. Several image quality metrics have been developed to deal specifically with HDR content, although in previous work we showed that the best results (i.e., better matches to the opinion of human expert observers) are obtained by an HDR metric that consists simply in applying a given standard dynamic range metric, called visual information fidelity (VIF), directly to HLG-encoded images. However, all these HDR metrics ignore the chroma components for their calculations, that is, they consider only the luminance channel. For this reason, in the current work, we conduct subjective evaluation experiments in a professional setting using compressed HDR/WCG images encoded with HLG and analyze the ability of the best HDR metric to detect perceivable distortions in the chroma components, as well as the suitability of popular color metrics (including ΔITPR , which supports parameters for HLG) to correlate with the opinion scores. Our first contribution is to show that there is a need to consider the chroma components in HDR metrics, as there are color distortions that subjects perceive but that the best HDR metric fails to detect. Our second contribution is the surprising result that VIF, which utilizes only the luminance channel, correlates much better with the subjective evaluation scores than the metrics investigated that do consider the color components.  
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  Notes CIC Approved no  
  Call Number SCV2021 Serial 3671  
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Author O. Fors; J. Nuñez; Xavier Otazu; A. Prades; Robert D. Cardinal edit  doi
openurl 
  Title Improving the Ability of Image Sensors to Detect Faint Stars and Moving Objects Using Image Deconvolution Techniques Type Journal Article
  Year 2010 Publication (down) Sensors Abbreviated Journal SENS  
  Volume 10 Issue 3 Pages 1743–1752  
  Keywords image processing; image deconvolution; faint stars; space debris; wavelet transform  
  Abstract Abstract: In this paper we show how the techniques of image deconvolution can increase the ability of image sensors as, for example, CCD imagers, to detect faint stars or faint orbital objects (small satellites and space debris). In the case of faint stars, we show that this benefit is equivalent to double the quantum efficiency of the used image sensor or to increase the effective telescope aperture by more than 30% without decreasing the astrometric precision or introducing artificial bias. In the case of orbital objects, the deconvolution technique can double the signal-to-noise ratio of the image, which helps to discover and control dangerous objects as space debris or lost satellites. The benefits obtained using CCD detectors can be extrapolated to any kind of image sensors.  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ FNO2010 Serial 1285  
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Author J. Martinez; Eva Costa; P. Herreros; F. Javier Sanchez; Ramon Baldrich edit  openurl
  Title A Modular and Scalable Architecture for PC-Based Real-Time Vision Systems Type Journal
  Year 2003 Publication (down) Real–Time Imaging, 9: 99–112 (IF: 0.512) Abbreviated Journal  
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  Notes CIC Approved no  
  Call Number CAT @ cat @ MCH2003b Serial 394  
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