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Author Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin edit  url
openurl 
  Title Towards automatic and flexible concept transfer Type Journal Article
  Year 2012 Publication Computers and Graphics Abbreviated Journal CG  
  Volume 36 Issue 6 Pages 622–634  
  Keywords  
  Abstract (down) This paper introduces a novel approach to automatic, yet flexible, image concepttransfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The presented method modifies the color content of an input image given only a concept specified by a user in natural language, thereby requiring minimal user input. This method is particularly useful for users who are aware of the message they wish to convey in the transferred image while being unsure of the color combination needed to achieve the corresponding transfer. Our framework is flexible for two reasons. First, the user may select one of two modalities to map input image chromaticities to target concept chromaticities depending on the level of photo-realism required. Second, the user may adjust the intensity level of the concepttransfer to his/her liking with a single parameter. The proposed method uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. Results show that our approach yields transferred images which effectively represent concepts as confirmed by a user study.  
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  Series Volume Series Issue Edition  
  ISSN 0097-8493 ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ MSM2012 Serial 2002  
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Author Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell edit   pdf
url  doi
openurl 
  Title Spectral sharpening by spherical sampling Type Journal Article
  Year 2012 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 29 Issue 7 Pages 1199-1210  
  Keywords  
  Abstract (down) There are many works in color that assume illumination change can be modeled by multiplying sensor responses by individual scaling factors. The early research in this area is sometimes grouped under the heading “von Kries adaptation”: the scaling factors are applied to the cone responses. In more recent studies, both in psychophysics and in computational analysis, it has been proposed that scaling factors should be applied to linear combinations of the cones that have narrower support: they should be applied to the so-called “sharp sensors.” In this paper, we generalize the computational approach to spectral sharpening in three important ways. First, we introduce spherical sampling as a tool that allows us to enumerate in a principled way all linear combinations of the cones. This allows us to, second, find the optimal sharp sensors that minimize a variety of error measures including CIE Delta E (previous work on spectral sharpening minimized RMS) and color ratio stability. Lastly, we extend the spherical sampling paradigm to the multispectral case. Here the objective is to model the interaction of light and surface in terms of color signal spectra. Spherical sampling is shown to improve on the state of the art.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1084-7529 ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ FVS2012 Serial 2000  
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Author Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell edit   pdf
url  doi
openurl 
  Title Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures Type Journal Article
  Year 2011 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 33 Issue 5 Pages 917-930  
  Keywords  
  Abstract (down) The segmentation of a single material reflectance is a challenging problem due to the considerable variation in image measurements caused by the geometry of the object, shadows, and specularities. The combination of these effects has been modeled by the dichromatic reflection model. However, the application of the model to real-world images is limited due to unknown acquisition parameters and compression artifacts. In this paper, we present a robust model for the shape of a single material reflectance in histogram space. The method is based on a multilocal creaseness analysis of the histogram which results in a set of ridges representing the material reflectances. The segmentation method derived from these ridges is robust to both shadow, shading and specularities, and texture in real-world images. We further complete the method by incorporating prior knowledge from image statistics, and incorporate spatial coherence by using multiscale color contrast information. Results obtained show that our method clearly outperforms state-of-the-art segmentation methods on a widely used segmentation benchmark, having as a main characteristic its excellent performance in the presence of shadows and highlights at low computational cost.  
  Address Los Alamitos; CA; USA;  
  Corporate Author Thesis  
  Publisher IEEE Computer Society Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes CIC Approved no  
  Call Number Admin @ si @ VBW2011 Serial 1715  
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Author Olivier Penacchio; C. Alejandro Parraga edit  url
openurl 
  Title What is the best criterion for an efficient design of retinal photoreceptor mosaics? Type Journal Article
  Year 2011 Publication Perception Abbreviated Journal PER  
  Volume 40 Issue Pages 197  
  Keywords  
  Abstract (down) The proportions of L, M and S photoreceptors in the primate retina are arguably determined by evolutionary pressure and the statistics of the visual environment. Two information theory-based approaches have been recently proposed for explaining the asymmetrical spatial densities of photoreceptors in humans. In the first approach Garrigan et al (2010 PLoS ONE 6 e1000677), a model for computing the information transmitted by cone arrays which considers the differential blurring produced by the long-wavelength accommodation of the eye’s lens is proposed. Their results explain the sparsity of S-cones but the optimum depends weakly on the L:M cone ratio. In the second approach (Penacchio et al, 2010 Perception 39 ECVP Supplement, 101), we show that human cone arrays make the visual representation scale-invariant, allowing the total entropy of the signal to be preserved while decreasing individual neurons’ entropy in further retinotopic representations. This criterion provides a thorough description of the distribution of L:M cone ratios and does not depend on differential blurring of the signal by the lens. Here, we investigate the similarities and differences of both approaches when applied to the same database. Our results support a 2-criteria optimization in the space of cone ratios whose components are arguably important and mostly unrelated.
[This work was partially funded by projects TIN2010-21771-C02-1 and Consolider-Ingenio 2010-CSD2007-00018 from the Spanish MICINN. CAP was funded by grant RYC-2007-00484]
 
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  Notes CIC Approved no  
  Call Number Admin @ si @ PeP2011a Serial 1719  
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Author Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias edit   pdf
url  openurl
  Title Understanding trained CNNs by indexing neuron selectivity Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 318-325  
  Keywords  
  Abstract (down) The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.  
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  Notes CIC; 600.087; 600.140; 600.118 Approved no  
  Call Number Admin @ si @ RVL2019 Serial 3310  
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