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| Author |
Susana Alvarez; Maria Vanrell |
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| Title |
Texton theory revisited: a bag-of-words approach to combine textons |
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Journal Article |
| Year |
2012 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
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| Volume |
45 |
Issue |
12 |
Pages |
4312-4325 |
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Abstract  |
The aim of this paper is to revisit an old theory of texture perception and
update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these lowdimensionaltexton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets. |
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0031-3203 |
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Admin @ si @ AlV2012a |
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2130 |
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| Author |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
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| Title |
Local Perceptual Weighting in JPEG2000 for Color Images |
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Conference Article |
| Year |
2010 |
Publication |
5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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255–260 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM (Chromatic Induction Wavelet Model). |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CAT @ cat @ MOV2010a |
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1307 |
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| Author |
Jaime Moreno; Xavier Otazu; Maria Vanrell |
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| Title |
Contribution of CIWaM in JPEG2000 Quantization for Color Images |
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Conference Article |
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2010 |
Publication |
Proceedings of The CREATE 2010 Conference |
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132–136 |
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The aim of this work is to explain how to apply perceptual concepts to define a perceptual pre-quantizer and to improve JPEG2000 compressor. The approach consists in quantizing wavelet transform coefficients using some of the human visual system behavior properties. Noise is fatal to image compression performance, because it can be both annoying for the observer and consumes excessive bandwidth when the imagery is transmitted. Perceptual pre-quantization reduces unperceivable details and thus improve both visual impression and transmission properties. The comparison between JPEG2000 without and with perceptual pre-quantization shows that the latter is not favorable in PSNR, but the recovered image is more compressed at the same or even better visual quality measured with a weighted PSNR. Perceptual criteria were taken from the CIWaM(ChromaticInductionWaveletModel). |
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Gjovik (Norway) |
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CREATE |
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CAT @ cat @ MOV2010b |
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1308 |
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| Author |
C. Alejandro Parraga; Olivier Penacchio; Maria Vanrell |
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| Title |
Retinal Filtering Matches Natural Image Statistics at Low Luminance Levels |
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Journal Article |
| Year |
2011 |
Publication |
Perception |
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PER |
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| Volume |
40 |
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96 |
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The assumption that the retina’s main objective is to provide a minimum entropy representation to higher visual areas (ie efficient coding principle) allows to predict retinal filtering in space–time and colour (Atick, 1992 Network 3 213–251). This is achieved by considering the power spectra of natural images (which is proportional to 1/f2) and the suppression of retinal and image noise. However, most studies consider images within a limited range of lighting conditions (eg near noon) whereas the visual system’s spatial filtering depends on light intensity and the spatiochromatic properties of natural scenes depend of the time of the day. Here, we explore whether the dependence of visual spatial filtering on luminance match the changes in power spectrum of natural scenes at different times of the day. Using human cone-activation based naturalistic stimuli (from the Barcelona Calibrated Images Database), we show that for a range of luminance levels, the shape of the retinal CSF reflects the slope of the power spectrum at low spatial frequencies. Accordingly, the retina implements the filtering which best decorrelates the input signal at every luminance level. This result is in line with the body of work that places efficient coding as a guiding neural principle. |
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Admin @ si @ PPV2011 |
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1720 |
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| Author |
C. Alejandro Parraga; Ramon Baldrich; Maria Vanrell |
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| Title |
Accurate Mapping of Natural Scenes Radiance to Cone Activation Space: A New Image Dataset |
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Conference Article |
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2010 |
Publication |
5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science |
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50–57 |
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The characterization of trichromatic cameras is usually done in terms of a device-independent color space, such as the CIE 1931 XYZ space. This is indeed convenient since it allows the testing of results against colorimetric measures. We have characterized our camera to represent human cone activation by mapping the camera sensor's (RGB) responses to human (LMS) through a polynomial transformation, which can be “customized” according to the types of scenes we want to represent. Here we present a method to test the accuracy of the camera measures and a study on how the choice of training reflectances for the polynomial may alter the results. |
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Joensuu, Finland |
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9781617388897 |
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CGIV/MCS |
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CIC |
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CAT @ cat @ PBV2010a |
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1322 |
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| Author |
Javier Vazquez; C. Alejandro Parraga; Maria Vanrell; Ramon Baldrich |
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| Title |
Color Constancy Algorithms: Psychophysical Evaluation on a New Dataset |
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Journal Article |
| Year |
2009 |
Publication |
Journal of Imaging Science and Technology |
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53 |
Issue |
3 |
Pages |
031105–9 |
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The estimation of the illuminant of a scene from a digital image has been the goal of a large amount of research in computer vision. Color constancy algorithms have dealt with this problem by defining different heuristics to select a unique solution from within the feasible set. The performance of these algorithms has shown that there is still a long way to go to globally solve this problem as a preliminary step in computer vision. In general, performance evaluation has been done by comparing the angular error between the estimated chromaticity and the chromaticity of a canonical illuminant, which is highly dependent on the image dataset. Recently, some workers have used high-level constraints to estimate illuminants; in this case selection is based on increasing the performance on the subsequent steps of the systems. In this paper we propose a new performance measure, the perceptual angular error. It evaluates the performance of a color constancy algorithm according to the perceptual preferences of humans, or naturalness (instead of the actual optimal solution) and is independent of the visual task. We show the results of a new psychophysical experiment comparing solutions from three different color constancy algorithms. Our results show that in more than a half of the judgments the preferred solution is not the one closest to the optimal solution. Our experiments were performed on a new dataset of images acquired with a calibrated camera with an attached neutral grey sphere, which better copes with the illuminant variations of the scene. |
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CIC |
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CAT @ cat @ VPV2009a |
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1171 |
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| Author |
Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias |
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| Title |
Understanding trained CNNs by indexing neuron selectivity |
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Journal Article |
| Year |
2020 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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| Volume |
136 |
Issue |
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Pages |
318-325 |
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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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CIC; 600.087; 600.140; 600.118 |
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Admin @ si @ RVL2019 |
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3310 |
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| Author |
Maria Vanrell; Naila Murray; Robert Benavente; C. Alejandro Parraga; Xavier Otazu; Ramon Baldrich |
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| Title |
Perception Based Representations for Computational Colour |
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Conference Article |
| Year |
2011 |
Publication |
3rd International Workshop on Computational Color Imaging |
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6626 |
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16-30 |
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colour perception, induction, naming, psychophysical data, saliency, segmentation |
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The perceived colour of a stimulus is dependent on multiple factors stemming out either from the context of the stimulus or idiosyncrasies of the observer. The complexity involved in combining these multiple effects is the main reason for the gap between classical calibrated colour spaces from colour science and colour representations used in computer vision, where colour is just one more visual cue immersed in a digital image where surfaces, shadows and illuminants interact seemingly out of control. With the aim to advance a few steps towards bridging this gap we present some results on computational representations of colour for computer vision. They have been developed by introducing perceptual considerations derived from the interaction of the colour of a point with its context. We show some techniques to represent the colour of a point influenced by assimilation and contrast effects due to the image surround and we show some results on how colour saliency can be derived in real images. We outline a model for automatic assignment of colour names to image points directly trained on psychophysical data. We show how colour segments can be perceptually grouped in the image by imposing shading coherence in the colour space. |
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Milan, Italy |
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Springer-Verlag |
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Raimondo Schettini, Shoji Tominaga, Alain Trémeau |
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978-3-642-20403-6 |
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CCIW |
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CIC |
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Admin @ si @ VMB2011 |
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1733 |
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| Author |
Eduard Vazquez; Ramon Baldrich; Joost Van de Weijer; Maria Vanrell |
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| Title |
Describing Reflectances for Colour Segmentation Robust to Shadows, Highlights and Textures |
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Journal Article |
| Year |
2011 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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33 |
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5 |
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917-930 |
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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. |
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Los Alamitos; CA; USA; |
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IEEE Computer Society |
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0162-8828 |
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CIC |
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Admin @ si @ VBW2011 |
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1715 |
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Jordi Roca; C. Alejandro Parraga; Maria Vanrell |
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| Title |
Categorical Focal Colours are Structurally Invariant Under Illuminant Changes |
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Conference Article |
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2011 |
Publication |
European Conference on Visual Perception |
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196 |
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The visual system perceives the colour of surfaces approximately constant under changes of illumination. In this work, we investigate how stable is the perception of categorical \“focal\” colours and their interrelations with varying illuminants and simple chromatic backgrounds. It has been proposed that best examples of colour categories across languages cluster in small regions of the colour space and are restricted to a set of 11 basic terms (Kay and Regier, 2003 Proceedings of the National Academy of Sciences of the USA 100 9085\–9089). Following this, we developed a psychophysical paradigm that exploits the ability of subjects to reliably reproduce the most representative examples of each category, adjusting multiple test patches embedded in a coloured Mondrian. The experiment was run on a CRT monitor (inside a dark room) under various simulated illuminants. We modelled the recorded data for each subject and adapted state as a 3D interconnected structure (graph) in Lab space. The graph nodes were the subject\’s focal colours at each adaptation state. The model allowed us to get a better distance measure between focal structures under different illuminants. We found that perceptual focal structures tend to be preserved better than the structures of the physical \“ideal\” colours under illuminant changes. |
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Perception 40 |
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ECVP |
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Admin @ si @ RPV2011 |
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1867 |
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Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell |
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Spectral sharpening by spherical sampling |
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Journal Article |
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2012 |
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Journal of the Optical Society of America A |
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JOSA A |
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29 |
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7 |
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1199-1210 |
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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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Admin @ si @ FVS2012 |
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2000 |
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Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Textual Descriptors for browsing people by visual appearence. |
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Conference Article |
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2002 |
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5è. Congrés Català d’Intel·ligència Artificial CCIA |
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Image retrieval, textual descriptors, colour naming, colour normalization, graph matching. |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building. |
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DAG;CIC |
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CAT @ cat @ TBB2002a |
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287 |
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Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Textual Descriptions for Browsing People by Visual Apperance. |
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Book Chapter |
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2002 |
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Lecture Notes in Artificial Intelligence |
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2504 |
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419-429 |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building |
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Springer Verlag |
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DAG;CIC |
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| Call Number |
CAT @ cat @ TBB2002b |
Serial |
319 |
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| Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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| Title |
High-Level Clothes Description Based on Color-Texture and Structural Features |
Type |
Book Chapter |
| Year |
2003 |
Publication |
Lecture Notes in Computer Science |
Abbreviated Journal |
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| Volume |
2652 |
Issue |
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Pages |
108–116 |
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| Abstract |
This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. |
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| Address |
Springer-Verlag |
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Expedition |
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Conference |
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| Notes |
DAG;CIC |
Approved |
no |
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| Call Number |
CAT @ cat @ BTL2003a |
Serial |
368 |
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| Permanent link to this record |
