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Maria Vanrell, & Jordi Vitria. (1993). Mathematical Morphology, Granulometries and Texture Perception. In SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV)..
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Robert Benavente, Gemma Sanchez, Ramon Baldrich, Maria Vanrell, & Josep Llados. (2000). Normalized colour segmentation for human appearance description. In 15 th International Conference on Pattern Recognition (Vol. 3, pp. 637–641).
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X. Binefa, Jordi Vitria, & Maria Vanrell. (1992). Reconstruccion tridimensional de imagenes Microscopicas. In V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes.
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Francesc Tous, Agnes Borras, Robert Benavente, Ramon Baldrich, Maria Vanrell, & Josep Llados. (2002). Textual Descriptors for browsing people by visual appearence. In 5è. Congrés Català d’Intel·ligència Artificial CCIA.
Abstract: 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.
Keywords: Image retrieval, textual descriptors, colour naming, colour normalization, graph matching.
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Agnes Borras, Francesc Tous, Josep Llados, & Maria Vanrell. (2003). High-Level Clothes Description Based on Colour-Texture and Structural Features. In 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 (Vol. 2652, pp. 108–116). LNCS.
Abstract: ecture Notes in Computer Science 2652 108–116
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Anna Salvatella, Maria Vanrell, & Ramon Baldrich. (2003). Subtexture Components for Texture Description. In 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 (Vol. 2652, pp. 884–892). LNCS.
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Anna Salvatella, Maria Vanrell, & Juan J. Villanueva. (2003). Texture Description based on Subtexture Components, 3rd International Workshop on Texture Syntesis and Analysis. In 3rd International Workshop on Texture Synthesis and Analysis, (77–82).
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Fernando Lopez, J.M. Valiente, Ramon Baldrich, & Maria Vanrell. (2005). Fast surface grading using color statistics in the CIELab space. In Pattern Recognition and Image Analysis. IbPRIA 2005 (Vol. LNCS 3523, pp. 66–673).
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Javier Vazquez, Maria Vanrell, & Ramon Baldrich. (2008). Towards a Psychophysical Evaluation of Colour Constancy Algorithms. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (372–377).
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C. Alejandro Parraga, Robert Benavente, Maria Vanrell, & Ramon Baldrich. (2008). Modelling Inter-Colour Regions of Colour Naming Space. In 4th European Conference on Colour in Graphics, Imaging and Vision Proceedings (218–222).
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Fahad Shahbaz Khan, Joost Van de Weijer, & Maria Vanrell. (2009). Top-Down Color Attention for Object Recognition. In 12th International Conference on Computer Vision (pp. 979–986).
Abstract: Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
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Jaime Moreno, Xavier Otazu, & Maria Vanrell. (2010). Local Perceptual Weighting in JPEG2000 for Color Images. In 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science (255–260).
Abstract: 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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Jaime Moreno, Xavier Otazu, & Maria Vanrell. (2010). Contribution of CIWaM in JPEG2000 Quantization for Color Images. In Proceedings of The CREATE 2010 Conference (132–136).
Abstract: 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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C. Alejandro Parraga, Ramon Baldrich, & Maria Vanrell. (2010). Accurate Mapping of Natural Scenes Radiance to Cone Activation Space: A New Image Dataset. In 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science (50–57).
Abstract: 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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