S. Garcia, Dani Rowe, Jordi Gonzalez, & Juan J. Villanueva. (2005). Articulated Object Modelling Using Neural Gas Networks.
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1998). Creaseness form level set extrinsec curvature..
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David Augusto Rojas, Joost Van de Weijer, & Theo Gevers. (2010). Color Edge Saliency Boosting using Natural Image Statistics. In 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science (228–234).
Abstract: State of the art methods for image matching, content-based retrieval and recognition use local features. Most of these still exploit only the luminance information for detection. The color saliency boosting algorithm has provided an efficient method to exploit the saliency of color edges based on information theory. However, during the design of this algorithm, some issues were not addressed in depth: (1) The method has ignored the underlying distribution of derivatives in natural images. (2) The dependence of information content in color-boosted edges on its spatial derivatives has not been quantitatively established. (3) To evaluate luminance and color contributions to saliency of edges, a parameter gradually balancing both contributions is required.
We introduce a novel algorithm, based on the principles of independent component analysis, which models the first order derivatives of color natural images by a generalized Gaussian distribution. Furthermore, using this probability model we show that for images with a Laplacian distribution, which is a particular case of generalized Gaussian distribution, the magnitudes of color-boosted edges reflect their corresponding information content. In order to evaluate the impact of color edge saliency in real world applications, we introduce an extension of the Laplacian-of-Gaussian detector to color, and the performance for image matching is evaluated. Our experiments show that our approach provides more discriminative regions in comparison with the original detector.
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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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Javier Vazquez, G. D. Finlayson, & Maria Vanrell. (2010). A compact singularity function to predict WCS data and unique hues. In 5th European Conference on Colour in Graphics, Imaging and Vision and 12th International Symposium on Multispectral Colour Science (33–38).
Abstract: Understanding how colour is used by the human vision system is a widely studied research field. The field, though quite advanced, still faces important unanswered questions. One of them is the explanation of the unique hues and the assignment of color names. This problem addresses the fact of different perceptual status for different colors.
Recently, Philipona and O'Regan have proposed a biological model that allows to extract the reflection properties of any surface independently of the lighting conditions. These invariant properties are the basis to compute a singularity index that predicts the asymmetries presented in unique hues and basic color categories psychophysical data, therefore is giving a further step in their explanation.
In this paper we build on their formulation and propose a new singularity index. This new formulation equally accounts for the location of the 4 peaks of the World colour survey and has two main advantages. First, it is a simple elegant numerical measure (the Philipona measurement is a rather cumbersome formula). Second, we develop a colour-based explanation for the measure.
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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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Hanne Kause, Patricia Marquez, Andrea Fuster, Aura Hernandez-Sabate, Luc Florack, Debora Gil, et al. (2015). Quality Assessment of Optical Flow in Tagging MRI. In 5th Dutch Bio-Medical Engineering Conference BME2015.
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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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Ilke Demir, Dena Bazazian, Adriana Romero, Viktoriia Sharmanska, & Lyne P. Tchapmi. (2018). WiCV 2018: The Fourth Women In Computer Vision Workshop. In 4th Women in Computer Vision Workshop (pp. 1941–19412).
Abstract: We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations.
Keywords: Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning
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Mohamed Ali Souibgui, Y.Kessentini, & Alicia Fornes. (2020). A conditional GAN based approach for distorted camera captured documents recovery. In 4th Mediterranean Conference on Pattern Recognition and Artificial Intelligence.
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Pau Torras, Arnau Baro, Alicia Fornes, & Lei Kang. (2022). Improving Handwritten Music Recognition through Language Model Integration. In 4th International Workshop on Reading Music Systems (WoRMS2022) (pp. 42–46).
Abstract: Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature.
Keywords: optical music recognition; historical sources; diversity; music theory; digital humanities
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Alejandro Tabas, Emili Balaguer-Ballester, & Laura Igual. (2014). Spatial Discriminant ICA for RS-fMRI characterisation. In 4th International Workshop on Pattern Recognition in Neuroimaging (pp. 1–4).
Abstract: Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.
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Quentin Angermann, Jorge Bernal, Cristina Sanchez Montes, Gloria Fernandez Esparrach, Xavier Gray, Olivier Romain, et al. (2017). Towards Real-Time Polyp Detection in Colonoscopy Videos: Adapting Still Frame-Based Methodologies for Video Sequences Analysis. In 4th International Workshop on Computer Assisted and Robotic Endoscopy (pp. 29–41).
Abstract: Colorectal cancer is the second cause of cancer death in United States: precursor lesions (polyps) detection is key for patient survival. Though colonoscopy is the gold standard screening tool, some polyps are still missed. Several computational systems have been proposed but none of them are used in the clinical room mainly due to computational constraints. Besides, most of them are built over still frame databases, decreasing their performance on video analysis due to the lack of output stability and not coping with associated variability on image quality and polyp appearance. We propose a strategy to adapt these methods to video analysis by adding a spatio-temporal stability module and studying a combination of features to capture polyp appearance variability. We validate our strategy, incorporated on a real-time detection method, on a public video database. Resulting method detects all
polyps under real time constraints, increasing its performance due to our
adaptation strategy.
Keywords: Polyp detection; colonoscopy; real time; spatio temporal coherence
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Naveen Onkarappa, Sujay M. Veerabhadrappa, & Angel Sappa. (2012). Optical Flow in Onboard Applications: A Study on the Relationship Between Accuracy and Scene Texture. In 4th International Conference on Signal and Image Processing (Vol. 221, pp. 257–267).
Abstract: Optical flow has got a major role in making advanced driver assistance systems (ADAS) a reality. ADAS applications are expected to perform efficiently in all kinds of environments, those are highly probable, that one can drive the vehicle in different kinds of roads, times and seasons. In this work, we study the relationship of optical flow with different roads, that is by analyzing optical flow accuracy on different road textures. Texture measures such as TeX , TeX and TeX are evaluated for this purpose. Further, the relation of regularization weight to the flow accuracy in the presence of different textures is also analyzed. Additionally, we present a framework to generate synthetic sequences of different textures in ADAS scenarios with ground-truth optical flow.
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Monica Piñol, Angel Sappa, & Ricardo Toledo. (2012). MultiTable Reinforcement for Visual Object Recognition. In 4th International Conference on Signal and Image Processing (Vol. 221, pp. 469–480). LNCS. Springer India.
Abstract: This paper presents a bag of feature based method for visual object recognition. Our contribution is focussed on the selection of the best feature descriptor. It is implemented by using a novel multi-table reinforcement learning method that selects among five of classical descriptors (i.e., Spin, SIFT, SURF, C-SIFT and PHOW) the one that best describes each image. Experimental results and comparisons are provided showing the improvements achieved with the proposed approach.
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