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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maria Vanrell; Jordi Vitria; Xavier Roca |
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Title |
A General Morphological Framework for Perceptual Texture Discrimination based on Granulometries. |
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Miscellaneous |
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1993 |
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Technical Workshop on Mathematical Morphology and its Applications to Signal Processing. |
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Barcelona |
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OR;ISE;CIC;MV |
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BCNPCL @ bcnpcl @ VVR1993 |
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154 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maria Vanrell; Jordi Vitria |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Mathematical Morphology, Granulometries and Texture Perception. |
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Miscellaneous |
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1993 |
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SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). |
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San Diego; CA; USA |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1993 |
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178 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maria Vanrell; Jordi Vitria |
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Title |
Optimal 3x3 decomposable disks for morphological transformations |
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1997 |
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Image and Vision Computing, 15(2): 845–854 |
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OR;CIC;MV |
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BCNPCL @ bcnpcl @ VaV1997c |
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543 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva |
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Title |
Colour Normalisation Based on Background Information. |
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Miscellaneous |
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2001 |
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Proceeding ICIP 2001, IEEE International Conference on Image Processing |
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ICIP 2001 |
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1 |
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874–877 |
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Grecia. |
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ADAS;DAG;CIC |
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ADAS @ adas @ VLP2001 |
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167 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Maria Vanrell |
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Title |
Exploring the space of behaviour of a texture perception algorithm |
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1997 |
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CVC Technical Report #12 |
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CVC (UAB) |
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CIC |
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CAT @ cat @ Van1997 |
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523 |
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Maria del Camp Davesa |
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Title |
Human action categorization in image sequences |
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Report |
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2011 |
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CVC Technical Report |
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169 |
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Bellaterra (Spain) |
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Computer Vision Center |
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Master's thesis |
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CiC;CIC |
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Admin @ si @ Dav2011 |
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1934 |
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Marcos V Conde; Javier Vazquez; Michael S Brown; Radu TImofte |
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Title |
NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement |
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2024 |
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38th AAAI Conference on Artificial Intelligence |
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3D lookup tables (3D LUTs) are a key component for image enhancement. Modern image signal processors (ISPs) have dedicated support for these as part of the camera rendering pipeline. Cameras typically provide multiple options for picture styles, where each style is usually obtained by applying a unique handcrafted 3D LUT. Current approaches for learning and applying 3D LUTs are notably fast, yet not so memory-efficient, as storing multiple 3D LUTs is required. For this reason and other implementation limitations, their use on mobile devices is less popular. In this work, we propose a Neural Implicit LUT (NILUT), an implicitly defined continuous 3D color transformation parameterized by a neural network. We show that NILUTs are capable of accurately emulating real 3D LUTs. Moreover, a NILUT can be extended to incorporate multiple styles into a single network with the ability to blend styles implicitly. Our novel approach is memory-efficient, controllable and can complement previous methods, including learned ISPs. |
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AAAI |
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CIC; MACO |
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Admin @ si @ CVB2024 |
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3872 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marcos V Conde; Florin Vasluianu; Javier Vazquez; Radu Timofte |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Perceptual image enhancement for smartphone real-time applications |
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2023 |
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Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision |
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1848-1858 |
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Recent advances in camera designs and imaging pipelines allow us to capture high-quality images using smartphones. However, due to the small size and lens limitations of the smartphone cameras, we commonly find artifacts or degradation in the processed images. The most common unpleasant effects are noise artifacts, diffraction artifacts, blur, and HDR overexposure. Deep learning methods for image restoration can successfully remove these artifacts. However, most approaches are not suitable for real-time applications on mobile devices due to their heavy computation and memory requirements. In this paper, we propose LPIENet, a lightweight network for perceptual image enhancement, with the focus on deploying it on smartphones. Our experiments show that, with much fewer parameters and operations, our model can deal with the mentioned artifacts and achieve competitive performance compared with state-of-the-art methods on standard benchmarks. Moreover, to prove the efficiency and reliability of our approach, we deployed the model directly on commercial smartphones and evaluated its performance. Our model can process 2K resolution images under 1 second in mid-level commercial smartphones. |
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Waikoloa; Hawai; USA; January 2023 |
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WACV |
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MACO; CIC |
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Admin @ si @ CVV2023 |
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3900 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell; Dimitris Samaras |
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Title |
The Photometry of Intrinsic Images |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1494-1501 |
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Intrinsic characterization of scenes is often the best way to overcome the illumination variability artifacts that complicate most computer vision problems, from 3D reconstruction to object or material recognition. This paper examines the deficiency of existing intrinsic image models to accurately account for the effects of illuminant color and sensor characteristics in the estimation of intrinsic images and presents a generic framework which incorporates insights from color constancy research to the intrinsic image decomposition problem. The proposed mathematical formulation includes information about the color of the illuminant and the effects of the camera sensors, both of which modify the observed color of the reflectance of the objects in the scene during the acquisition process. By modeling these effects, we get a “truly intrinsic” reflectance image, which we call absolute reflectance, which is invariant to changes of illuminant or camera sensors. This model allows us to represent a wide range of intrinsic image decompositions depending on the specific assumptions on the geometric properties of the scene configuration and the spectral properties of the light source and the acquisition system, thus unifying previous models in a single general framework. We demonstrate that even partial information about sensors improves significantly the estimated reflectance images, thus making our method applicable for a wide range of sensors. We validate our general intrinsic image framework experimentally with both synthetic data and natural images. |
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Columbus; Ohio; USA; June 2014 |
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CVPR |
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CIC; 600.052; 600.051; 600.074 |
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Admin @ si @ SPB2014 |
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2506 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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Title |
Names and Shades of Color for Intrinsic Image Estimation |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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278-285 |
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In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. |
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Providence, Rhode Island |
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IEEE Xplore |
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1063-6919 |
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978-1-4673-1226-4 |
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CIC |
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Admin @ si @ SPB2012 |
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2026 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marc Serra |
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Title |
Estimating Intrinsic Images from Physical and Categorical Color Cues |
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2010 |
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CVC Technical Report |
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151 |
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CIC |
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Admin @ si @ Ser2010 |
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1345 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Marc Serra |
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Modeling, estimation and evaluation of intrinsic images considering color information |
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2015 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Image values are the result of a combination of visual information coming from multiple sources. Recovering information from the multiple factors thatproduced an image seems a hard and ill-posed problem. However, it is important to observe that humans develop the ability to interpret images and recognize and isolate specific physical properties of the scene.
Images describing a single physical characteristic of an scene are called intrinsic images. These images would benefit most computer vision tasks which are often affected by the multiple complex effects that are usually found in natural images (e.g. cast shadows, specularities, interreflections...).
In this thesis we analyze the problem of intrinsic image estimation from different perspectives, including the theoretical formulation of the problem, the visual cues that can be used to estimate the intrinsic components and the evaluation mechanisms of the problem. |
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September 2015 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Robert Benavente;Olivier Penacchio |
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978-84-943427-4-5 |
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CIC; 600.074 |
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Admin @ si @ Ser2015 |
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2688 |
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M. Gonzalez-Audicana; Xavier Otazu; O. Fors; R Garcia; J. Nuñez |
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Fusion of different spatial and spectral resolution images: development, apllication and comparison of new methods based on wavelets. |
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2002 |
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Proceedings of the 1st. International Symposium Recent Advances in Quantitative Remote Sensing. |
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CIC |
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CAT @ cat @ GOF2002 |
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291 |
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M. Gonzalez-Audicana; Xavier Otazu; O. Fors; A. Seco |
![goto web page url](img/www.gif)
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Comparison between Mallats and the trous discrete wavelet transform based algorithms for the fusion of multispectral and panchromatic images |
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2005 |
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International Journal of Remote Sensing, 26(3):595–614 (IF: 0.925) |
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CAT @ cat @ GOF2005 |
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530 |
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M. Danelljan; Fahad Shahbaz Khan; Michael Felsberg; Joost Van de Weijer |
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Title |
Adaptive color attributes for real-time visual tracking |
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Conference Article |
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2014 |
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27th IEEE Conference on Computer Vision and Pattern Recognition |
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1090 - 1097 |
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Visual tracking is a challenging problem in computer vision. Most state-of-the-art visual trackers either rely on luminance information or use simple color representations for image description. Contrary to visual tracking, for object
recognition and detection, sophisticated color features when combined with luminance have shown to provide excellent performance. Due to the complexity of the tracking problem, the desired color feature should be computationally
efficient, and possess a certain amount of photometric invariance while maintaining high discriminative power.
This paper investigates the contribution of color in a tracking-by-detection framework. Our results suggest that color attributes provides superior performance for visual tracking. We further propose an adaptive low-dimensional
variant of color attributes. Both quantitative and attributebased evaluations are performed on 41 challenging benchmark color sequences. The proposed approach improves the baseline intensity-based tracker by 24% in median distance precision. Furthermore, we show that our approach outperforms
state-of-the-art tracking methods while running at more than 100 frames per second. |
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Nottingham; UK; September 2014 |
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CIC; LAMP; 600.074; 600.079 |
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Admin @ si @ DKF2014 |
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2509 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Justine Giroux; Mohammad Reza Karimi Dastjerdi; Yannick Hold-Geoffroy; Javier Vazquez; Jean François Lalonde |
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Title |
Towards a Perceptual Evaluation Framework for Lighting Estimation |
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Conference Article |
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2024 |
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Arxiv |
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rogress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets. While this may appear to be a reasonable approach, we demonstrate that doing so does not correlate to human preference when the estimated lighting is used to relight a virtual scene into a real photograph. To study this, we design a controlled psychophysical experiment where human observers must choose their preference amongst rendered scenes lit using a set of lighting estimation algorithms selected from the recent literature, and use it to analyse how these algorithms perform according to human perception. Then, we demonstrate that none of the most popular IQA metrics from the literature, taken individually, correctly represent human perception. Finally, we show that by learning a combination of existing IQA metrics, we can more accurately represent human preference. This provides a new perceptual framework to help evaluate future lighting estimation algorithms. |
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Seattle; USA; June 2024 |
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MACO; CIC |
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Admin @ si @ GDH2024 |
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3999 |
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Judit Martinez; Eva Costa; P. Herreros; F. Javier Sanchez; Ramon Baldrich |
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A Modular and Scalable Architecture for PC-Based Real-Time Vision Systems |
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Journal Article |
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2003 |
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Real–Time Imaging, (IF: 0.512) |
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9 |
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99-112 |
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CAT @ cat @ MCH2003b |
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394 |
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Author ![sorted by Author field, descending order (down)](img/sort_desc.gif) |
Josep M. Gonfaus; Xavier Boix; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez |
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Title |
Harmony Potentials for Joint Classification and Segmentation |
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Conference Article |
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2010 |
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23rd IEEE Conference on Computer Vision and Pattern Recognition |
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3280–3287 |
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Hierarchical conditional random fields have been successfully applied to object segmentation. One reason is their ability to incorporate contextual information at different scales. However, these models do not allow multiple labels to be assigned to a single node. At higher scales in the image, this yields an oversimplified model, since multiple classes can be reasonable expected to appear within one region. This simplified model especially limits the impact that observations at larger scales may have on the CRF model. Neglecting the information at larger scales is undesirable since class-label estimates based on these scales are more reliable than at smaller, noisier scales. To address this problem, we propose a new potential, called harmony potential, which can encode any possible combination of class labels. We propose an effective sampling strategy that renders tractable the underlying optimization problem. Results show that our approach obtains state-of-the-art results on two challenging datasets: Pascal VOC 2009 and MSRC-21. |
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San Francisco CA, USA |
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1063-6919 |
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978-1-4244-6984-0 |
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ADAS;CIC;ISE |
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ADAS @ adas @ GBW2010 |
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1296 |
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