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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
N. Pares; J.R. Serra |
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Tailleur: El problema del sastre. |
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Miscellaneous |
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1992 |
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V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes. |
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Admin @ si @ PaS1992 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan |
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Title |
The GIDOC Prototype |
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Conference Article |
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2010 |
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10th International Workshop on Pattern Recognition in Information Systems |
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82-89 |
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Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions. |
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Funchal, Portugal |
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978-989-8425-14-0 |
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Admin @ si @ STP2010 |
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1868 |
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N. Zakaria; Jean-Marc Ogier; Josep Llados |
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Title |
On-line Graphics Recognition based on Invariant Spatio-Sequential Descriptor: Fuzzy Matrix |
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2005 |
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Sixth IAPR International Workshop on Graphics Recognition (GREC 2005), 248–259 |
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Hong Kong (China) |
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DAG @ dag @ YFY2005b |
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622 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
N. Zakaria; Jean-Marc Ogier; Josep Llados |
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Title |
The Fuzzy-Spatial Descriptor for the Online Graphic Recognition: Overlapping Matrix Algorithm |
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2006 |
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7th International Workshop, Document Analysis Systems VII (DAS´06), LNCS 3872: 616–627 |
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Nelson (New Zealand) |
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DAG @ dag @ ZOL2006 |
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629 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
N.Nayef; F.Yin; I.Bizid; H.Choi; Y.Feng; Dimosthenis Karatzas; Z.Luo; Umapada Pal; Christophe Rigaud; J. Chazalon; W.Khlif; Muhammad Muzzamil Luqman; Jean-Christophe Burie; C.L.Liu; Jean-Marc Ogier |
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Title |
ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT |
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2017 |
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14th International Conference on Document Analysis and Recognition |
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1454-1459 |
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Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-the-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge. |
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Kyoto; Japan; November 2017 |
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978-1-5386-3586-5 |
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ICDAR |
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DAG; 600.121 |
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Admin @ si @ NYB2017 |
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3097 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray |
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Title |
Perceptual Feature Detection |
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Report |
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2009 |
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CVC Technical Report |
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131 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Mur2009 |
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2390 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray |
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Title |
Predicting Saliency and Aesthetics in Images: A Bottom-up Perspective |
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2012 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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In Part 1 of the thesis, we hypothesize that salient and non-salient image regions can be estimated to be the regions which are enhanced or assimilated in standard low-level color image representations. We prove this hypothesis by adapting a low-level model of color perception into a saliency estimation model. This model shares the three main steps found in many successful models for predicting attention in a scene: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. For such models, integrating spatial information and justifying the choice of various parameter values remain open problems. Our saliency model inherits a principled selection of parameters as well as an innate spatial pooling mechanism from the perception model on which it is based. This pooling mechanism has been fitted using psychophysical data acquired in color-luminance setting experiments. The proposed model outperforms the state-of-the-art at the task of predicting eye-fixations from two datasets. After demonstrating the effectiveness of our basic saliency model, we introduce an improved image representation, based on geometrical grouplets, that enhances complex low-level visual features such as corners and terminations, and suppresses relatively simpler features such as edges. With this improved image representation, the performance of our saliency model in predicting eye-fixations increases for both datasets.
In Part 2 of the thesis, we investigate the problem of aesthetic visual analysis. While a great deal of research has been conducted on hand-crafting image descriptors for aesthetics, little attention so far has been dedicated to the collection, annotation and distribution of ground truth data. Because image aesthetics is complex and subjective, existing datasets, which have few images and few annotations, have significant limitations. To address these limitations, we have introduced a new large-scale database for conducting Aesthetic Visual Analysis, which we call AVA. AVA contains more than 250,000 images, along with a rich variety of annotations. We investigate how the wealth of data in AVA can be used to tackle the challenge of understanding and assessing visual aesthetics by looking into several problems relevant for aesthetic analysis. We demonstrate that by leveraging the data in AVA, and using generic low-level features such as SIFT and color histograms, we can exceed state-of-the-art performance in aesthetic quality prediction tasks.
Finally, we entertain the hypothesis that low-level visual information in our saliency model can also be used to predict visual aesthetics by capturing local image characteristics such as feature contrast, grouping and isolation, characteristics thought to be related to universal aesthetic laws. We use the weighted center-surround responses that form the basis of our saliency model to create a feature vector that describes aesthetics. We also introduce a novel color space for fine-grained color representation. We then demonstrate that the resultant features achieve state-of-the-art performance on aesthetic quality classification.
As such, a promising contribution of this thesis is to show that several vision experiences – low-level color perception, visual saliency and visual aesthetics estimation – may be successfully modeled using a unified framework. This suggests a similar architecture in area V1 for both color perception and saliency and adds evidence to the hypothesis that visual aesthetics appreciation is driven in part by low-level cues. |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Xavier Otazu;Maria Vanrell |
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Admin @ si @ Mur2012 |
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2212 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Eduard Vazquez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Lacuna Restoration: How to choose a neutral colour? |
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Conference Article |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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248–252 |
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Painting restoration which involves filling in material loss (called lacuna) is a complex process. Several standard techniques exist to tackle lacuna restoration,
and this article focuses on those techniques that employ a “neutral” colour to mask the defect. Restoration experts often disagree on the choice of such a colour and in fact, the concept of a neutral colour is controversial. We posit that a neutral colour is one that attracts relatively little visual attention for a specific lacuna. We conducted an eye tracking experiment to compare two common neutral
colour selection methods, specifically the most common local colour and the mean local colour. Results obtained demonstrate that the most common local colour triggers less visual attention in general. Notwithstanding, we have observed instances in which the most common colour triggers a significant amount of attention when subjects spent time resolving their confusion about whether or not a lacuna was part of the painting. |
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Gjovik, Norway |
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CREATE |
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Admin @ si @ MuV2010 |
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1297 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Luca Marchesotti; Florent Perronnin |
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AVA: A Large-Scale Database for Aesthetic Visual Analysis |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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2408-2415 |
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With the ever-expanding volume of visual content available, the ability to organize and navigate such content by aesthetic preference is becoming increasingly important. While still in its nascent stage, research into computational models of aesthetic preference already shows great potential. However, to advance research, realistic, diverse and challenging databases are needed. To this end, we introduce a new large-scale database for conducting Aesthetic Visual Analysis: AVA. It contains over 250,000 images along with a rich variety of meta-data including a large number of aesthetic scores for each image, semantic labels for over 60 categories as well as labels related to photographic style. We show the advantages of AVA with respect to existing databases in terms of scale, diversity, and heterogeneity of annotations. We then describe several key insights into aesthetic preference afforded by AVA. Finally, we demonstrate, through three applications, how the large scale of AVA can be leveraged to improve performance on existing preference tasks |
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Providence, Rhode Islan |
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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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Admin @ si @ MMP2012a |
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2025 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Luca Marchesotti; Florent Perronnin |
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Title |
Learning to Rank Images using Semantic and Aesthetic Labels |
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2012 |
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23rd British Machine Vision Conference |
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110.1-110.10 |
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Most works on image retrieval from text queries have addressed the problem of retrieving semantically relevant images. However, the ability to assess the aesthetic quality of an image is an increasingly important differentiating factor for search engines. In this work, given a semantic query, we are interested in retrieving images which are semantically relevant and score highly in terms of aesthetics/visual quality. We use large-margin classifiers and rankers to learn statistical models capable of ordering images based on the aesthetic and semantic information. In particular, we compare two families of approaches: while the first one attempts to learn a single ranker which takes into account both semantic and aesthetic information, the second one learns separate semantic and aesthetic models. We carry out a quantitative and qualitative evaluation on a recently-published large-scale dataset and we show that the second family of techniques significantly outperforms the first one. |
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Guildford, London |
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1-901725-46-4 |
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BMVC |
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Admin @ si @ MMP2012b |
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2027 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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Title |
Saliency Estimation Using a Non-Parametric Low-Level Vision Model |
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Conference Article |
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2011 |
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IEEE conference on Computer Vision and Pattern Recognition |
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433-440 |
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Gaussian mixture model;ad hoc parameter selection;center-surround inhibition windows;center-surround mechanism;color appearance model;convolution;eye-fixation data;human vision;innate spatial pooling mechanism;inverse wavelet transform;low-level visual front-end;nonparametric low-level vision model;saliency estimation;saliency map;scale integration;scale-weighted center-surround response;scale-weighting function;visual task;Gaussian processes;biology;biology computing;colour vision;computer vision;visual perception;wavelet transforms |
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Many successful models for predicting attention in a scene involve three main steps: convolution with a set of filters, a center-surround mechanism and spatial pooling to construct a saliency map. However, integrating spatial information and justifying the choice of various parameter values remain open problems. In this paper we show that an efficient model of color appearance in human vision, which contains a principled selection of parameters as well as an innate spatial pooling mechanism, can be generalized to obtain a saliency model that outperforms state-of-the-art models. Scale integration is achieved by an inverse wavelet transform over the set of scale-weighted center-surround responses. The scale-weighting function (termed ECSF) has been optimized to better replicate psychophysical data on color appearance, and the appropriate sizes of the center-surround inhibition windows have been determined by training a Gaussian Mixture Model on eye-fixation data, thus avoiding ad-hoc parameter selection. Additionally, we conclude that the extension of a color appearance model to saliency estimation adds to the evidence for a common low-level visual front-end for different visual tasks. |
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Colorado Springs |
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1063-6919 |
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978-1-4577-0394-2 |
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CIC |
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Admin @ si @ MVO2011 |
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1757 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Maria Vanrell; Xavier Otazu; C. Alejandro Parraga |
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Title |
Low-level SpatioChromatic Grouping for Saliency Estimation |
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Journal Article |
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2013 |
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IEEE Transactions on Pattern Analysis and Machine Intelligence |
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TPAMI |
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35 |
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11 |
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2810-2816 |
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We propose a saliency model termed SIM (saliency by induction mechanisms), which is based on a low-level spatiochromatic model that has successfully predicted chromatic induction phenomena. In so doing, we hypothesize that the low-level visual mechanisms that enhance or suppress image detail are also responsible for making some image regions more salient. Moreover, SIM adds geometrical grouplets to enhance complex low-level features such as corners, and suppress relatively simpler features such as edges. Since our model has been fitted on psychophysical chromatic induction data, it is largely nonparametric. SIM outperforms state-of-the-art methods in predicting eye fixations on two datasets and using two metrics. |
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CIC; 600.051; 600.052; 605.203 |
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Admin @ si @ MVO2013 |
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2289 |
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Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin |
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Towards Automatic Concept Transfer |
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Conference Article |
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2011 |
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Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Non-Photorealistic Animation and Rendering |
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167.176 |
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chromatic modeling, color concepts, color transfer, concept transfer |
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This paper introduces a novel approach to automatic concept transfer; examples of concepts are “romantic”, “earthy”, and “luscious”. The approach 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 approach 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. The user may adjust the intensity level of the concept transfer to his/her liking with a single parameter. The proposed approach uses a convex clustering algorithm, with a novel pruning mechanism, to automatically set the complexity of models of chromatic content. It also uses the Earth-Mover's Distance to compute a mapping between the models of the input image and the target chromatic concept. Results show that our approach yields transferred images which effectively represent concepts, as confirmed by a user study. |
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ACM Press |
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978-1-4503-0907-3 |
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Admin @ si @ MSM2011 |
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1866 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin |
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Towards automatic and flexible concept transfer |
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2012 |
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Computers and Graphics |
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CG |
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36 |
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6 |
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622–634 |
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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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0097-8493 |
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2002 |
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Nataliya Shapovalova |
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On Importance of Interaction and Context |
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2010 |
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CVC Technical Report |
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155 |
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Master's thesis |
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Admin @ si @ Sha2010 |
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1355 |
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