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| Author | Noha Elfiky; Fahad Shahbaz Khan; Joost Van de Weijer; Jordi Gonzalez | ||||
| Title | Discriminative Compact Pyramids for Object and Scene Recognition | Type | Journal Article | ||
Year  |
2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| Volume | 45 | Issue | 4 | Pages | 1627-1636 |
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| Abstract | Spatial pyramids have been successfully applied to incorporating spatial information into bag-of-words based image representation. However, a major drawback is that it leads to high dimensional image representations. In this paper, we present a novel framework for obtaining compact pyramid representation. First, we investigate the usage of the divisive information theoretic feature clustering (DITC) algorithm in creating a compact pyramid representation. In many cases this method allows us to reduce the size of a high dimensional pyramid representation up to an order of magnitude with little or no loss in accuracy. Furthermore, comparison to clustering based on agglomerative information bottleneck (AIB) shows that our method obtains superior results at significantly lower computational costs. Moreover, we investigate the optimal combination of multiple features in the context of our compact pyramid representation. Finally, experiments show that the method can obtain state-of-the-art results on several challenging data sets. | ||||
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| ISSN | 0031-3203 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | ISE; CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ EKW2012 | Serial | 1807 | ||
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| Author | Susana Alvarez; Anna Salvatella; Maria Vanrell; Xavier Otazu | ||||
| Title | Low-dimensional and Comprehensive Color Texture Description | Type | Journal Article | ||
| Year |
2012 | Publication | Computer Vision and Image Understanding | Abbreviated Journal | CVIU |
| Volume | 116 | Issue | I | Pages | 54-67 |
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| Abstract | Image retrieval can be dealt by combining standard descriptors, such as those of MPEG-7, which are defined independently for each visual cue (e.g. SCD or CLD for Color, HTD for texture or EHD for edges).
A common problem is to combine similarities coming from descriptors representing different concepts in different spaces. In this paper we propose a color texture description that bypasses this problem from its inherent definition. It is based on a low dimensional space with 6 perceptual axes. Texture is described in a 3D space derived from a direct implementation of the original Julesz’s Texton theory and color is described in a 3D perceptual space. This early fusion through the blob concept in these two bounded spaces avoids the problem and allows us to derive a sparse color-texture descriptor that achieves similar performance compared to MPEG-7 in image retrieval. Moreover, our descriptor presents comprehensive qualities since it can also be applied either in segmentation or browsing: (a) a dense image representation is defined from the descriptor showing a reasonable performance in locating texture patterns included in complex images; and (b) a vocabulary of basic terms is derived to build an intermediate level descriptor in natural language improving browsing by bridging semantic gap |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1077-3142 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CAT;CIC | Approved | no | ||
| Call Number | Admin @ si @ ASV2012 | Serial | 1827 | ||
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| Author | Arjan Gijsenij; Theo Gevers; Joost Van de Weijer | ||||
| Title | Improving Color Constancy by Photometric Edge Weighting | Type | Journal Article | ||
| Year |
2012 | Publication | IEEE Transaction on Pattern Analysis and Machine Intelligence | Abbreviated Journal | TPAMI |
| Volume | 34 | Issue | 5 | Pages | 918-929 |
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| Abstract | : Edge-based color constancy methods make use of image derivatives to estimate the illuminant. However, different edge types exist in real-world images such as material, shadow and highlight edges. These different edge types may have a distinctive influence on the performance of the illuminant estimation. Therefore, in this paper, an extensive analysis is provided of different edge types on the performance of edge-based color constancy methods. First, an edge-based taxonomy is presented classifying edge types based on their photometric properties (e.g. material, shadow-geometry and highlights). Then, a performance evaluation of edge-based color constancy is provided using these different edge types. From this performance evaluation it is derived that specular and shadow edge types are more valuable than material edges for the estimation of the illuminant. To this end, the (iterative) weighted Grey-Edge algorithm is proposed in which these edge types are more emphasized for the estimation of the illuminant. Images that are recorded under controlled circumstances demonstrate that the proposed iterative weighted Grey-Edge algorithm based on highlights reduces the median angular error with approximately $25\%$. In an uncontrolled environment, improvements in angular error up to $11\%$ are obtained with respect to regular edge-based color constancy. | ||||
| Address | Los Alamitos; CA; USA; | ||||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0162-8828 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC;ISE | Approved | no | ||
| Call Number | Admin @ si @ GGW2012 | Serial | 1850 | ||
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| Author | Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell; Antonio Lopez | ||||
| Title | Color Attributes for Object Detection | Type | Conference Article | ||
| Year |
2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 3306-3313 | ||
| Keywords | pedestrian detection | ||||
| Abstract | State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,
leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape. In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe- art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14% in mean AP over conventional state-of-the-art methods. |
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| Address | Providence; Rhode Island; USA; | ||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | ADAS; CIC; | Approved | no | ||
| Call Number | Admin @ si @ KRW2012 | Serial | 1935 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell | ||||
| Title | Modulating Shape Features by Color Attention for Object Recognition | Type | Journal Article | ||
| Year |
2012 | Publication | International Journal of Computer Vision | Abbreviated Journal | IJCV |
| Volume | 98 | Issue | 1 | Pages | 49-64 |
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| Abstract | Bag-of-words based image representation is a successful approach for object recognition. Generally, 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, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information. | ||||
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| Publisher | Springer Netherlands | Place of Publication | Editor | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 0920-5691 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ KWV2012 | Serial | 1864 | ||
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| Author | Javier Vazquez; J. Kevin O'Regan; Maria Vanrell; Graham D. Finlayson | ||||
| Title | A new spectrally sharpened basis to predict colour naming, unique hues, and hue cancellation | Type | Journal Article | ||
| Year |
2012 | Publication | Journal of Vision | Abbreviated Journal | VSS |
| Volume | 12 | Issue | 6 (7) | Pages | 1-14 |
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| Abstract | When light is reflected off a surface, there is a linear relation between the three human photoreceptor responses to the incoming light and the three photoreceptor responses to the reflected light. Different colored surfaces have different linear relations. Recently, Philipona and O'Regan (2006) showed that when this relation is singular in a mathematical sense, then the surface is perceived as having a highly nameable color. Furthermore, white light reflected by that surface is perceived as corresponding precisely to one of the four psychophysically measured unique hues. However, Philipona and O'Regan's approach seems unrelated to classical psychophysical models of color constancy. In this paper we make this link. We begin by transforming cone sensors to spectrally sharpened counterparts. In sharp color space, illumination change can be modeled by simple von Kries type scalings of response values within each of the spectrally sharpened response channels. In this space, Philipona and O'Regan's linear relation is captured by a simple Land-type color designator defined by dividing reflected light by incident light. This link between Philipona and O'Regan's theory and Land's notion of color designator gives the model biological plausibility. We then show that Philipona and O'Regan's singular surfaces are surfaces which are very close to activating only one or only two of such newly defined spectrally sharpened sensors, instead of the usual three. Closeness to zero is quantified in a new simplified measure of singularity which is also shown to relate to the chromaticness of colors. As in Philipona and O'Regan's original work, our new theory accounts for a large variety of psychophysical color data. | ||||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ VOV2012 | Serial | 1998 | ||
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| Author | Javier Vazquez; Maria Vanrell; Ramon Baldrich; Francesc Tous | ||||
| Title | Color Constancy by Category Correlation | Type | Journal Article | ||
| Year |
2012 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
| Volume | 21 | Issue | 4 | Pages | 1997-2007 |
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| Abstract | Finding color representations which are stable to illuminant changes is still an open problem in computer vision. Until now most approaches have been based on physical constraints or statistical assumptions derived from the scene, while very little attention has been paid to the effects that selected illuminants have
on the final color image representation. The novelty of this work is to propose perceptual constraints that are computed on the corrected images. We define the category hypothesis, which weights the set of feasible illuminants according to their ability to map the corrected image onto specific colors. Here we choose these colors as the universal color categories related to basic linguistic terms which have been psychophysically measured. These color categories encode natural color statistics, and their relevance across different cultures is indicated by the fact that they have received a common color name. From this category hypothesis we propose a fast implementation that allows the sampling of a large set of illuminants. Experiments prove that our method rivals current state-of-art performance without the need for training algorithmic parameters. Additionally, the method can be used as a framework to insert top-down information from other sources, thus opening further research directions in solving for color constancy. |
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1057-7149 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ VVB2012 | Serial | 1999 | ||
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| Author | Graham D. Finlayson; Javier Vazquez; Sabine Süsstrunk; Maria Vanrell | ||||
| Title | Spectral sharpening by spherical sampling | Type | Journal Article | ||
| Year |
2012 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
| Volume | 29 | Issue | 7 | Pages | 1199-1210 |
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| Abstract | 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 1084-7529 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ FVS2012 | Serial | 2000 | ||
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| Author | Naila Murray; Sandra Skaff; Luca Marchesotti; Florent Perronnin | ||||
| Title | Towards automatic and flexible concept transfer | Type | Journal Article | ||
| Year |
2012 | Publication | Computers and Graphics | Abbreviated Journal | CG |
| Volume | 36 | Issue | 6 | Pages | 622–634 |
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| Abstract | 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 0097-8493 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MSM2012 | Serial | 2002 | ||
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| Author | Naila Murray; Luca Marchesotti; Florent Perronnin | ||||
| Title | AVA: A Large-Scale Database for Aesthetic Visual Analysis | Type | Conference Article | ||
| Year |
2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2408-2415 | ||
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| Abstract | 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 | ||||
| Address | Providence, Rhode Islan | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MMP2012a | Serial | 2025 | ||
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| Author | Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell | ||||
| Title | Names and Shades of Color for Intrinsic Image Estimation | Type | Conference Article | ||
| Year |
2012 | Publication | 25th IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 278-285 | ||
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| Abstract | 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. | ||||
| Address | Providence, Rhode Island | ||||
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| Publisher | IEEE Xplore | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
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| ISSN | 1063-6919 | ISBN | 978-1-4673-1226-4 | Medium | |
| Area | Expedition | Conference | CVPR | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ SPB2012 | Serial | 2026 | ||
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| Author | Naila Murray; Luca Marchesotti; Florent Perronnin | ||||
| Title | Learning to Rank Images using Semantic and Aesthetic Labels | Type | Conference Article | ||
| Year |
2012 | Publication | 23rd British Machine Vision Conference | Abbreviated Journal | |
| Volume | Issue | Pages | 110.1-110.10 | ||
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| Abstract | 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. | ||||
| Address | Guildford, London | ||||
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| ISSN | ISBN | 1-901725-46-4 | Medium | ||
| Area | Expedition | Conference | BMVC | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ MMP2012b | Serial | 2027 | ||
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| Author | Joost Van de Weijer; Robert Benavente; Maria Vanrell; Cordelia Schmid; Ramon Baldrich; Jacob Verbeek; Diane Larlus | ||||
| Title | Color Naming | Type | Book Chapter | ||
| Year |
2012 | Publication | Color in Computer Vision: Fundamentals and Applications | Abbreviated Journal | |
| Volume | Issue | 17 | Pages | 287-317 | |
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| Publisher | John Wiley & Sons, Ltd. | Place of Publication | Editor | Theo Gevers;Arjan Gijsenij;Joost Van de Weijer;Jan-Mark Geusebroek | |
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ WBV2012 | Serial | 2063 | ||
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| Author | Ernest Valveny; Robert Benavente; Agata Lapedriza; Miquel Ferrer; Jaume Garcia; Gemma Sanchez | ||||
| Title | Adaptation of a computer programming course to the EXHE requirements: evaluation five years later | Type | Miscellaneous | ||
| Year |
2012 | Publication | European Journal of Engineering Education | Abbreviated Journal | |
| Volume | 37 | Issue | 3 | Pages | 243-254 |
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| Notes | DAG; CIC; OR; invisible;MV | Approved | no | ||
| Call Number | Admin @ si @ VBL2012 | Serial | 2070 | ||
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| Author | Susana Alvarez; Maria Vanrell | ||||
| Title | Texton theory revisited: a bag-of-words approach to combine textons | Type | Journal Article | ||
| Year |
2012 | Publication | Pattern Recognition | Abbreviated Journal | PR |
| 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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| Series Volume | Series Issue | Edition | |||
| ISSN | 0031-3203 | ISBN | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ AlV2012a | Serial | 2130 | ||
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| Author | Javier Vazquez; Robert Benavente; Maria Vanrell | ||||
| Title | Naming constraints constancy | Type | Conference Article | ||
| Year |
2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | |
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| Abstract | Different studies have shown that languages from industrialized cultures
share a set of 11 basic colour terms: red, green, blue, yellow, pink, purple, brown, orange, black, white, and grey (Berlin & Kay, 1969, Basic Color Terms, University of California Press)( Kay & Regier, 2003, PNAS, 100, 9085-9089). Some of these studies have also reported the best representatives or focal values of each colour (Boynton and Olson, 1990, Vision Res. 30,1311–1317), (Sturges and Whitfield, 1995, CRA, 20:6, 364–376). Some further studies have provided us with fuzzy datasets for color naming by asking human observers to rate colours in terms of membership values (Benavente -et al-, 2006, CRA. 31:1, 48–56,). Recently, a computational model based on these human ratings has been developed (Benavente -et al-, 2008, JOSA-A, 25:10, 2582-2593). This computational model follows a fuzzy approach to assign a colour name to a particular RGB value. For example, a pixel with a value (255,0,0) will be named 'red' with membership 1, while a cyan pixel with a RGB value of (0, 200, 200) will be considered to be 0.5 green and 0.5 blue. In this work, we show how this colour naming paradigm can be applied to different computer vision tasks. In particular, we report results in colour constancy (Vazquez-Corral -et al-, 2012, IEEE TIP, in press) showing that the classical constraints on either illumination or surface reflectance can be substituted by the statistical properties encoded in the colour names. [Supported by projects TIN2010-21771-C02-1, CSD2007-00018]. |
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| Area | Expedition | Conference | AV A | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ VBV2012 | Serial | 2131 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco | ||||
| Title | An investigation into plausible neural mechanisms related to the the CIWaM computational model for brightness induction | Type | Conference Article | ||
| Year |
2012 | Publication | 2nd Joint AVA / BMVA Meeting on Biological and Machine Vision | Abbreviated Journal | |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas. From a purely computational perspective, we built a low-level computational model (CIWaM) of early sensory processing based on multi-resolution wavelets with the aim of replicating brightness and colour (Otazu et al., 2010, Journal of Vision, 10(12):5) induction effects. Furthermore, we successfully used the CIWaM architecture to define a computational saliency model (Murray et al, 2011, CVPR, 433-440; Vanrell et al, submitted to AVA/BMVA'12). From a biological perspective, neurophysiological evidence suggests that perceived brightness information may be explicitly represented in V1. In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (Li, 1999, Network:Comput. Neural Syst., 10, 187-212) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2-3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as visual saliency, which share with brightness induction the relevant effect of contextual influences (the ones modelled by CIWaM). In the proposed model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition taken from the computational model (CIWaM).
This model successfully accounts for well known pyschophysical effects (among them: the White's and modied White's effects, the Todorovic, Chevreul, achromatic ring patterns, and grating induction effects) for static contexts and also for brigthness induction in dynamic contexts defined by modulating the luminance of surrounding areas. From a methodological point of view, we conclude that the results obtained by the computational model (CIWaM) are compatible with the ones obtained by the neurodynamical model proposed here. |
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| Area | Expedition | Conference | AV A | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ OPD2012a | Serial | 2132 | ||
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| Author | Xavier Otazu; Olivier Penacchio; Laura Dempere-Marco | ||||
| Title | Brightness induction by contextual influences in V1: a neurodynamical account | Type | Abstract | ||
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2012 | Publication | Journal of Vision | Abbreviated Journal | VSS |
| Volume | 12 | Issue | 9 | Pages | |
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| Abstract | Brightness induction is the modulation of the perceived intensity of an area by the luminance of surrounding areas and reveals fundamental properties of neural organization in the visual system. Several phenomenological models have been proposed that successfully account for psychophysical data (Pessoa et al. 1995, Blakeslee and McCourt 2004, Barkan et al. 2008, Otazu et al. 2008).
Neurophysiological evidence suggests that brightness information is explicitly represented in V1 and neuronal response modulations have been observed followingluminance changes outside their receptive fields (Rossi and Paradiso, 1999). In this work we investigate possible neural mechanisms that offer a plausible explanation for such effects. To this end, we consider the model by Z.Li (1999) which is based on biological data and focuses on the part of V1 responsible for contextual influences, namely, layer 2–3 pyramidal cells, interneurons, and horizontal intracortical connections. This model has proven to account for phenomena such as contour detection and preattentive segmentation, which share with brightness induction the relevant effect of contextual influences. In our model, the input to the network is derived from a complete multiscale and multiorientation wavelet decomposition which makes it possible to recover an image reflecting the perceived intensity. The proposed model successfully accounts for well known pyschophysical effects (among them: the White's and modified White's effects, the Todorović, Chevreul, achromatic ring patterns, and grating induction effects). Our work suggests that intra-cortical interactions in the primary visual cortex could partially explain perceptual brightness induction effects and reveals how a common general architecture may account for several different fundamental processes emerging early in the visual pathway. |
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ OPD2012b | Serial | 2178 | ||
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