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Author | Francesc Tous; Maria Vanrell; Ramon Baldrich | ||||
Title | Exploring Colour Constancy Solutions. | Type | Miscellaneous | ||
Year | 2004 | Publication | CGIV 2004 Second European Conference on Colour in Graphics, Imaging, and Vision, 24:29 | Abbreviated Journal | |
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Address | Aachen (Germany) | ||||
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ TVB2004 | Serial | 452 | ||
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Author | Francesc Tous; Maria Vanrell; Ramon Baldrich | ||||
Title | Relaxed Grey-World: Computational Colour Constancy by Surface Matching | Type | Book Chapter | ||
Year | 2005 | Publication | Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3522:192–199 | Abbreviated Journal | |
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Address | Estoril (Portugal) | ||||
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ TVB2005 | Serial | 555 | ||
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Author | Graham D. Finlayson; Javier Vazquez; Fufu Fang | ||||
Title | The Discrete Cosine Maximum Ignorance Assumption | Type | Conference Article | ||
Year | 2021 | Publication | 29th Color and Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 13-18 | ||
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Abstract | the performance of colour correction algorithms are dependent on the reflectance sets used. Sometimes, when the testing reflectance set is changed the ranking of colour correction algorithms also changes. To remove dependence on dataset we can
make assumptions about the set of all possible reflectances. In the Maximum Ignorance with Positivity (MIP) assumption we assume that all reflectances with per wavelength values between 0 and 1 are equally likely. A weakness in the MIP is that it fails to take into account the correlation of reflectance functions between wavelengths (many of the assumed reflectances are, in reality, not possible). In this paper, we take the view that the maximum ignorance assumption has merit but, hitherto it has been calculated with respect to the wrong coordinate basis. Here, we propose the Discrete Cosine Maximum Ignorance assumption (DCMI), where all reflectances that have coordinates between max and min bounds in the Discrete Cosine Basis coordinate system are equally likely. Here, the correlation between wavelengths is encoded and this results in the set of all plausible reflectances ’looking like’ typical reflectances that occur in nature. This said the DCMI model is also a superset of all measured reflectance sets. Experiments show that, in colour correction, adopting the DCMI results in similar colour correction performance as using a particular reflectance set. |
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Address | Virtual; November 2021 | ||||
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Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | FVF2021 | Serial | 3596 | ||
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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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ISSN | 1084-7529 | ISBN | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ FVS2012 | Serial | 2000 | ||
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Author | Hassan Ahmed Sial | ||||
Title | Estimating Light Effects from a Single Image: Deep Architectures and Ground-Truth Generation | Type | Book Whole | ||
Year | 2021 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | In this thesis, we explore how to estimate the effects of the light interacting with the scene objects from a single image. To achieve this goal, we focus on recovering intrinsic components like reflectance, shading, or light properties such as color and position using deep architectures. The success of these approaches relies on training on large and diversified image datasets. Therefore, we present several contributions on this such as: (a) a data-augmentation technique; (b) a ground-truth for an existing multi-illuminant dataset; (c) a family of synthetic datasets, SID for Surreal Intrinsic Datasets, with diversified backgrounds and coherent light conditions; and (d) a practical pipeline to create hybrid ground-truths to overcome the complexity of acquiring realistic light conditions in a massive way. In parallel with the creation of datasets, we trained different flexible encoder-decoder deep architectures incorporating physical constraints from the image formation models.
In the last part of the thesis, we apply all the previous experience to two different problems. Firstly, we create a large hybrid Doc3DShade dataset with real shading and synthetic reflectance under complex illumination conditions, that is used to train a two-stage architecture that improves the character recognition task in complex lighting conditions of unwrapped documents. Secondly, we tackle the problem of single image scene relighting by extending both, the SID dataset to present stronger shading and shadows effects, and the deep architectures to use intrinsic components to estimate new relit images. |
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Address | September 2021 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | IMPRIMA | Place of Publication | Editor | Maria Vanrell;Ramon Baldrich | |
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ISSN | ISBN | 978-84-122714-8-5 | Medium | ||
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Notes | CIC; | Approved | no | ||
Call Number | Admin @ si @ Sia2021 | Serial | 3607 | ||
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Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell | ||||
Title | Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects | Type | Journal Article | ||
Year | 2020 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
Volume | 37 | Issue | 1 | Pages | 1-15 |
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Abstract | Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results. | ||||
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Notes | CIC; 600.140; 600.12; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SBV2019 | Serial | 3311 | ||
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Author | Hassan Ahmed Sial; Ramon Baldrich; Maria Vanrell; Dimitris Samaras | ||||
Title | Light Direction and Color Estimation from Single Image with Deep Regression | Type | Conference Article | ||
Year | 2020 | Publication | London Imaging Conference | Abbreviated Journal | |
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Abstract | We present a method to estimate the direction and color of the scene light source from a single image. Our method is based on two main ideas: (a) we use a new synthetic dataset with strong shadow effects with similar constraints to the SID dataset; (b) we define a deep architecture trained on the mentioned dataset to estimate the direction and color of the scene light source. Apart from showing good performance on synthetic images, we additionally propose a preliminary procedure to obtain light positions of the Multi-Illumination dataset, and, in this way, we also prove that our trained model achieves good performance when it is applied to real scenes. | ||||
Address | Virtual; September 2020 | ||||
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Area | Expedition | Conference | LIM | ||
Notes | CIC; 600.118; 600.140; | Approved | no | ||
Call Number | Admin @ si @ SBV2020 | Serial | 3460 | ||
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Author | Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell | ||||
Title | Color-based data augmentation for Reflectance Estimation | Type | Conference Article | ||
Year | 2018 | Publication | 26th Color Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 284-289 | ||
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Abstract | Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. | ||||
Address | Vancouver; November 2018 | ||||
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Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ SSB2018a | Serial | 3129 | ||
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Author | Ivet Rafegas | ||||
Title | Exploring Low-Level Vision Models. Case Study: Saliency Prediction | Type | Report | ||
Year | 2013 | Publication | CVC Technical Report | Abbreviated Journal | |
Volume | 175 | Issue | Pages | ||
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Corporate Author | Thesis | Master's thesis | |||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Raf2013 | Serial | 2409 | ||
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Author | Ivet Rafegas | ||||
Title | Color in Visual Recognition: from flat to deep representations and some biological parallelisms | Type | Book Whole | ||
Year | 2017 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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Abstract | Visual recognition is one of the main problems in computer vision that attempts to solve image understanding by deciding what objects are in images. This problem can be computationally solved by using relevant sets of visual features, such as edges, corners, color or more complex object parts. This thesis contributes to how color features have to be represented for recognition tasks.
Image features can be extracted following two different approaches. A first approach is defining handcrafted descriptors of images which is then followed by a learning scheme to classify the content (named flat schemes in Kruger et al. (2013). In this approach, perceptual considerations are habitually used to define efficient color features. Here we propose a new flat color descriptor based on the extension of color channels to boost the representation of spatio-chromatic contrast that surpasses state-of-the-art approaches. However, flat schemes present a lack of generality far away from the capabilities of biological systems. A second approach proposes evolving these flat schemes into a hierarchical process, like in the visual cortex. This includes an automatic process to learn optimal features. These deep schemes, and more specifically Convolutional Neural Networks (CNNs), have shown an impressive performance to solve various vision problems. However, there is a lack of understanding about the internal representation obtained, as a result of automatic learning. In this thesis we propose a new methodology to explore the internal representation of trained CNNs by defining the Neuron Feature as a visualization of the intrinsic features encoded in each individual neuron. Additionally, and inspired by physiological techniques, we propose to compute different neuron selectivity indexes (e.g., color, class, orientation or symmetry, amongst others) to label and classify the full CNN neuron population to understand learned representations. Finally, using the proposed methodology, we show an in-depth study on how color is represented on a specific CNN, trained for object recognition, that competes with primate representational abilities (Cadieu et al (2014)). We found several parallelisms with biological visual systems: (a) a significant number of color selectivity neurons throughout all the layers; (b) an opponent and low frequency representation of color oriented edges and a higher sampling of frequency selectivity in brightness than in color in 1st layer like in V1; (c) a higher sampling of color hue in the second layer aligned to observed hue maps in V2; (d) a strong color and shape entanglement in all layers from basic features in shallower layers (V1 and V2) to object and background shapes in deeper layers (V4 and IT); and (e) a strong correlation between neuron color selectivities and color dataset bias. |
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Address | November 2017 | ||||
Corporate Author | Thesis | Ph.D. thesis | |||
Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Maria Vanrell | |
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ISSN | ISBN | 978-84-945373-7-0 | Medium | ||
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Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ Raf2017 | Serial | 3100 | ||
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Author | Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez | ||||
Title | Enhancing spatio-chromatic representation with more-than-three color coding for image description | Type | Journal Article | ||
Year | 2017 | Publication | Journal of the Optical Society of America A | Abbreviated Journal | JOSA A |
Volume | 34 | Issue | 5 | Pages | 827-837 |
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Abstract | Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding. | ||||
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Notes | CIC; 600.087 | Approved | no | ||
Call Number | Admin @ si @ RVB2017 | Serial | 2892 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Color spaces emerging from deep convolutional networks | Type | Conference Article | ||
Year | 2016 | Publication | 24th Color and Imaging Conference | Abbreviated Journal | |
Volume | Issue | Pages | 225-230 | ||
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Abstract | Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers. |
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Address | San Diego; USA; November 2016 | ||||
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Area | Expedition | Conference | CIC | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ RaV2016a | Serial | 2894 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Colour Visual Coding in trained Deep Neural Networks | Type | Abstract | ||
Year | 2016 | Publication | European Conference on Visual Perception | Abbreviated Journal | |
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Address | Barcelona; Spain; August 2016 | ||||
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Area | Expedition | Conference | ECVP | ||
Notes | CIC | Approved | no | ||
Call Number | Admin @ si @ RaV2016b | Serial | 2895 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Color representation in CNNs: parallelisms with biological vision | Type | Conference Article | ||
Year | 2017 | Publication | ICCV Workshop on Mutual Benefits ofr Cognitive and Computer Vision | Abbreviated Journal | |
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Abstract | Convolutional Neural Networks (CNNs) trained for object recognition tasks present representational capabilities approaching to primate visual systems [1]. This provides a computational framework to explore how image features
are efficiently represented. Here, we dissect a trained CNN [2] to study how color is represented. We use a classical methodology used in physiology that is measuring index of selectivity of individual neurons to specific features. We use ImageNet Dataset [20] images and synthetic versions of them to quantify color tuning properties of artificial neurons to provide a classification of the network population. We conclude three main levels of color representation showing some parallelisms with biological visual systems: (a) a decomposition in a circular hue space to represent single color regions with a wider hue sampling beyond the first layer (V2), (b) the emergence of opponent low-dimensional spaces in early stages to represent color edges (V1); and (c) a strong entanglement between color and shape patterns representing object-parts (e.g. wheel of a car), objectshapes (e.g. faces) or object-surrounds configurations (e.g. blue sky surrounding an object) in deeper layers (V4 or IT). |
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Address | Venice; Italy; October 2017 | ||||
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Area | Expedition | Conference | ICCV-MBCC | ||
Notes | CIC; 600.087; 600.051 | Approved | no | ||
Call Number | Admin @ si @ RaV2017 | Serial | 2984 | ||
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Author | Ivet Rafegas; Maria Vanrell | ||||
Title | Color encoding in biologically-inspired convolutional neural networks | Type | Journal Article | ||
Year | 2018 | Publication | Vision Research | Abbreviated Journal | VR |
Volume | 151 | Issue | Pages | 7-17 | |
Keywords | Color coding; Computer vision; Deep learning; Convolutional neural networks | ||||
Abstract | Convolutional Neural Networks have been proposed as suitable frameworks to model biological vision. Some of these artificial networks showed representational properties that rival primate performances in object recognition. In this paper we explore how color is encoded in a trained artificial network. It is performed by estimating a color selectivity index for each neuron, which allows us to describe the neuron activity to a color input stimuli. The index allows us to classify whether they are color selective or not and if they are of a single or double color. We have determined that all five convolutional layers of the network have a large number of color selective neurons. Color opponency clearly emerges in the first layer, presenting 4 main axes (Black-White, Red-Cyan, Blue-Yellow and Magenta-Green), but this is reduced and rotated as we go deeper into the network. In layer 2 we find a denser hue sampling of color neurons and opponency is reduced almost to one new main axis, the Bluish-Orangish coinciding with the dataset bias. In layers 3, 4 and 5 color neurons are similar amongst themselves, presenting different type of neurons that detect specific colored objects (e.g., orangish faces), specific surrounds (e.g., blue sky) or specific colored or contrasted object-surround configurations (e.g. blue blob in a green surround). Overall, our work concludes that color and shape representation are successively entangled through all the layers of the studied network, revealing certain parallelisms with the reported evidences in primate brains that can provide useful insight into intermediate hierarchical spatio-chromatic representations. | ||||
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Notes | CIC; 600.051; 600.087 | Approved | no | ||
Call Number | Admin @ si @RaV2018 | Serial | 3114 | ||
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Author | Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias | ||||
Title | Understanding trained CNNs by indexing neuron selectivity | Type | Journal Article | ||
Year | 2020 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
Volume | 136 | Issue | Pages | 318-325 | |
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Abstract | The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful. | ||||
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Notes | CIC; 600.087; 600.140; 600.118 | Approved | no | ||
Call Number | Admin @ si @ RVL2019 | Serial | 3310 | ||
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Author | J. Nuñez; O. Fors; Xavier Otazu; Vicenç Pala; Roman Arbiol; M.T. Merino | ||||
Title | A Wavelet-Based Method for the Determination of the Relative Resolution Between Remotely Sensed Images | Type | Journal | ||
Year | 2006 | Publication | IEEE Transactions on Geoscience and Remote Sensing, 44(9): 2539–2548 | Abbreviated Journal | |
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ NFO2006 | Serial | 660 | ||
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Author | J. Nuñez; Xavier Otazu; M.T. Merino | ||||
Title | A Multiresolution-Based Method for the Determination of the Relative Resolution between Images. First Application to Remote Sensing and Medical Images | Type | Journal | ||
Year | 2005 | Publication | International Journal of Imaging Systems and Technology, 15(5): 225–235 (IF: 0.439) | Abbreviated Journal | |
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Notes | CIC | Approved | no | ||
Call Number | CAT @ cat @ NOM2005 | Serial | 645 | ||
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