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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 | ||
| Area | Expedition | Conference | |||
| 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 |
Ivo Everts; Jan van Gemert; Theo Gevers | ||||
| Title | Evaluation of Color STIPs for Human Action Recognition | Type | Conference Article | ||
| Year | 2013 | Publication | IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | |
| Volume | Issue | Pages | 2850-2857 | ||
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| Abstract | This paper is concerned with recognizing realistic human actions in videos based on spatio-temporal interest points (STIPs). Existing STIP-based action recognition approaches operate on intensity representations of the image data. Because of this, these approaches are sensitive to disturbing photometric phenomena such as highlights and shadows. Moreover, valuable information is neglected by discarding chromaticity from the photometric representation. These issues are addressed by Color STIPs. Color STIPs are multi-channel reformulations of existing intensity-based STIP detectors and descriptors, for which we consider a number of chromatic representations derived from the opponent color space. This enhanced modeling of appearance improves the quality of subsequent STIP detection and description. Color STIPs are shown to substantially outperform their intensity-based counterparts on the challenging UCF~sports, UCF11 and UCF50 action recognition benchmarks. Moreover, the results show that color STIPs are currently the single best low-level feature choice for STIP-based approaches to human action recognition. | ||||
| Address | Portland; oregon; June 2013 | ||||
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| ISSN | 1063-6919 | ISBN | Medium | ||
| Area | Expedition | Conference | CVPR | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ EGG2013 | Serial | 2364 | ||
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| Author |
Ivo Everts; Jan van Gemert; Theo Gevers | ||||
| Title | Per-patch Descriptor Selection using Surface and Scene Properties | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision | Abbreviated Journal | |
| Volume | 7577 | Issue | VI | Pages | 172-186 |
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| Abstract | Local image descriptors are generally designed for describing all possible image patches. Such patches may be subject to complex variations in appearance due to incidental object, scene and recording conditions. Because of this, a single-best descriptor for accurate image representation under all conditions does not exist. Therefore, we propose to automatically select from a pool of descriptors the one that is best suitable based on object surface and scene properties. These properties are measured on the fly from a single image patch through a set of attributes. Attributes are input to a classifier which selects the best descriptor. Our experiments on a large dataset of colored object patches show that the proposed selection method outperforms the best single descriptor and a-priori combinations of the descriptor pool. | ||||
| Address | Florence, Italy | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| ISSN | 0302-9743 | ISBN | 978-3-642-33782-6 | Medium | |
| Area | Expedition | Conference | ECCV | ||
| Notes | ALTRES;ISE | Approved | no | ||
| Call Number | Admin @ si @ EGG2012 | Serial | 2023 | ||
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| Author |
J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier | ||||
| Title | Improving Document Matching Performance by Local Descriptor Filtering | Type | Conference Article | ||
| Year | 2015 | Publication | 6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 1216 - 1220 | ||
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| Abstract | In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by using
ORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements. |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | CBDAR | ||
| Notes | DAG; 600.077; 601.223; 600.084 | Approved | no | ||
| Call Number | Admin @ si @ CRO2015a | Serial | 2680 | ||
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| Author |
J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier; Josep Llados | ||||
| Title | A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation | Type | Conference Article | ||
| Year | 2015 | Publication | 13th International Conference on Document Analysis and Recognition ICDAR2015 | Abbreviated Journal | |
| Volume | Issue | Pages | 621-625 | ||
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| Abstract | This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015 |
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| Address | Nancy; France; August 2015 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.084; 600.061; 601.223; 600.077 | Approved | no | ||
| Call Number | Admin @ si @ CRO2015b | Serial | 2685 | ||
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| Author |
J. Chazalon; P. Gomez-Kramer; Jean-Christophe Burie; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; Nibal Nayef; Marçal Rusiñol; N. Sidere; Jean-Marc Ogier | ||||
| Title | SmartDoc 2017 Video Capture: Mobile Document Acquisition in Video Mode | Type | Conference Article | ||
| Year | 2017 | Publication | 1st International Workshop on Open Services and Tools for Document Analysis | Abbreviated Journal | |
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| Abstract | As mobile document acquisition using smartphones is getting more and more common, along with the continuous improvement of mobile devices (both in terms of computing power and image quality), we can wonder to which extent mobile phones can replace desktop scanners. Modern applications can cope with perspective distortion and normalize the contrast of a document page captured with a smartphone, and in some cases like bottle labels or posters, smartphones even have the advantage of allowing the acquisition of non-flat or large documents. However, several cases remain hard to handle, such as reflective documents (identity cards, badges, glossy magazine cover, etc.) or large documents for which some regions require an important amount of detail. This paper introduces the SmartDoc 2017 benchmark (named “SmartDoc Video Capture”), which aims at
assessing whether capturing documents using the video mode of a smartphone could solve those issues. The task under evaluation is both a stitching and a reconstruction problem, as the user can move the device over different parts of the document to capture details or try to erase highlights. The material released consists of a dataset, an evaluation method and the associated tool, a sample method, and the tools required to extend the dataset. All the components are released publicly under very permissive licenses, and we particularly cared about maximizing the ease of understanding, usage and improvement. |
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| Address | Kyoto; Japan; November 2017 | ||||
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| Area | Expedition | Conference | ICDAR-OST | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ CGB2017 | Serial | 2997 | ||
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| Author |
J. Elder; Fadi Dornaika; Y. Hou; R. Goldstein | ||||
| Title | Attentive wide-field sensing for visual telepresence and surveillance | Type | Book Chapter | ||
| Year | 2005 | Publication | L. Itti, G. Rees and J. Tsotsos (editors), Neurobiology of Attention, Academic Press / Elsevier | Abbreviated Journal | |
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| Call Number | Admin @ si @ EDH2005 | Serial | 604 | ||
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| Author |
J. Filipe; Juan Andrade; J.L. Ferrier | ||||
| Title | FAF 2005 | Type | Miscellaneous | ||
| Year | 2005 | Publication | Proceedings of the 2nd International Conference on Informatics in Control, Automation and Robotics, INSTICC Press | Abbreviated Journal | |
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| Address | Barcelona (Spain) | ||||
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| Call Number | Admin @ si @ FAF2005 | Serial | 609 | ||
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| Author |
J. Garcia; J.M. Sanchez; X. Orriols; X. Binefa | ||||
| Title | Chromatic aberration and depth extraction. | Type | Conference Article | ||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition | Abbreviated Journal | |
| Volume | 1 | Issue | Pages | 762-765 | |
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| Address | Barcelona. | ||||
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| Call Number | Admin @ si @ GSO2000 | Serial | 226 | ||
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