| Home | << 1 2 3 4 5 6 7 8 >> |
|
| Records | Links | |||||
|---|---|---|---|---|---|---|
| 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 | |
| Keywords | ||||||
| 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. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
Notes  |
CIC; 600.087 | Approved | no | |||
| Call Number | Admin @ si @ RVB2017 | Serial | 2892 | |||
| Permanent link to this record | ||||||
| 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 | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| 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). |
|||||
| Address | Venice; Italy; October 2017 | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ICCV-MBCC | |||
| Notes |
CIC; 600.087; 600.051 | Approved | no | |||
| Call Number | Admin @ si @ RaV2017 | Serial | 2984 | |||
| Permanent link to this record | ||||||
| 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 | ||
| Keywords | ||||||
| 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. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
CIC; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ RVL2019 | Serial | 3310 | |||
| Permanent link to this record | ||||||
| Author | Sagnik Das; Hassan Ahmed Sial; Ke Ma; Ramon Baldrich; Maria Vanrell; Dimitris Samaras |
|
||||
| Title | Intrinsic Decomposition of Document Images In-the-Wild | Type | Conference Article | |||
| Year | 2020 | Publication | 31st British Machine Vision Conference | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | Automatic document content processing is affected by artifacts caused by the shape
of the paper, non-uniform and diverse color of lighting conditions. Fully-supervised methods on real data are impossible due to the large amount of data needed. Hence, the current state of the art deep learning models are trained on fully or partially synthetic images. However, document shadow or shading removal results still suffer because: (a) prior methods rely on uniformity of local color statistics, which limit their application on real-scenarios with complex document shapes and textures and; (b) synthetic or hybrid datasets with non-realistic, simulated lighting conditions are used to train the models. In this paper we tackle these problems with our two main contributions. First, a physically constrained learning-based method that directly estimates document reflectance based on intrinsic image formation which generalizes to challenging illumination conditions. Second, a new dataset that clearly improves previous synthetic ones, by adding a large range of realistic shading and diverse multi-illuminant conditions, uniquely customized to deal with documents in-the-wild. The proposed architecture works in two steps. First, a white balancing module neutralizes the color of the illumination on the input image. Based on the proposed multi-illuminant dataset we achieve a good white-balancing in really difficult conditions. Second, the shading separation module accurately disentangles the shading and paper material in a self-supervised manner where only the synthetic texture is used as a weak training signal (obviating the need for very costly ground truth with disentangled versions of shading and reflectance). The proposed approach leads to significant generalization of document reflectance estimation in real scenes with challenging illumination. We extensively evaluate on the real benchmark datasets available for intrinsic image decomposition and document shadow removal tasks. Our reflectance estimation scheme, when used as a pre-processing step of an OCR pipeline, shows a 21% improvement of character error rate (CER), thus, proving the practical applicability. The data and code will be available at: https://github.com/cvlab-stonybrook/DocIIW. |
|||||
| Address | Virtual; September 2020 | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | BMVC | |||
| Notes |
CIC; 600.087; 600.140; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ DSM2020 | Serial | 3461 | |||
| Permanent link to this record | ||||||
| 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 | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| 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 | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | LIM | |||
| Notes |
CIC; 600.118; 600.140; | Approved | no | |||
| Call Number | Admin @ si @ SBV2020 | Serial | 3460 | |||
| Permanent link to this record | ||||||
| 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 | |
| Keywords | ||||||
| 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. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
CIC; 600.140; 600.12; 600.118 | Approved | no | |||
| Call Number | Admin @ si @ SBV2019 | Serial | 3311 | |||
| Permanent link to this record | ||||||
| Author | Robert Benavente; Gemma Sanchez; Ramon Baldrich; Maria Vanrell; Josep Llados |
|
||||
| Title | Normalized colour segmentation for human appearance description. | Type | Miscellaneous | |||
| Year | 2000 | Publication | 15 th International Conference on Pattern Recognition, 3:637–641. | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | Barcelona. | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BSB2000 | Serial | 223 | |||
| Permanent link to this record | ||||||
| Author | Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
|
||||
| Title | Textual Descriptors for browsing people by visual appearence. | Type | Conference Article | |||
| Year | 2002 | Publication | 5è. Congrés Català d’Intel·ligència Artificial CCIA | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | Image retrieval, textual descriptors, colour naming, colour normalization, graph matching. | |||||
| Abstract | This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building. | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ TBB2002a | Serial | 287 | |||
| Permanent link to this record | ||||||
| Author | Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
|
||||
| Title | Textual Descriptions for Browsing People by Visual Apperance. | Type | Book Chapter | |||
| Year | 2002 | Publication | Lecture Notes in Artificial Intelligence | Abbreviated Journal | ||
| Volume | 2504 | Issue | Pages | 419-429 | ||
| Keywords | ||||||
| Abstract | This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building | |||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Springer Verlag | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ TBB2002b | Serial | 319 | |||
| Permanent link to this record | ||||||
| Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
|
||||
| Title | High-Level Clothes Description Based on Color-Texture and Structural Features | Type | Book Chapter | |||
| Year | 2003 | Publication | Lecture Notes in Computer Science | Abbreviated Journal | ||
| Volume | 2652 | Issue | Pages | 108–116 | ||
| Keywords | ||||||
| Abstract | This work is a part of a surveillance system where content- based image retrieval is done in terms of people appearance. Given an image of a person, our work provides an automatic description of his clothing according to the colour, texture and structural composition of its garments. We present a two-stage process composed by image segmentation and a region-based interpretation. We segment an image by modelling it due to an attributed graph and applying a hybrid method that follows a split-and-merge strategy. We propose the interpretation of five cloth combinations that are modelled in a graph structure in terms of region features. The interpretation is viewed as a graph matching with an associated cost between the segmentation and the cloth models. Fi- nally, we have tested the process with a ground-truth of one hundred images. | |||||
| Address | Springer-Verlag | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BTL2003a | Serial | 368 | |||
| Permanent link to this record | ||||||
| Author | Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
|
||||
| Title | High-Level Clothes Description Based on Colour-Texture and Structural Features | Type | Conference Article | |||
| Year | 2003 | Publication | 1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | Palma de Mallorca | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC | Approved | no | |||
| Call Number | CAT @ cat @ BTL2003b | Serial | 369 | |||
| Permanent link to this record | ||||||
| Author | Ramon Baldrich; Ricardo Toledo; Ernest Valveny; Maria Vanrell |
|
||||
| Title | Perceptual Colour Image Segmentation. | Type | Miscellaneous | |||
| Year | 2002 | Publication | Proceeding of the Second IASTED International Conference Visualization, Imaging and Image Proceesing VIIP 2002: 145–150. | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
DAG;CIC;ADAS | Approved | no | |||
| Call Number | CAT @ cat @ BTV2002 | Serial | 290 | |||
| Permanent link to this record | ||||||
| Author | Maria Vanrell; Jordi Vitria |
|
||||
| Title | Mathematical Morphology, Granulometries and Texture Perception. | Type | Miscellaneous | |||
| Year | 1993 | Publication | SPIE International Symposium on Optical Instrumentation and Applied Science (Conference on image Algebra and Morphological image Processing IV). | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | San Diego; CA; USA | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
OR;CIC;MV | Approved | no | |||
| Call Number | BCNPCL @ bcnpcl @ VaV1993 | Serial | 178 | |||
| Permanent link to this record | ||||||
| Author | X. Binefa; Jordi Vitria; Maria Vanrell |
|
||||
| Title | Reconstruccion tridimensional de imagenes Microscopicas. | Type | Miscellaneous | |||
| Year | 1992 | Publication | V Simposium Nacional de Reconocimiento de Formas y Analisis de Imagenes | Abbreviated Journal | ||
| Volume | Issue | Pages | ||||
| Keywords | ||||||
| Abstract | ||||||
| Address | ||||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | ||||
| Notes |
OR;CIC;MV | Approved | no | |||
| Call Number | BCNPCL @ bcnpcl @ BVV1992b | Serial | 255 | |||
| Permanent link to this record | ||||||