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Author |
Albert Gordo |
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Title |
A Cyclic Page Layout Descriptor for Document Classification & Retrieval |
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Year |
2009 |
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CVC Technical Report |
Abbreviated Journal |
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Volume |
128 |
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Computer Vision Center |
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Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC;DAG |
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no |
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Admin @ si @ Gor2009 |
Serial |
2387 |
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Author |
David Augusto Rojas |
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Title |
Colouring Local Feature Detection for Matching |
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2009 |
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CVC Technical Report |
Abbreviated Journal |
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133 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Roj2009 |
Serial |
2392 |
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Author |
Olivier Penacchio |
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Title |
Relative Density of L, M, S photoreceptors in the Human Retina |
Type |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
135 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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Admin @ si @ Pen2009 |
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2394 |
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Author |
Xavier Boix |
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Title |
Learning Conditional Random Fields for Stereo |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
136 |
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Computer Vision Center |
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Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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no |
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Admin @ si @ Boi2009 |
Serial |
2395 |
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Author |
Shida Beigpour |
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Title |
Physics-based Reflectance Estimation Applied to Recoloring |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
137 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Bellaterra, Barcelona |
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CIC |
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no |
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Call Number |
Admin @ si @ Bei2009 |
Serial |
2396 |
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Author |
Jose Carlos Rubio |
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Title |
Graph matching based on graphical models with application to vehicle tracking and classification at night |
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Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
144 |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ Rub2009 |
Serial |
2398 |
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Author |
Ivet Rafegas |
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Title |
Exploring Low-Level Vision Models. Case Study: Saliency Prediction |
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Report |
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Year |
2013 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
175 |
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Master's thesis |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ Raf2013 |
Serial |
2409 |
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Author |
Ricard Balague |
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Title |
Exploring the combination of color cues for intrinsic image decomposition |
Type |
Report |
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Year |
2014 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
178 |
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Abstract |
Intrinsic image decomposition is a challenging problem that consists in separating an image into its physical characteristics: reflectance and shading. This problem can be solved in different ways, but most methods have combined information from several visual cues. In this work we describe an extension of an existing method proposed by Serra et al. which considers two color descriptors and combines them by means of a Markov Random Field. We analyze in depth the weak points of the method and we explore more possibilities to use in both descriptors. The proposed extension depends on the combination of the cues considered to overcome some of the limitations of the original method. Our approach is tested on the MIT dataset and Beigpour et al. dataset, which contain images of real objects acquired under controlled conditions and synthetic images respectively, with their corresponding ground truth. |
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UAB; September 2014 |
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Master's thesis |
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Notes |
CIC; 600.074 |
Approved |
no |
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Call Number |
Admin @ si @ Bal2014 |
Serial |
2579 |
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Author |
Javier Vazquez |
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Title |
Content-based Colour Space |
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Year |
2007 |
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CVC Technical Report #116 |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ Vaz2007b |
Serial |
828 |
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Author |
Maria Vanrell |
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Title |
Exploring the space of behaviour of a texture perception algorithm |
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Year |
1997 |
Publication |
CVC Technical Report #12 |
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CIC |
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CAT @ cat @ Van1997 |
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523 |
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Author |
A. Martinez; Robert Benavente |
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Title |
The AR face database |
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1998 |
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CVC Technical Report #24 |
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CIC |
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CAT @ cat @ MaB1998 |
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534 |
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Author |
Robert Benavente |
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Title |
Dealing with colour variability: application to a colour naming task |
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1999 |
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CVC Technical Report #32 |
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CAT @ cat @ Ben1999 |
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53 |
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Author |
Jordi Vitria; Petia Radeva; X. Binefa; A. Pujol; Ernest Valveny; Robert Benavente; Craig Von Land |
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Real time recognition of pharmaceutical products by subspace methods |
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1999 |
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CVC Technical Report #35 |
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OR;MILAB;DAG;CIC;MV |
Approved |
no |
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BCNPCL @ bcnpcl @ VRB1999b |
Serial |
54 |
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Author |
Robert Benavente; Maria Vanrell |
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Title |
A colour naming experiment |
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2001 |
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CVC Technical Report #56 |
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CIC |
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no |
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CAT @ cat @ BeV2001 |
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78 |
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Author |
Anna Salvatella; Maria Vanrell |
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Title |
Towards a texture representation database |
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2002 |
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CVC Technical Report #60 |
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CIC |
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no |
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Call Number |
CAT @ cat @ SaV2002 |
Serial |
526 |
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Author |
Bojana Gajic; Ramon Baldrich |
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Title |
Cross-domain fashion image retrieval |
Type |
Conference Article |
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Year |
2018 |
Publication |
CVPR 2018 Workshop on Women in Computer Vision (WiCV 2018, 4th Edition) |
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19500-19502 |
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Abstract |
Cross domain image retrieval is a challenging task that implies matching images from one domain to their pairs from another domain. In this paper we focus on fashion image retrieval, which involves matching an image of a fashion item taken by users, to the images of the same item taken in controlled condition, usually by professional photographer. When facing this problem, we have different products
in train and test time, and we use triplet loss to train the network. We stress the importance of proper training of simple architecture, as well as adapting general models to the specific task. |
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Salt Lake City, USA; 22 June 2018 |
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CVPRW |
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Notes |
CIC; 600.087 |
Approved |
no |
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Call Number |
Admin @ si @ |
Serial |
3709 |
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| Permanent link to this record |
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Author |
Bojana Gajic; Ariel Amato; Ramon Baldrich; Joost Van de Weijer; Carlo Gatta |
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Title |
Area Under the ROC Curve Maximization for Metric Learning |
Type |
Conference Article |
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Year |
2022 |
Publication |
CVPR 2022 Workshop on Efficien Deep Learning for Computer Vision (ECV 2022, 5th Edition) |
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Training; Computer vision; Conferences; Area measurement; Benchmark testing; Pattern recognition |
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Abstract |
Most popular metric learning losses have no direct relation with the evaluation metrics that are subsequently applied to evaluate their performance. We hypothesize that training a metric learning model by maximizing the area under the ROC curve (which is a typical performance measure of recognition systems) can induce an implicit ranking suitable for retrieval problems. This hypothesis is supported by previous work that proved that a curve dominates in ROC space if and only if it dominates in Precision-Recall space. To test this hypothesis, we design and maximize an approximated, derivable relaxation of the area under the ROC curve. The proposed AUC loss achieves state-of-the-art results on two large scale retrieval benchmark datasets (Stanford Online Products and DeepFashion In-Shop). Moreover, the AUC loss achieves comparable performance to more complex, domain specific, state-of-the-art methods for vehicle re-identification. |
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New Orleans, USA; 20 June 2022 |
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CVPRW |
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CIC; LAMP; |
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Admin @ si @ GAB2022 |
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3700 |
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Author |
Jaime Moreno; Xavier Otazu |
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Title |
Image coder based on Hilbert scanning of embedded quadTrees |
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Conference Article |
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2011 |
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Data Compression Conference |
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470-470 |
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Abstract |
In this work we present an effective and computationally simple algorithm for image compression based on Hilbert Scanning of Embedded quadTrees (Hi-SET). It allows to represent an image as an embedded bitstream along a fractal function. Embedding is an important feature of modern image compression algorithms, in this way Salomon in [1, pg. 614] cite that another feature and perhaps a unique one is the fact of achieving the best quality for the number of bits input by the decoder at any point during the decoding. Hi-SET possesses also this latter feature. Furthermore, the coder is based on a quadtree partition strategy, that applied to image transformation structures such as discrete cosine or wavelet transform allows to obtain an energy clustering both in frequency and space. The coding algorithm is composed of three general steps, using just a list of significant pixels. |
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DCC |
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Notes |
CIC |
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no |
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Call Number |
Admin @ si @ MoO2011b |
Serial |
2177 |
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