Records |
Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
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Conference Article |
Year |
2007 |
Publication |
Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
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163 |
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197–204 |
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978-1-58603-798-7 |
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CCIA’07 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRF2007a |
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831 |
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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
Title |
Using the Average Landmark Vector Method for Robot Homing |
Type |
Conference Article |
Year |
2007 |
Publication |
Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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no |
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Admin @ si @ GRL2007 |
Serial |
899 |
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Author |
Jon Almazan |
Title |
Deforming the Blurred Shape Model for Shape Description and Recognition |
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Report |
Year |
2010 |
Publication |
CVC Technical Report |
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163 |
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Master's thesis |
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no |
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Admin @ si @ Alm2010 |
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1354 |
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Author |
Monica Piñol |
Title |
Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning |
Type |
Report |
Year |
2010 |
Publication |
CVC Technical Report |
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162 |
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Bellaterra (Barcelona) |
Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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ADAS |
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no |
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Admin @ si @ Piñ2010 |
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1936 |
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Author |
David Fernandez |
Title |
Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors |
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Report |
Year |
2010 |
Publication |
CVC Technical Report |
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161 |
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Master's thesis |
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DAG |
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no |
Call Number |
Admin @ si @ Fer2010b |
Serial |
1353 |
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Author |
Ekain Artola |
Title |
Human Attention Map Prediction Combining Visual Features |
Type |
Report |
Year |
2010 |
Publication |
CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
160 |
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Bachelor's thesis |
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no |
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Admin @ si @ Art2010 |
Serial |
1352 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
Type |
Journal Article |
Year |
2022 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
160 |
Issue |
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Pages |
43-49 |
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Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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DAG; 600.121; 600.162; 602.230 |
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no |
Call Number |
Admin @ si @ SFK2022 |
Serial |
3736 |
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Author |
Anjan Dutta |
Title |
Symbol Spotting in Graphical Documents by Serialized Subgraph Matching |
Type |
Report |
Year |
2010 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
159 |
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Master's thesis |
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DAG |
Approved |
no |
Call Number |
Admin @ si @ Dut2010 |
Serial |
1351 |
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Author |
Lluis Pere de las Heras |
Title |
Syntactic Model for Semantic Document Analysis |
Type |
Report |
Year |
2010 |
Publication |
CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
158 |
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no |
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Admin @ si @ Per2010 |
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1350 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad |
Title |
Locality Regularized Group Sparse Coding for Action Recognition |
Type |
Journal Article |
Year |
2017 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
158 |
Issue |
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Pages |
106-114 |
Keywords |
Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition |
Abstract |
Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. |
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HuPBA; no proj |
Approved |
no |
Call Number |
Admin @ si @ BGE2017 |
Serial |
3014 |
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Author |
Patricia Marquez |
Title |
Conditions Ensuring Accuracy of Local Optical Flow Schemes |
Type |
Report |
Year |
2010 |
Publication |
CVC Tehcnical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
157 |
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Accurate computation of optical flow is a key-point in many image processing fields. Detection of anomalous and unpredicted agents (such as pedestrians, bikers or cars) in urban scenes or pathology discrimination in medical imaging sequences, to mention just a two. The above kinds sequences present two main difficulties for standard optical flow techniques. On one hand, variability in acquisition conditions (illuminance, medical imaging modality, ...) force an alterantive representation for images fulfilling the britghtness constancy constrain. On the hand, current variational schemes produce oversmoothed fields unable to properly model discontinuous behaviours such as collisions or functionless pathological areas. This master project explores the abilities and limitations of local and global optical flow approaches. The master student will put especial emphasis in the theoretical grounds behind in order to design a variational framework combining the theoretical advantages of the considered techniques. In particular an optical flow based on Gabor phase tracking (developed in the group for medical imaging) will be generalized to urban scenes. |
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Master's thesis |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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no |
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IAM @ iam @ Mar2010 |
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1582 |
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Author |
Zhanwu Xiong |
Title |
A Pompd Model for Active Camera Control |
Type |
Report |
Year |
2010 |
Publication |
CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
156 |
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Master's thesis |
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ISE |
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no |
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Admin @ si @ Xio2010 |
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1356 |
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Author |
Debora Gil; Jose Maria-Carazo; Roberto Marabini |
Title |
On the nature of 2D crystal unbending |
Type |
Journal Article |
Year |
2006 |
Publication |
Journal of Structural Biology |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
156 |
Issue |
3 |
Pages |
546-555 |
Keywords |
Electron microscopy |
Abstract |
Crystal unbending, the process that aims to recover a perfect crystal from experimental data, is one of the more important steps in electron crystallography image processing. The unbending process involves three steps: estimation of the unit cell displacements from their ideal positions, extension of the deformation field to the whole image and transformation of the image in order to recover an ideal crystal. In this work, we present a systematic analysis of the second step oriented to address two issues. First, whether the unit cells remain undistorted and only the distance between them should be changed (rigid case) or should be modified with the same deformation suffered by the whole crystal (elastic case). Second, the performance of different extension algorithms (interpolation versus approximation) is explored. Our experiments show that there is no difference between elastic and rigid cases or among the extension algorithms. This implies that the deformation fields are constant over large areas. Furthermore, our results indicate that the main source of error is the transformation of the crystal image. |
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1047-8477 |
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IAM; |
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no |
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IAM @ iam @ GCM2006 |
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1519 |
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Author |
Nataliya Shapovalova |
Title |
On Importance of Interaction and Context |
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Report |
Year |
2010 |
Publication |
CVC Technical Report |
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Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
155 |
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Master's thesis |
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no |
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Admin @ si @ Sha2010 |
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1355 |
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Author |
Mariella Dimiccoli; Marc Bolaños; Estefania Talavera; Maedeh Aghaei; Stavri G. Nikolov; Petia Radeva |
Title |
SR-Clustering: Semantic Regularized Clustering for Egocentric Photo Streams Segmentation |
Type |
Journal Article |
Year |
2017 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
Volume ![sorted by Volume (numeric) field, descending order (down)](img/sort_desc.gif) |
155 |
Issue |
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55-69 |
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While wearable cameras are becoming increasingly popular, locating relevant information in large unstructured collections of egocentric images is still a tedious and time consuming processes. This paper addresses the problem of organizing egocentric photo streams acquired by a wearable camera into semantically meaningful segments. First, contextual and semantic information is extracted for each image by employing a Convolutional Neural Networks approach. Later, by integrating language processing, a vocabulary of concepts is defined in a semantic space. Finally, by exploiting the temporal coherence in photo streams, images which share contextual and semantic attributes are grouped together. The resulting temporal segmentation is particularly suited for further analysis, ranging from activity and event recognition to semantic indexing and summarization. Experiments over egocentric sets of nearly 17,000 images, show that the proposed approach outperforms state-of-the-art methods. |
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MILAB; 601.235 |
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Admin @ si @ DBT2017 |
Serial |
2714 |
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