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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automatic Detection of Intestinal Juices in Wireless Capsule Video Endoscopy |
Type |
Conference Article |
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Year |
2006 |
Publication |
18th International Conference on Pattern Recognition |
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Volume |
4 |
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719-722 |
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Keywords |
Clinical diagnosis , Endoscopes , Fluids and secretions , Gabor filters , Hospitals , Image sequence analysis , Intestines , Lighting , Shape , Visualization |
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Abstract |
Wireless capsule video endoscopy is a novel and challenging clinical technique, whose major reported drawback relates to the high amount of time needed for video visualization. In this paper, we propose a method for the rejection of the parts of the video resulting not valid for analysis by means of automatic detection of intestinal juices. We applied Gabor filters for the characterization of the bubble-like shape of intestinal juices in fasting patients. Our method achieves a significant reduction in visualization time, with no relevant loss of valid frames. The proposed approach is easily extensible to other image analysis scenarios where the described pattern of bubbles can be found. |
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Hong Kong |
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1051-4651 |
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0-7695-2521-0 |
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800 |
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ICPR |
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MV;OR;MILAB;SIAI |
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no |
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BCNPCL @ bcnpcl @ VSV2006b; IAM @ iam @ VSV2006g |
Serial |
727 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados; David Fernandez; Cristina Cañero |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Use case visual Bag-of-Words techniques for camera based identity document classification |
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Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
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721 - 725 |
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Abstract |
Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes. |
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Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.077; 600.061; |
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no |
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Admin @ si @ HRL2015a |
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2726 |
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Author |
Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera |
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Title |
Graph Cuts Optimization for Multi-Limb Human Segmentation in Depth Maps |
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Conference Article |
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Year |
2012 |
Publication |
25th IEEE Conference on Computer Vision and Pattern Recognition |
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726-732 |
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We present a generic framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs in depth maps. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α-β swap Graph-cuts algorithm. Moreover, depth of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches. |
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Portland; Oregon; June 2013 |
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IEEE Xplore |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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Conference |
CVPR |
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Notes |
MILAB;HuPBA |
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no |
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Call Number |
Admin @ si @ HZM2012b |
Serial |
2046 |
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Author |
Lluis Pere de las Heras; Oriol Ramos Terrades; Josep Llados |
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Title |
Attributed Graph Grammar for floor plan analysis |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
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726 - 730 |
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In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations. |
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Nancy; France; August 2015 |
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ICDAR |
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Notes |
DAG; 600.077; 600.061 |
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no |
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Call Number |
Admin @ si @ HRL2015b |
Serial |
2727 |
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Permanent link to this record |
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Author |
Margarita Torre; Beatriz Remeseiro; Petia Radeva; Fernando Martinez |
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Title |
DeepNEM: Deep Network Energy-Minimization for Agricultural Field Segmentation |
Type |
Journal Article |
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Year |
2020 |
Publication |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
Abbreviated Journal |
JSTAEOR |
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Volume |
13 |
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726-737 |
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Abstract |
One of the main characteristics of agricultural fields is that the appearance of different crops and their growth status, in an aerial image, is varied, and has a wide range of radiometric values and high level of variability. The extraction of these fields and their monitoring are activities that require a high level of human intervention. In this article, we propose a novel automatic algorithm, named deep network energy-minimization (DeepNEM), to extract agricultural fields in aerial images. The model-guided process selects the most relevant image clues extracted by a deep network, completes them and finally generates regions that represent the agricultural fields under a minimization scheme. DeepNEM has been tested over a broad range of fields in terms of size, shape, and content. Different measures were used to compare the DeepNEM with other methods, and to prove that it represents an improved approach to achieve a high-quality segmentation of agricultural fields. Furthermore, this article also presents a new public dataset composed of 1200 images with their parcels boundaries annotations. |
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MILAB |
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no |
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Call Number |
Admin @ si @ TRR2020 |
Serial |
3410 |
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Permanent link to this record |
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Author |
Lluis Gomez; Andres Mafla; Marçal Rusiñol; Dimosthenis Karatzas |
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Title |
Single Shot Scene Text Retrieval |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
11218 |
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Pages ![sorted by First Page field, ascending order (up)](img/sort_asc.gif) |
728-744 |
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Keywords |
Image retrieval; Scene text; Word spotting; Convolutional Neural Networks; Region Proposals Networks; PHOC |
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Abstract |
Textual information found in scene images provides high level semantic information about the image and its context and it can be leveraged for better scene understanding. In this paper we address the problem of scene text retrieval: given a text query, the system must return all images containing the queried text. The novelty of the proposed model consists in the usage of a single shot CNN architecture that predicts at the same time bounding boxes and a compact text representation of the words in them. In this way, the text based image retrieval task can be casted as a simple nearest neighbor search of the query text representation over the outputs of the CNN over the entire image
database. Our experiments demonstrate that the proposed architecture
outperforms previous state-of-the-art while it offers a significant increase
in processing speed. |
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Munich; September 2018 |
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LNCS |
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ECCV |
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DAG; 600.084; 601.338; 600.121; 600.129 |
Approved |
no |
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Call Number |
Admin @ si @ GMR2018 |
Serial |
3143 |
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Permanent link to this record |
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Author |
H. Emrah Tasli; Jan van Gemert; Theo Gevers |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Spot the differences: from a photograph burst to the single best picture |
Type |
Conference Article |
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Year |
2013 |
Publication |
21ST ACM International Conference on Multimedia |
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729-732 |
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Abstract |
With the rise of the digital camera, people nowadays typically take several near-identical photos of the same scene to maximize the chances of a good shot. This paper proposes a user-friendly tool for exploring a personal photo gallery for selecting or even creating the best shot of a scene between its multiple alternatives. This functionality is realized through a graphical user interface where the best viewpoint can be selected from a generated panorama of the scene. Once the viewpoint is selected, the user is able to go explore possible alternatives coming from the other images. Using this tool, one can explore a photo gallery efficiently. Moreover, additional compositions from other images are also possible. With such additional compositions, one can go from a burst of photographs to the single best one. Even funny compositions of images, where you can duplicate a person in the same image, are possible with our proposed tool. |
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Barcelona |
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ACM-MM |
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ALTRES;ISE |
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no |
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TGG2013 |
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2368 |
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Author |
Albert Gordo; Florent Perronnin |
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Title |
Asymmetric Distances for Binary Embeddings |
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Conference Article |
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Year |
2011 |
Publication |
IEEE Conference on Computer Vision and Pattern Recognition |
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729 - 736 |
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In large-scale query-by-example retrieval, embedding image signatures in a binary space offers two benefits: data compression and search efficiency. While most embedding algorithms binarize both query and database signatures, it has been noted that this is not strictly a requirement. Indeed, asymmetric schemes which binarize the database signatures but not the query still enjoy the same two benefits but may provide superior accuracy. In this work, we propose two general asymmetric distances which are applicable to a wide variety of embedding techniques including Locality Sensitive Hashing (LSH), Locality Sensitive Binary Codes (LSBC), Spectral Hashing (SH) and Semi-Supervised Hashing (SSH). We experiment on four public benchmarks containing up to 1M images and show that the proposed asymmetric distances consistently lead to large improvements over the symmetric Hamming distance for all binary embedding techniques. We also propose a novel simple binary embedding technique – PCA Embedding (PCAE) – which is shown to yield competitive results with respect to more complex algorithms such as SH and SSH. |
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Providence, RI |
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978-1-4577-0394-2 |
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CVPR |
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DAG |
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Admin @ si @ GoP2011; IAM @ iam @ GoP2011 |
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1817 |
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Author |
Bogdan Raducanu; Fadi Dornaika |
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Title |
Natural Facial Expression Recognition Using Dynamic and Static Schemes |
Type |
Conference Article |
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Year |
2009 |
Publication |
5th International Symposium on Visual Computing |
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5875 |
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730–739 |
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Affective computing is at the core of a new paradigm in HCI and AI represented by human-centered computing. Within this paradigm, it is expected that machines will be enabled with perceiving capabilities, making them aware about users’ affective state. The current paper addresses the problem of facial expression recognition from monocular videos sequences. We propose a dynamic facial expression recognition scheme, which is proven to be very efficient. Furthermore, it is conveniently compared with several static-based systems adopting different magnitude of facial expression. We provide evaluations of performance using Linear Discriminant Analysis (LDA), Non parametric Discriminant Analysis (NDA), and Support Vector Machines (SVM). We also provide performance evaluations using arbitrary test video sequences. |
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Las Vegas, USA |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-10330-8 |
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ISVC |
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OR;MV |
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no |
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BCNPCL @ bcnpcl @ RaD2009 |
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1257 |
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Author |
Xavier Otazu; Maria Vanrell; C. Alejandro Parraga |
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Title |
Multiresolution Wavelet Framework Models Brightness Induction Effects |
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Journal |
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2008 |
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Vision Research |
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VR |
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48 |
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5 |
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733–751 |
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CIC |
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no |
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CAT @ cat @ OVP2008a |
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927 |
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Author |
Jaume Garcia; Debora Gil; Luis Badiella; Aura Hernandez-Sabate; Francesc Carreras; Sandra Pujades; Enric Marti |
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Title |
A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function |
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Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
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29 |
Issue |
3 |
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733-745 |
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A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains. |
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0278-0062 |
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IAM |
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no |
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IAM @ iam @ GGH2010b |
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1507 |
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Author |
Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Title |
Unsupervised writer adaptation of whole-word HMMs with application to word-spotting |
Type |
Journal Article |
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Year |
2010 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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31 |
Issue |
8 |
Pages ![sorted by First Page field, ascending order (up)](img/sort_asc.gif) |
742–749 |
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Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis |
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In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.
Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition. |
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Elsevier |
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DAG |
Approved |
no |
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Call Number |
DAG @ dag @ RPS2010 |
Serial |
1290 |
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Permanent link to this record |
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Author |
Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
On-board camera extrinsic parameter estimation |
Type |
Journal Article |
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Year |
2006 |
Publication |
Electronics Letters |
Abbreviated Journal |
EL |
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Volume |
42 |
Issue |
13 |
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745–746 |
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Abstract |
An efficient technique for real-time estimation of camera extrinsic parameters is presented. It is intended to be used on on-board vision systems for driving assistance applications. The proposed technique is based on the use of a commercial stereo vision system that does not need any visual feature extraction. |
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IEE |
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ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ SGD2006a |
Serial |
655 |
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Author |
Marc Oliu; Javier Selva; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Folded Recurrent Neural Networks for Future Video Prediction |
Type |
Conference Article |
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Year |
2018 |
Publication |
15th European Conference on Computer Vision |
Abbreviated Journal |
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Volume |
11218 |
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745-761 |
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Abstract |
Future video prediction is an ill-posed Computer Vision problem that recently received much attention. Its main challenges are the high variability in video content, the propagation of errors through time, and the non-specificity of the future frames: given a sequence of past frames there is a continuous distribution of possible futures. This work introduces bijective Gated Recurrent Units, a double mapping between the input and output of a GRU layer. This allows for recurrent auto-encoders with state sharing between encoder and decoder, stratifying the sequence representation and helping to prevent capacity problems. We show how with this topology only the encoder or decoder needs to be applied for input encoding and prediction, respectively. This reduces the computational cost and avoids re-encoding the predictions when generating a sequence of frames, mitigating the propagation of errors. Furthermore, it is possible to remove layers from an already trained model, giving an insight to the role performed by each layer and making the model more explainable. We evaluate our approach on three video datasets, outperforming state of the art prediction results on MMNIST and UCF101, and obtaining competitive results on KTH with 2 and 3 times less memory usage and computational cost than the best scored approach. |
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Munich; September 2018 |
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ECCV |
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Notes |
HUPBA; no menciona |
Approved |
no |
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Call Number |
Admin @ si @ OSE2018 |
Serial |
3204 |
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Author |
Mehdi Mirza-Mohammadi; Sergio Escalera; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization |
Type |
Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
5702 |
Issue |
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748–756 |
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Abstract |
Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step. |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-03766-5 |
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Conference |
CAIP |
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Notes |
HuPBA; MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ MEP2009 |
Serial |
1185 |
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Permanent link to this record |