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Md.Mostafa Kamal Sarker; Mohammed Jabreel; , Hatem A. Rashwan; Syeda Furruka Banu; Petia Radeva; Domenec Puig |
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Title |
CuisineNet: Food Attributes Classification using Multi-scale Convolution Network |
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Conference Article |
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Year |
2018 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference of the Catalan Association for Artificial Intelligence |
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365-372 |
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Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models. |
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Roses; catalonia; October 2018 |
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CCIA |
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MILAB; no menciona |
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Admin @ si @ SJR2018 |
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3113 |
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Md.Mostafa Kamal Sarker; , Hatem A. Rashwan; Farhan Akram; Syeda Furruka Banu; Adel Saleh; Vivek Kumar Singh; Forhad U. H. Chowdhury; Saddam Abdulwahab; Santiago Romani; Petia Radeva; Domenec Puig |
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Title |
SLSDeep: Skin Lesion Segmentation Based on Dilated Residual and Pyramid Pooling Networks. |
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Conference Article |
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Year |
2018 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Medical Image Computing & Computer Assisted Intervention |
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2 |
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21-29 |
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Skin lesion segmentation (SLS) in dermoscopic images is a crucial task for automated diagnosis of melanoma. In this paper, we present a robust deep learning SLS model, so-called SLSDeep, which is represented as an encoder-decoder network. The encoder network is constructed by dilated residual layers, in turn, a pyramid pooling network followed by three convolution layers is used for the decoder. Unlike the traditional methods employing a cross-entropy loss, we investigated a loss function by combining both Negative Log Likelihood (NLL) and End Point Error (EPE) to accurately segment the melanoma regions with sharp boundaries. The robustness of the proposed model was evaluated on two public databases: ISBI 2016 and 2017 for skin lesion analysis towards melanoma detection challenge. The proposed model outperforms the state-of-the-art methods in terms of segmentation accuracy. Moreover, it is capable to segment more than 100 images of size 384x384 per second on a recent GPU. |
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Granada; Espanya; September 2018 |
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MICCAI |
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MILAB; no proj |
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no |
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Admin @ si @ SRA2018 |
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3112 |
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Author |
David Vazquez; Antonio Lopez; Daniel Ponsa |
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Title |
Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection |
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Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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3492 - 3495 |
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Pedestrian Detection; Domain Adaptation; Virtual worlds |
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Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1). |
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Tsukuba Science City, Japan |
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IEEE |
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Tsukuba Science City, JAPAN |
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1051-4651 |
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978-1-4673-2216-4 |
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ADAS |
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no |
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ADAS @ adas @ VLP2012 |
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1981 |
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Author |
Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios |
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Title |
Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs |
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Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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2664 - 2667 |
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We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ADAS |
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no |
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Admin @ si @ RSL2012a; |
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2032 |
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Author |
German Ros; Jesus Martinez del Rincon; Gines Garcia-Mateos |
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Title |
Articulated Particle Filter for Hand Tracking |
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Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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3581 - 3585 |
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This paper proposes a new version of Particle Filter, called Articulated Particle Filter – ArPF -, which has been specifically designed for an efficient sampling of hierarchical spaces, generated by articulated objects. Our approach decomposes the articulated motion into layers for efficiency purposes, making use of a careful modeling of the diffusion noise along with its propagation through the articulations. This produces an increase of accuracy and prevent for divergences. The algorithm is tested on hand tracking due to its complex hierarchical articulated nature. With this purpose, a new dataset generation tool for quantitative evaluation is also presented in this paper. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ADAS |
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no |
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Admin @ si @ RMG2012 |
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2031 |
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Author |
Francisco Cruz; Oriol Ramos Terrades |
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Title |
Document segmentation using relative location features |
Type |
Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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1562-1565 |
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In this paper we evaluate the use of Relative Location Features (RLF) on a historical document segmentation task, and compare the quality of the results obtained on structured and unstructured documents using RLF and not using them. We prove that using these features improve the final segmentation on documents with a strong structure, while their application on unstructured documents does not show significant improvement. Although this paper is not focused on segmenting unstructured documents, results obtained on a benchmark dataset are equal or even overcome previous results of similar works. |
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Tsukuba Science City, Japan |
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DAG |
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no |
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Admin @ si @ CrR2012 |
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2051 |
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Author |
Volkmar Frinken; Francisco Zamora; Salvador España; Maria Jose Castro; Andreas Fischer; Horst Bunke |
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Title |
Long-Short Term Memory Neural Networks Language Modeling for Handwriting Recognition |
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Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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701-704 |
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Unconstrained handwritten text recognition systems maximize the combination of two separate probability scores. The first one is the observation probability that indicates how well the returned word sequence matches the input image. The second score is the probability that reflects how likely a word sequence is according to a language model. Current state-of-the-art recognition systems use statistical language models in form of bigram word probabilities. This paper proposes to model the target language by means of a recurrent neural network with long-short term memory cells. Because the network is recurrent, the considered context is not limited to a fixed size especially as the memory cells are designed to deal with long-term dependencies. In a set of experiments conducted on the IAM off-line database we show the superiority of the proposed language model over statistical n-gram models. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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DAG |
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no |
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Admin @ si @ FZE2012 |
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2052 |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Andrew Bagdanov; Josep Llados |
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Title |
Multipage Document Retrieval by Textual and Visual Representations |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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521-524 |
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In this paper we present a multipage administrative document image retrieval system based on textual and visual representations of document pages. Individual pages are represented by textual or visual information using a bag-of-words framework. Different fusion strategies are evaluated which allow the system to perform multipage document retrieval on the basis of a single page retrieval system. Results are reported on a large dataset of document images sampled from a banking workflow. |
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Tsukuba Science City, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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DAG |
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no |
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Admin @ si @ RKB2012 |
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2053 |
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Author |
Antonio Hernandez; Miguel Angel Bautista; Xavier Perez Sala; Victor Ponce; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
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Title |
BoVDW: Bag-of-Visual-and-Depth-Words for Gesture Recognition |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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We present a Bag-of-Visual-and-Depth-Words (BoVDW) model for gesture recognition, an extension of the Bag-of-Visual-Words (BoVW) model, that benefits from the multimodal fusion of visual and depth features. State-of-the-art RGB and depth features, including a new proposed depth descriptor, are analysed and combined in a late fusion fashion. The method is integrated in a continuous gesture recognition pipeline, where Dynamic Time Warping (DTW) algorithm is used to perform prior segmentation of gestures. Results of the method in public data sets, within our gesture recognition pipeline, show better performance in comparison to a standard BoVW model. |
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1051-4651 |
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978-1-4673-2216-4 |
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HuPBA;MV |
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no |
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Admin @ si @ HBP2012 |
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2122 |
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Author |
Anjan Dutta; Jaume Gibert; Josep Llados; Horst Bunke; Umapada Pal |
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Title |
Combination of Product Graph and Random Walk Kernel for Symbol Spotting in Graphical Documents |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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1663-1666 |
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This paper explores the utilization of product graph for spotting symbols on graphical documents. Product graph is intended to find the candidate subgraphs or components in the input graph containing the paths similar to the query graph. The acute angle between two edges and their length ratio are considered as the node labels. In a second step, each of the candidate subgraphs in the input graph is assigned with a distance measure computed by a random walk kernel. Actually it is the minimum of the distances of the component to all the components of the model graph. This distance measure is then used to eliminate dissimilar components. The remaining neighboring components are grouped and the grouped zone is considered as a retrieval zone of a symbol similar to the queried one. The entire method works online, i.e., it doesn't need any preprocessing step. The present paper reports the initial results of the method, which are very encouraging. |
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Tsukuba, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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DAG |
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no |
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Admin @ si @ DGL2012 |
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2125 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Text/graphic separation using a sparse representation with multi-learned dictionaries |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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Graphics Recognition; Layout Analysis; Document Understandin |
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In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds. |
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Tsukuba |
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DAG |
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no |
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Admin @ si @ DTR2012a |
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2135 |
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Author |
Josep M. Gonfaus; Theo Gevers; Arjan Gijsenij; Xavier Roca; Jordi Gonzalez |
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Title |
Edge Classification using Photo-Geo metric features |
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Conference Article |
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2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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1497 - 1500 |
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Edges are caused by several imaging cues such as shadow, material and illumination transitions. Classification methods have been proposed which are solely based on photometric information, ignoring geometry to classify the physical nature of edges in images. In this paper, the aim is to present a novel strategy to handle both photometric and geometric information for edge classification. Photometric information is obtained through the use of quasi-invariants while geometric information is derived from the orientation and contrast of edges. Different combination frameworks are compared with a new principled approach that captures both information into the same descriptor. From large scale experiments on different datasets, it is shown that, in addition to photometric information, the geometry of edges is an important visual cue to distinguish between different edge types. It is concluded that by combining both cues the performance improves by more than 7% for shadows and highlights. |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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Conference |
ICPR |
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Notes |
ISE |
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no |
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Call Number |
Admin @ si @ GGG2012b |
Serial |
2142 |
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Permanent link to this record |
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Author |
Adela Barbulescu; Wenjuan Gong; Jordi Gonzalez; Thomas B. Moeslund; Xavier Roca |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
3D Human Pose Estimation Using 2D Body Part Detectors |
Type |
Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition |
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Pages |
2484 - 2487 |
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Abstract |
Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional input data, such as silhouettes, or controlled camera settings. We present a framework that is capable of estimating the 3D pose of a person from single images or monocular image sequences without requiring background information and which is robust to camera variations. The framework models the non-linearity present in human pose estimation as it benefits from flexible learning approaches, including a highly customizable 2D detector. Results on the HumanEva benchmark show how they perform and influence the quality of the 3D pose estimates. |
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Tsubuka, Japan |
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Edition |
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ISSN |
1051-4651 |
ISBN |
978-1-4673-2216-4 |
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ICPR |
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Notes |
ISE |
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no |
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Call Number |
Admin @ si @ BGG2012 |
Serial |
2172 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Antonio Hernandez; Victor Ponce; Xavier Perez Sala; Xavier Baro; Oriol Pujol; Cecilio Angulo; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Probability-based Dynamic TimeWarping for Gesture Recognition on RGB-D data |
Type |
Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
Abbreviated Journal |
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Volume |
7854 |
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Pages |
126-135 |
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Abstract |
Dynamic Time Warping (DTW) is commonly used in gesture recognition tasks in order to tackle the temporal length variability of gestures. In the DTW framework, a set of gesture patterns are compared one by one to a maybe infinite test sequence, and a query gesture category is recognized if a warping cost below a certain threshold is found within the test sequence. Nevertheless, either taking one single sample per gesture category or a set of isolated samples may not encode the variability of such gesture category. In this paper, a probability-based DTW for gesture recognition is proposed. Different samples of the same gesture pattern obtained from RGB-Depth data are used to build a Gaussian-based probabilistic model of the gesture. Finally, the cost of DTW has been adapted accordingly to the new model. The proposed approach is tested in a challenging scenario, showing better performance of the probability-based DTW in comparison to state-of-the-art approaches for gesture recognition on RGB-D data. |
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Springer Berlin Heidelberg |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-40302-6 |
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WDIA |
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Notes |
MILAB; OR;HuPBA;MV |
Approved |
no |
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Call Number |
Admin @ si @ BHP2012 |
Serial |
2120 |
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Permanent link to this record |
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Author |
Miguel Reyes; Albert Clapes; Luis Felipe Mejia; Jose Ramirez; Juan R Revilla; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title |
Posture Analysis and Range of Movement Estimation using Depth Maps |
Type |
Conference Article |
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Year |
2012 |
Publication ![sorted by Publication field, ascending order (up)](img/sort_asc.gif) |
21st International Conference on Pattern Recognition International Workshop on Depth Image Analysis |
Abbreviated Journal |
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Volume |
7854 |
Issue |
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Pages |
97-105 |
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Keywords |
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Abstract |
World Health Organization estimates that 80% of the world population is affected of back pain during his life. Current practices to analyze back problems are expensive, subjective, and invasive. In this work, we propose a novel tool for posture and range of movement estimation based on the analysis of 3D information from depth maps. Given a set of keypoints defined by the user, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matching using a novel point-to-point fitting procedure, and accurate measurements about posture, spinal curvature, and range of movement are computed. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent musculoskeletal disorders, such as back pain, as well as tracking the posture evolution of patients in rehabilitation treatments. |
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Springer Berlin Heidelberg |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0302-9743 |
ISBN |
978-3-642-40302-6 |
Medium |
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Area |
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Expedition |
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Conference |
WDIA |
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Notes |
HuPBA;MILAB |
Approved |
no |
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Call Number |
Admin @ si @ RCM2012 |
Serial |
2121 |
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Permanent link to this record |