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Author |
Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol |
![goto web page url](img/www.gif)
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Title |
Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning |
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Conference Article |
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Year |
2023 |
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17th International Conference on Document Analysis and Recognition |
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14192 |
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106-121 |
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Scene Text Detection; Scene Text Recognition; Transformer Acceleration |
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Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds. |
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San Jose; CA; USA; August 2023 |
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DAG |
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Admin @ si @ GKR2023a |
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3907 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Scene Representations for Autonomous Driving: an approach based on polygonal primitives |
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Conference Article |
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Year |
2015 |
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2nd Iberian Robotics Conference ROBOT2015 |
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417 |
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503-515 |
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Scene reconstruction; Point cloud; Autonomous vehicles |
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In this paper, we present a novel methodology to compute a 3D scene
representation. The algorithm uses macro scale polygonal primitives to model the scene. This means that the representation of the scene is given as a list of large scale polygons that describe the geometric structure of the environment. Results show that the approach is capable of producing accurate descriptions of the scene. In addition, the algorithm is very efficient when compared to other techniques. |
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Lisboa; Portugal; November 2015 |
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ROBOT |
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ADAS; 600.076; 600.086 |
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no |
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Admin @ si @ OSS2015a |
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2662 |
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Author |
Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Incremental texture mapping for autonomous driving |
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Journal Article |
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Year |
2016 |
Publication |
Robotics and Autonomous Systems |
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RAS |
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84 |
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113-128 |
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Keywords ![sorted by Keywords field, descending order (down)](img/sort_desc.gif) |
Scene reconstruction; Autonomous driving; Texture mapping |
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Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. |
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ADAS; 600.086 |
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no |
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Admin @ si @ OSS2016b |
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2912 |
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Author |
Carola Figueroa Flores; David Berga; Joost Van de Weijer; Bogdan Raducanu |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Saliency for free: Saliency prediction as a side-effect of object recognition |
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Journal Article |
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Year |
2021 |
Publication |
Pattern Recognition Letters |
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PRL |
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150 |
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1-7 |
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Saliency maps; Unsupervised learning; Object recognition |
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Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects instead of the background. So far, computational methods for saliency estimation required the explicit generation of a saliency map, process which is usually achieved via eyetracking experiments on still images. This is a tedious process that needs to be repeated for each new dataset. In the current paper, we demonstrate that is possible to automatically generate saliency maps without ground-truth. In our approach, saliency maps are learned as a side effect of object recognition. Extensive experiments carried out on both real and synthetic datasets demonstrated that our approach is able to generate accurate saliency maps, achieving competitive results when compared with supervised methods. |
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LAMP; 600.147; 600.120 |
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no |
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Admin @ si @ FBW2021 |
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3559 |
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Author |
Estefania Talavera; Carolin Wuerich; Nicolai Petkov; Petia Radeva |
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Title |
Topic modelling for routine discovery from egocentric photo-streams |
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Journal Article |
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Year |
2020 |
Publication |
Pattern Recognition |
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PR |
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104 |
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107330 |
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Keywords ![sorted by Keywords field, descending order (down)](img/sort_desc.gif) |
Routine; Egocentric vision; Lifestyle; Behaviour analysis; Topic modelling |
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Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed. |
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MILAB; no proj |
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no |
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Admin @ si @ TWP2020 |
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3435 |
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Author |
Estefania Talavera; Nicolai Petkov; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Unsupervised Routine Discovery in Egocentric Photo-Streams |
Type |
Conference Article |
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2019 |
Publication |
18th International Conference on Computer Analysis of Images and Patterns |
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11678 |
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576-588 |
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Routine discovery; Lifestyle; Egocentric vision; Behaviour analysis |
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The routine of a person is defined by the occurrence of activities throughout different days, and can directly affect the person’s health. In this work, we address the recognition of routine related days. To do so, we rely on egocentric images, which are recorded by a wearable camera and allow to monitor the life of the user from a first-person view perspective. We propose an unsupervised model that identifies routine related days, following an outlier detection approach. We test the proposed framework over a total of 72 days in the form of photo-streams covering around 2 weeks of the life of 5 different camera wearers. Our model achieves an average of 76% Accuracy and 68% Weighted F-Score for all the users. Thus, we show that our framework is able to recognise routine related days and opens the door to the understanding of the behaviour of people. |
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Salermo; Italy; September 2019 |
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CAIP |
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MILAB; no proj |
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no |
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Admin @ si @ TPR2019a |
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3367 |
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Author |
Sangheeta Roy; Palaiahnakote Shivakumara; Namita Jain; Vijeta Khare; Anjan Dutta; Umapada Pal; Tong Lu |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Rough-Fuzzy based Scene Categorization for Text Detection and Recognition in Video |
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Journal Article |
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2018 |
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Pattern Recognition |
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PR |
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80 |
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64-82 |
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Rough set; Fuzzy set; Video categorization; Scene image classification; Video text detection; Video text recognition |
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Scene image or video understanding is a challenging task especially when number of video types increases drastically with high variations in background and foreground. This paper proposes a new method for categorizing scene videos into different classes, namely, Animation, Outlet, Sports, e-Learning, Medical, Weather, Defense, Economics, Animal Planet and Technology, for the performance improvement of text detection and recognition, which is an effective approach for scene image or video understanding. For this purpose, at first, we present a new combination of rough and fuzzy concept to study irregular shapes of edge components in input scene videos, which helps to classify edge components into several groups. Next, the proposed method explores gradient direction information of each pixel in each edge component group to extract stroke based features by dividing each group into several intra and inter planes. We further extract correlation and covariance features to encode semantic features located inside planes or between planes. Features of intra and inter planes of groups are then concatenated to get a feature matrix. Finally, the feature matrix is verified with temporal frames and fed to a neural network for categorization. Experimental results show that the proposed method outperforms the existing state-of-the-art methods, at the same time, the performances of text detection and recognition methods are also improved significantly due to categorization. |
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DAG; 600.097; 600.121 |
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Admin @ si @ RSJ2018 |
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3096 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Structural Recognition of hand drawn floor plans |
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Conference Article |
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1996 |
Publication |
VI National Symposium on Pattern Recognition and Image Analysis |
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Rotational Symmetry; Reflectional Symmetry; String Matching. |
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A system to recognize hand drawn architectural drawings in a CAD environment has been deve- loped. In this paper we focus on its high level interpretation module. To interpret a floor plan, the system must identify several building elements, whose description is stored in a library of pat- terns, as well as their spatial relationships. We propose a structural approach based on subgraph isomorphism techniques to obtain a high-level interpretation of the document. The vectorized input document and the patterns to be recognized are represented by attributed graphs. Discrete relaxation techniques (AC4 algorithm) have been applied to develop the matching algorithm. The process has been divided in three steps: node labeling, local consistency and global consistency verification. The hand drawn creation causes disturbed line drawings with several accuracy errors, which must be taken into account. Here we have identified them and the AC4 algorithm has been adapted to manage them. |
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Cordoba |
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DAG;IAM; |
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IAM @ iam @ LIM1995 |
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1565 |
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Author |
Josep Llados; Horst Bunke; Enric Marti |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Finding rotational symmetries by cyclic string matching |
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Journal Article |
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1997 |
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Pattern recognition letters |
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18 |
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14 |
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1435-1442 |
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Rotational symmetry; Reflectional symmetry; String matching |
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Symmetry is an important shape feature. In this paper, a simple and fast method to detect perfect and distorted rotational symmetries of 2D objects is described. The boundary of a shape is polygonally approximated and represented as a string. Rotational symmetries are found by cyclic string matching between two identical copies of the shape string. The set of minimum cost edit sequences that transform the shape string to a cyclically shifted version of itself define the rotational symmetry and its order. Finally, a modification of the algorithm is proposed to detect reflectional symmetries. Some experimental results are presented to show the reliability of the proposed algorithm |
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Elsevier |
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DAG;IAM; |
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IAM @ iam @ LBM1997a |
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1562 |
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Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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ROC curves and video analysis optimization in intestinal capsule endoscopy |
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Journal Article |
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2006 |
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Pattern Recognition Letters |
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PRL |
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27 |
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8 |
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875–881 |
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ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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800 |
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MILAB;MV;SIAI |
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BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
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647 |
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Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas |
![download PDF file pdf](img/file_PDF.gif)
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Cutting Sayre's Knot: Reading Scene Text without Segmentation. Application to Utility Meters |
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Conference Article |
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2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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97-102 |
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Robust Reading; End-to-end Systems; CNN; Utility Meters |
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In this paper we present a segmentation-free system for reading text in natural scenes. A CNN architecture is trained in an end-to-end manner, and is able to directly output readings without any explicit text localization step. In order to validate our proposal, we focus on the specific case of reading utility meters. We present our results in a large dataset of images acquired by different users and devices, so text appears in any location, with different sizes, fonts and lengths, and the images present several distortions such as
dirt, illumination highlights or blur. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GRK2018 |
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3102 |
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Author |
Xinhang Song; Shuqiang Jiang; Luis Herranz |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Combining Models from Multiple Sources for RGB-D Scene Recognition |
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Conference Article |
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2017 |
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26th International Joint Conference on Artificial Intelligence |
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4523-4529 |
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Robotics and Vision; Vision and Perception |
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Depth can complement RGB with useful cues about object volumes and scene layout. However, RGB-D image datasets are still too small for directly training deep convolutional neural networks (CNNs), in contrast to the massive monomodal RGB datasets. Previous works in RGB-D recognition typically combine two separate networks for RGB and depth data, pretrained with a large RGB dataset and then fine tuned to the respective target RGB and depth datasets. These approaches have several limitations: 1) only use low-level filters learned from RGB data, thus not being able to exploit properly depth-specific patterns, and 2) RGB and depth features are only combined at high-levels but rarely at lower-levels. In this paper, we propose a framework that leverages both knowledge acquired from large RGB datasets together with depth-specific cues learned from the limited depth data, obtaining more effective multi-source and multi-modal representations. We propose a multi-modal combination method that selects discriminative combinations of layers from the different source models and target modalities, capturing both high-level properties of the task and intrinsic low-level properties of both modalities. |
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Melbourne; Australia; August 2017 |
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IJCAI |
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LAMP; 600.120 |
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Admin @ si @ SJH2017b |
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2966 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Shadow Resistant Road Segmentation from a Mobile Monocular System |
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2007 |
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3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:9–16 |
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road detection |
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Gerona (Spain) |
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ADAS;CIC |
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no |
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ADAS @ adas @ ALB2007 |
Serial |
943 |
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Author |
Jose Manuel Alvarez; Antonio Lopez; Ramon Baldrich |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Illuminant Invariant Model-Based Road Segmentation |
Type |
Conference Article |
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Year |
2008 |
Publication |
IEEE Intelligent Vehicles Symposium, |
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1155–1180 |
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road detection |
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Eindhoven (The Netherlands) |
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ADAS;CIC |
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no |
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Call Number |
ADAS @ adas @ ALB2008 |
Serial |
1045 |
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Author |
Jose Manuel Alvarez; Antonio Lopez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Novel Index for Objective Evaluation of Road Detection Algorithms |
Type |
Conference Article |
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Year |
2008 |
Publication |
Intelligent Transportation Systems. 11th International IEEE Conference on, |
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815–820 |
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road detection |
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Beijing (Xina) |
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Conference |
ITSC |
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Notes |
ADAS |
Approved |
no |
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Call Number |
ADAS @ adas @ AlL2008 |
Serial |
1074 |
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Permanent link to this record |