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Josep Llados, & Enric Marti. (1999). A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems..
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Josep Llados, J. Lopez-Krahe, & Enric Marti. (1999). A Hough-based method for hatched pattern detection in maps and diagrams..
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Jordi Gonzalez, Javier Varona, Xavier Roca, & Juan J. Villanueva. (2003). A Human Action Comparison Framework for Motion Understanding.
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Zhong Jin, Jing-Yu Yang, & Zhen Lou. (2005). A luminance-conditional distribution model of skin color information.
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Josep Llados, Felipe Lumbreras, & Javier Varona. (1999). A multidocument platform for automatic reading of identity cards..
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Marco Pedersoli. (2008). A Multiresolution Cascade for Human Detection.
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Xavier Roca, X. Binefa, & Jordi Vitria. (1998). A New Autofocus Algorithm for Cytological Tissue in a Microscopy Environment..
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Razieh Rastgoo, Kourosh Kiani, & Sergio Escalera. (2022). A Non-Anatomical Graph Structure for isolated hand gesture separation in continuous gesture sequences.
Abstract: Continuous Hand Gesture Recognition (CHGR) has been extensively studied by researchers in the last few decades. Recently, one model has been presented to deal with the challenge of the boundary detection of isolated gestures in a continuous gesture video [17]. To enhance the model performance and also replace the handcrafted feature extractor in the presented model in [17], we propose a GCN model and combine it with the stacked Bi-LSTM and Attention modules to push the temporal information in the video stream. Considering the breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN model to empower the 3D hand skeleton features. Finally, the class probabilities of each isolated gesture are fed to the post-processing module, borrowed from [17]. Furthermore, we replace the anatomical graph structure with some non-anatomical graph structures. Due to the lack of a large dataset, including both the continuous gesture sequences and the corresponding isolated gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR), RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show the superiority of the proposed model in dealing with isolated gesture boundaries detection in continuous gesture sequences
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J. Suri, S. Singh, S. Laxminarayan, R. Cesar, H. Jelinek, Petia Radeva, et al. (2003). A Note on Future Research in Vascular and Plaque Segmentation.
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Daniel Ponsa, & Xavier Roca. (2002). A Novel Approach to Generate Multiple Shape Models..
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Petia Radeva, J. Guerrero, & C. Molina. (1998). A Physics-Based Kohonen Ring..
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Stefan Lonn, Petia Radeva, & Mariella Dimiccoli. (2018). A picture is worth a thousand words but how to organize thousands of pictures?.
Abstract: We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 10 persons. Experimental results demonstrate better user satisfaction with respect to state of the art solutions in terms of organization.
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Gemma Sanchez, Ernest Valveny, Josep Llados, Joan Mas, & N. Lozano. (2004). A platform to extract knowledge from graphic documents. Application to an architectural sketch understanding scenario.
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Francisco Cruz, & Oriol Ramos Terrades. (2018). A probabilistic framework for handwritten text line segmentation.
Abstract: We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.
Keywords: Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning
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Mikhail Mozerov, V. Kober, & I.A. Ovseyevich. (2006). A Stereo Matching Algorithm with Global Smoothness Criterion.
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