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Author Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez edit   pdf
url  openurl
  Title Biometric Recognition Based on Line Shape Descriptors Type Book Chapter
  Year 2006 Publication (up) Lecture Notes in Computer Science Abbreviated Journal  
  Volume 3926 Issue Pages 346–357,  
  Keywords  
  Abstract Abstract. In this paper we propose biometric descriptors inspired by shape signatures traditionally used in graphics recognition approaches. In particular several methods based on line shape descriptors used to iden- tify newborns from the biometric information of the ears are developed. The process steps are the following: image acquisition, ear segmentation, ear normalization, feature extraction and identification. Several shape signatures are defined from contour images. These are formulated in terms of zoning and contour crossings descriptors. Experimental results are presented to demonstrate the effectiveness of the used techniques.  
  Address  
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  Publisher Springer Link Place of Publication Editor  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ CSL2006 Serial 685  
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol edit   pdf
openurl 
  Title La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals Type Journal
  Year 2016 Publication (up) Lligall, Revista Catalana d'Arxivística Abbreviated Journal  
  Volume 39 Issue Pages 20-46  
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  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ FLR2016 Serial 2897  
Permanent link to this record
 
 
Author Josep Llados; Young-Bin Kwon edit  openurl
  Title Graphics Recognition. Recent Advances and Perspectives Type Miscellaneous
  Year 2004 Publication (up) LNCS 3080, ISBN: 3–540–22478–5 Abbreviated Journal  
  Volume Issue Pages  
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  Abstract  
  Address Springer-Verlag  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ LlK2004 Serial 515  
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Author Josep Llados; Enric Marti edit  openurl
  Title A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems Type Journal Article
  Year 1999 Publication (up) Machine Graphics & Vision Abbreviated Journal  
  Volume 8 Issue Pages 195-211  
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  Notes DAG;IAM; Approved no  
  Call Number IAM @ iam @ LIM1999 Serial 1568  
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Author Josep Llados; Enric Marti edit  openurl
  Title Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction Type Journal Article
  Year 1999 Publication (up) Machine Graphics & Vision journal, special issue on Graph transformation Abbreviated Journal  
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  Notes DAG;IAM Approved no  
  Call Number IAM @ iam @ LIM1999c Serial 1569  
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Author Josep Llados; Enric Marti edit  openurl
  Title A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems. Type Miscellaneous
  Year 1999 Publication (up) Machine Graphics & Vision, 8(2):195–211. Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ LlM1999a Serial 187  
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Author Josep Llados; Jaime Lopez-Krahe; Enric Marti edit   pdf
doi  openurl
  Title A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform Type Book Chapter
  Year 1997 Publication (up) Machine Vision and Applications Abbreviated Journal  
  Volume 10 Issue 3 Pages 150-158  
  Keywords Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition  
  Abstract Presently, man-machine interface development is a widespread research activity. A system to understand hand drawn architectural drawings in a CAD environment is presented in this paper. To understand a document, we have to identify its building elements and their structural properties. An attributed graph structure is chosen as a symbolic representation of the input document and the patterns to recognize in it. An inexact subgraph isomorphism procedure using relaxation labeling techniques is performed. In this paper we focus on how to speed up the matching. There is a building element, the walls, characterized by a hatching pattern. Using a straight line Hough transform (SLHT)-based method, we recognize this pattern, characterized by parallel straight lines, and remove from the input graph the edges belonging to this pattern. The isomorphism is then applied to the remainder of the input graph. When all the building elements have been recognized, the document is redrawn, correcting the inaccurate strokes obtained from a hand-drawn input.  
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  Notes DAG;IAM Approved no  
  Call Number IAM @ iam @ LLM1997a Serial 1566  
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Author L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink edit  url
openurl 
  Title A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting Type Journal
  Year 2014 Publication (up) Manuscript Cultures Abbreviated Journal  
  Volume 7 Issue Pages 47-58  
  Keywords  
  Abstract With the ongoing progress in digitization, huge document collections and archives have become available to a broad audience. Scanned document images can be transmitted electronically and studied simultaneously throughout the world. While this is very beneficial, it is often impossible to perform automated searches on these document collections. Optical character recognition usually fails when it comes to handwritten or historic documents. In order to address the need for exploring document collections rapidly, researchers are working on word spotting. In query-by-example word spotting scenarios, the user selects an exemplary occurrence of the query word in a document image. The word spotting system then retrieves all regions in the collection that are visually similar to the given example of the query word. The best matching regions are presented to the user and no actual transcription is required.
An important property of a word spotting system is the computational speed with which queries can be executed. In our previous work, we presented a relatively slow but high-precision method. In the present work, we will extend this baseline system to an integrated two-stage approach. In a coarse-grained first stage, we will filter document images efficiently in order to identify regions that are likely to contain the query word. In the fine-grained second stage, these regions will be analyzed with our previously presented high-precision method. Finally, we will report recognition results and query times for the well-known George Washington
benchmark in our evaluation. We achieve state-of-the-art recognition results while the query times can be reduced to 50% in comparison with our baseline.		  
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  Notes DAG; 600.061; 600.077 Approved no  
  Call Number Admin @ si @ Serial 3190  
Permanent link to this record
 
 
Author Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas edit   pdf
url  openurl
  Title Self-Supervised Learning from Web Data for Multimodal Retrieval Type Book Chapter
  Year 2019 Publication (up) Multi-Modal Scene Understanding Book Abbreviated Journal  
  Volume Issue Pages 279-306  
  Keywords self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding  
  Abstract Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings.  
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  Notes DAG; 600.129; 601.338; 601.310 Approved no  
  Call Number Admin @ si @ GGG2019 Serial 3266  
Permanent link to this record
 
 
Author Palaiahnakote Shivakumara; Anjan Dutta; Chew Lim Tan; Umapada Pal edit  doi
openurl 
  Title Multi-oriented scene text detection in video based on wavelet and angle projection boundary growing Type Journal Article
  Year 2014 Publication (up) Multimedia Tools and Applications Abbreviated Journal MTAP  
  Volume 72 Issue 1 Pages 515-539  
  Keywords  
  Abstract In this paper, we address two complex issues: 1) Text frame classification and 2) Multi-oriented text detection in video text frame. We first divide a video frame into 16 blocks and propose a combination of wavelet and median-moments with k-means clustering at the block level to identify probable text blocks. For each probable text block, the method applies the same combination of feature with k-means clustering over a sliding window running through the blocks to identify potential text candidates. We introduce a new idea of symmetry on text candidates in each block based on the observation that pixel distribution in text exhibits a symmetric pattern. The method integrates all blocks containing text candidates in the frame and then all text candidates are mapped on to a Sobel edge map of the original frame to obtain text representatives. To tackle the multi-orientation problem, we present a new method called Angle Projection Boundary Growing (APBG) which is an iterative algorithm and works based on a nearest neighbor concept. APBG is then applied on the text representatives to fix the bounding box for multi-oriented text lines in the video frame. Directional information is used to eliminate false positives. Experimental results on a variety of datasets such as non-horizontal, horizontal, publicly available data (Hua’s data) and ICDAR-03 competition data (camera images) show that the proposed method outperforms existing methods proposed for video and the state of the art methods for scene text as well.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1380-7501 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.077 Approved no  
  Call Number Admin @ si @ SDT2014 Serial 2357  
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