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Francesc Tous; Agnes Borras; Robert Benavente; Ramon Baldrich; Maria Vanrell; Josep Llados |
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Title |
Textual Descriptions for Browsing People by Visual Apperance. |
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2002 |
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Lecture Notes in Artificial Intelligence |
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2504 |
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419-429 |
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This paper presents a first approach to build colour and structural descriptors for information retrieval on a people database. Queries are formulated in terms of their appearance that allows to seek people wearing specific clothes of a given colour name or texture. Descriptors are automatically computed by following three essential steps. A colour naming labelling from pixel properties. A region seg- mentation step based on colour properties of pixels combined with edge information. And a high level step that models the region arrangements in order to build clothes structure. Results are tested on large set of images from real scenes taken at the entrance desk of a building |
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Springer Verlag |
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DAG;CIC |
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CAT @ cat @ TBB2002b |
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319 |
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Anton Cervantes; Gemma Sanchez; Josep Llados; Agnes Borras; Ana Rodriguez |
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Title |
Biometric Recognition Based on Line Shape Descriptors |
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2006 |
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Lecture Notes in Computer Science |
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3926 |
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346–357, |
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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. |
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DAG @ dag @ CSL2006 |
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685 |
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Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
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Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
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2016 |
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Lligall, Revista Catalana d'Arxivística |
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39 |
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20-46 |
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DAG; 600.097 |
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Admin @ si @ FLR2016 |
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2897 |
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Josep Llados; Young-Bin Kwon |
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Graphics Recognition. Recent Advances and Perspectives |
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2004 |
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LNCS 3080, ISBN: 3–540–22478–5 |
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Springer-Verlag |
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DAG @ dag @ LlK2004 |
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515 |
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Josep Llados; Enric Marti |
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A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems |
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1999 |
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Machine Graphics & Vision |
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8 |
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195-211 |
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DAG;IAM; |
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IAM @ iam @ LIM1999 |
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1568 |
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Author |
Josep Llados; Enric Marti |
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Title |
Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction |
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1999 |
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Machine Graphics & Vision journal, special issue on Graph transformation |
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DAG;IAM |
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IAM @ iam @ LIM1999c |
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1569 |
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Josep Llados; Enric Marti |
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A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems. |
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1999 |
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Machine Graphics & Vision, 8(2):195–211. |
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DAG @ dag @ LlM1999a |
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187 |
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Josep Llados; Jaime Lopez-Krahe; Enric Marti |
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A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform |
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1997 |
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Machine Vision and Applications |
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10 |
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3 |
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150-158 |
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Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition |
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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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IAM @ iam @ LLM1997a |
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1566 |
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Author |
L. Rothacker; Marçal Rusiñol; Josep Llados; G.A. Fink |
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Title |
A Two-stage Approach to Segmentation-Free Query-by-example Word Spotting |
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2014 |
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Manuscript Cultures |
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7 |
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47-58 |
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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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DAG; 600.061; 600.077 |
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Admin @ si @ |
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3190 |
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Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Self-Supervised Learning from Web Data for Multimodal Retrieval |
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2019 |
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Multi-Modal Scene Understanding Book |
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279-306 |
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self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding |
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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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DAG; 600.129; 601.338; 601.310 |
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Admin @ si @ GGG2019 |
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3266 |
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