Josep M. Gonfaus. (2009). Semantic Segmentation of Images Using Random Ferns (Vol. 132). Master's thesis, , Bellaterra, Barcelona.
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Josep M. Gonfaus. (2012). Towards Deep Image Understanding: From pixels to semantics (Jordi Gonzalez, & Theo Gevers, Eds.). Ph.D. thesis, Ediciones Graficas Rey, .
Abstract: Understanding the content of the images is one of the greatest challenges of computer vision. Recognition of objects appearing in images, identifying and interpreting their actions are the main purposes of Image Understanding. This thesis seeks to identify what is present in a picture by categorizing and locating all the objects in the scene.
Images are composed by pixels, and one possibility consists of assigning to each pixel an object category, which is commonly known as semantic segmentation. By incorporating information as a contextual cue, we are able to resolve the ambiguity within categories at the pixel-level. We propose three levels of scale in order to resolve such ambiguity.
Another possibility to represent the objects is the object detection task. In this case, the aim is to recognize and localize the whole object by accurately placing a bounding box around it. We present two new approaches. The first one is focused on improving the object representation of deformable part models with the concept of factorized appearances. The second approach addresses the issue of reducing the computational cost for multi-class recognition. The results given have been validated on several commonly used datasets, reaching international recognition and state-of-the-art within the field
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Josep Llados, Horst Bunke, & Enric Marti. (1997). Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes. In Intelligent Robots: Sensing, Modeling and Planning (pp. 164–179). World Scientific Press.
Abstract: Dagstuhl Workshop
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Josep Llados, & Young-Bin Kwon. (2004). Graphics Recognition. Recent Advances and Perspectives.
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Josep Llados, W. Liu, & Jean-Marc Ogier. (2007). Seventh IAPR International Workshop on Graphics Recognition GREC 2007.
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Josep Llados, Partha Pratim Roy, Jose Antonio Rodriguez, & Gemma Sanchez. (2007). Word Spotting in Archive Documents using Shape Contexts. In 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4478:290–297.
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Josep Llados, Marçal Rusiñol, Alicia Fornes, David Fernandez, & Anjan Dutta. (2012). On the Influence of Word Representations for Handwritten Word Spotting in Historical Documents. IJPRAI - International Journal of Pattern Recognition and Artificial Intelligence, 26(5), 1263002–126027.
Abstract: 0,624 JCR
Word spotting is the process of retrieving all instances of a queried keyword from a digital library of document images. In this paper we evaluate the performance of different word descriptors to assess the advantages and disadvantages of statistical and structural models in a framework of query-by-example word spotting in historical documents. We compare four word representation models, namely sequence alignment using DTW as a baseline reference, a bag of visual words approach as statistical model, a pseudo-structural model based on a Loci features representation, and a structural approach where words are represented by graphs. The four approaches have been tested with two collections of historical data: the George Washington database and the marriage records from the Barcelona Cathedral. We experimentally demonstrate that statistical representations generally give a better performance, however it cannot be neglected that large descriptors are difficult to be implemented in a retrieval scenario where word spotting requires the indexation of data with million word images.
Keywords: Handwriting recognition; word spotting; historical documents; feature representation; shape descriptors Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218001412630025
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Josep Llados, & Marçal Rusiñol. (2014). Graphics Recognition Techniques. In D. Doermann, & K. Tombre (Eds.), Handbook of Document Image Processing and Recognition (Vol. D, pp. 489–521). Springer London.
Abstract: This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process.
Keywords: Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation
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Josep Llados, Jaime Lopez-Krahe, Gemma Sanchez, & Enric Marti. (2000). Interprétation de cartes et plans par mise en correspondance de graphes de attributs. In 12 Congrès Francophone AFRIF–AFIA (Vol. 3, pp. 225–234).
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Josep Llados, Jaime Lopez-Krahe, & Enric Marti. (1997). A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform. In Machine Vision and Applications (Vol. 10, pp. 150–158).
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.
Keywords: Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition
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Josep Llados, Jaime Lopez-Krahe, & Enric Marti. (1996). Hand drawn document understanding using the straight line Hough transform and graph matching. In Proceedings of the 13th International Pattern Recognition Conference (ICPR’96) (Vol. 2, pp. 497–501). Vienna , Austria.
Abstract: This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized
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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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Josep Llados, J. Lopez-Krahe, & D. Archambault. (2007). Special Issue on Information Technologies for Visually Impaired People. Novatica, 4–7.
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Josep Llados, Horst Bunke, & Enric Marti. (1997). Finding rotational symmetries by cyclic string matching. PRL - Pattern recognition letters, 18(14), 1435–1442.
Abstract: 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
Keywords: Rotational symmetry; Reflectional symmetry; String matching
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Josep Llados, Horst Bunke, & Enric Marti. (1996). Using cyclic string matching to find rotational and reflectional symmetric shapes. In H. B. H. N. R.C. Bolles (Ed.), Dagstuhl Seminar on Modelling and Planning for Sensor–based Intelligent Robot Systems. Saarbrucken (Germany).: World Scientific.
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