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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados; Mathieu Nicolas Delalandre |
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Title |
Multi-Oriented and Multi-Sized Touching Character Segmentation using Dynamic Programming |
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Conference Article |
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2009 |
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10th International Conference on Document Analysis and Recognition |
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11–15 |
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In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region at the background portion. Using Convex Hull information, we use these background information to find some initial points to segment a touching string into possible primitive segments (a primitive segment consists of a single character or a part of a character). Next these primitive segments are merged to get optimum segmentation and dynamic programming is applied using total likelihood of characters as the objective function. SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Circular ring and convex hull ring based approach has been used along with angular information of the contour pixels of the character to make the feature rotation invariant. From the experiment, we obtained encouraging results. |
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Barcelona, Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG @ dag @ RPL2009a |
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1240 |
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Author |
Anjan Dutta; Zeynep Akata |
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Title |
Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval |
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Conference Article |
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2019 |
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32nd IEEE Conference on Computer Vision and Pattern Recognition |
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5089-5098 |
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Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer vision, allowing to retrieve natural images relevant to sketch queries that might not been seen in the training phase. Existing works either require aligned sketch-image pairs or inefficient memory fusion layer for mapping the visual information to a semantic space. In this work, we propose a semantically aligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR, where each branch maps the visual information to a common semantic space via an adversarial training. Each of these branches maintains a cycle consistency that only requires supervision at category levels, and avoids the need of highly-priced aligned sketch-image pairs. A classification criteria on the generators' outputs ensures the visual to semantic space mapping to be discriminating. Furthermore, we propose to combine textual and hierarchical side information via a feature selection auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in zero-shot SBIR performance over the state-of-the-art on the challenging Sketchy and TU-Berlin datasets. |
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Long beach; California; USA; June 2019 |
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CVPR |
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DAG; 600.141; 600.121 |
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Admin @ si @ DuA2019 |
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3268 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Seal Object Detection in Document Images using GHT of Local Component Shapes |
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Conference Article |
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Year |
2010 |
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10th ACM Symposium On Applied Computing |
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23–27 |
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Due to noise, overlapped text/signature and multi-oriented nature, seal (stamp) object detection involves a difficult challenge. This paper deals with automatic detection of seal from documents with cluttered background. Here, a seal object is characterized by scale and rotation invariant spatial feature descriptors (distance and angular position) computed from recognition result of individual connected components (characters). Recognition of multi-scale and multi-oriented component is done using Support Vector Machine classifier. Generalized Hough Transform (GHT) is used to detect the seal and a voting is casted for finding possible location of the seal object in a document based on these spatial feature descriptor of components pairs. The peak of votes in GHT accumulator validates the hypothesis to locate the seal object in a document. Experimental results show that, the method is efficient to locate seal instance of arbitrary shape and orientation in documents. |
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Sierre, Switzerland |
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SAC |
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DAG @ dag @ RPL2010a |
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1291 |
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Author |
Muhammad Muzzamil Luqman; Thierry Brouard; Jean-Yves Ramel; Josep Llados |
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Title |
Vers une approche foue of encapsulation de graphes: application a la reconnaissance de symboles |
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Conference Article |
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2010 |
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Colloque International Francophone sur l'Écrit et le Document |
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169-184 |
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Fuzzy interval; Graph embedding; Bayesian network; Symbol recognition |
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We present a new methodology for symbol recognition, by employing a structural approach for representing visual associations in symbols and a statistical classifier for recognition. A graphic symbol is vectorized, its topological and geometrical details are encoded by an attributed relational graph and a signature is computed for it. Data adapted fuzzy intervals have been introduced for addressing the sensitivity of structural representations to noise. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set, and is deployed in a supervised learning scenario for recognizing query symbols. Experimental results on pre-segmented 2D linear architectural and electronic symbols from GREC databases are presented. |
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Sousse, Tunisia |
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CIFED |
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DAG @ dag @ LBR2010a |
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1293 |
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Author |
Albert Gordo; Alicia Fornes; Ernest Valveny; Josep Llados |
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Title |
A Bag of Notes Approach to Writer Identification in Old Handwritten Music Scores |
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Conference Article |
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Year |
2010 |
Publication |
9th IAPR International Workshop on Document Analysis Systems |
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247–254 |
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Determining the authorship of a document, namely writer identification, can be an important source of information for document categorization. Contrary to text documents, the identification of the writer of graphical documents is still a challenge. In this paper we present a robust approach for writer identification in a particular kind of graphical documents, old music scores. This approach adapts the bag of visual terms method for coping with graphic documents. The identification is performed only using the graphical music notation. For this purpose, we generate a graphic vocabulary without recognizing any music symbols, and consequently, avoiding the difficulties in the recognition of hand-drawn symbols in old and degraded documents. The proposed method has been tested on a database of old music scores from the 17th to 19th centuries, achieving very high identification rates. |
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Boston; USA; |
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978-1-60558-773-8 |
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DAG @ dag @ GFV2010 |
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1320 |
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Author |
Alicia Fornes; Josep Llados |
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Title |
A Symbol-dependent Writer Identifcation Approach in Old Handwritten Music Scores |
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Conference Article |
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2010 |
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12th International Conference on Frontiers in Handwriting Recognition |
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634 - 639 |
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Writer identification consists in determining the writer of a piece of handwriting from a set of writers. In this paper we introduce a symbol-dependent approach for identifying the writer of old music scores, which is based on two symbol recognition methods. The main idea is to use the Blurred Shape Model descriptor and a DTW-based method for detecting, recognizing and describing the music clefs and notes. The proposed approach has been evaluated in a database of old music scores, achieving very high writer identification rates. |
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Kolkata (India) |
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978-1-4244-8353-2 |
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ICFHR |
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DAG |
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DAG @ dag @ FoL2010 |
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1321 |
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Author |
Jaume Gibert; Ernest Valveny |
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Title |
Graph Embedding based on Nodes Attributes Representatives and a Graph of Words Representation. |
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Conference Article |
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2010 |
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13th International worshop on structural and syntactic pattern recognition and 8th international worshop on statistical pattern recognition |
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6218 |
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223–232 |
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Although graph embedding has recently been used to extend statistical pattern recognition techniques to the graph domain, some existing embeddings are usually computationally expensive as they rely on classical graph-based operations. In this paper we present a new way to embed graphs into vector spaces by first encapsulating the information stored in the original graph under another graph representation by clustering the attributes of the graphs to be processed. This new representation makes the association of graphs to vectors an easy step by just arranging both node attributes and the adjacency matrix in the form of vectors. To test our method, we use two different databases of graphs whose nodes attributes are of different nature. A comparison with a reference method permits to show that this new embedding is better in terms of classification rates, while being much more faster. |
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Springer Berlin Heidelberg |
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In E.R. Hancock, R.C. Wilson, T. Windeatt, I. Ulusoy and F. Escolano, |
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0302-9743 |
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978-3-642-14979-5 |
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S+SSPR |
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DAG |
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DAG @ dag @ GiV2010 |
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1416 |
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Author |
Anjan Dutta; Umapada Pal; Alicia Fornes; Josep Llados |
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Title |
An Efficient Staff Removal Technique from Printed Musical Documents |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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1965–1968 |
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Staff removal is an important preprocessing step of the Optical Music Recognition (OMR). The process aims to remove the stafflines from a musical document and retain only the musical symbols, later these symbols are used effectively to identify the music information. This paper proposes a simple but robust method to remove stafflines from printed musical scores. In the proposed methodology we have considered a staffline segment as a horizontal linkage of vertical black runs with uniform height. We have used the neighbouring properties of a staffline segment to validate it as a true segment. We have considered the dataset along with the deformations described in for evaluation purpose. From experimentation we have got encouraging results. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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DAG |
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DAG @ dag @ DPF2010 |
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1420 |
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Author |
Alicia Fornes; Sergio Escalera; Josep Llados; Ernest Valveny |
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Title |
Symbol Classification using Dynamic Aligned Shape Descriptor |
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Conference Article |
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2010 |
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20th International Conference on Pattern Recognition |
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1957–1960 |
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Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we propose a new descriptor and distance computation for coping with the problem of symbol recognition in the domain of Graphical Document Image Analysis. The proposed D-Shape descriptor encodes the arrangement information of object parts in a circular structure, allowing different levels of distortion. The classification is performed using a cyclic Dynamic Time Warping based method, allowing distortions and rotation. The methodology has been validated on different data sets, showing very high recognition rates. |
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Istanbul (Turkey) |
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1051-4651 |
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978-1-4244-7542-1 |
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ICPR |
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DAG; HUPBA; MILAB |
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BCNPCL @ bcnpcl @ FEL2010 |
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1421 |
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Author |
Albert Gordo; Jaume Gibert; Ernest Valveny; Marçal Rusiñol |
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Title |
A Kernel-based Approach to Document Retrieval |
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Conference Article |
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2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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377–384 |
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In this paper we tackle the problem of document image retrieval by combining a similarity measure between documents and the probability that a given document belongs to a certain class. The membership probability to a specific class is computed using Support Vector Machines in conjunction with similarity measure based kernel applied to structural document representations. In the presented experiments, we use different document representations, both visual and structural, and we apply them to a database of historical documents. We show how our method based on similarity kernels outperforms the usual distance-based retrieval. |
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Boston; USA; |
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978-1-60558-773-8 |
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DAG @ dag @ GGV2010 |
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1431 |
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