|
|
David Guillamet, & Jordi Vitria. (2001). Discriminant Basis for Object Classification..
|
|
|
|
Jon Almazan, Alicia Fornes, & Ernest Valveny. (2011). A Non-Rigid Feature Extraction Method for Shape Recognition. In 11th International Conference on Document Analysis and Recognition (pp. 987–991).
Abstract: This paper presents a methodology for shape recognition that focuses on dealing with the difficult problem of large deformations. The proposed methodology consists in a novel feature extraction technique, which uses a non-rigid representation adaptable to the shape. This technique employs a deformable grid based on the computation of geometrical centroids that follows a region partitioning algorithm. Then, a feature vector is extracted by computing pixel density measures around these geometrical centroids. The result is a shape descriptor that adapts its representation to the given shape and encodes the pixel density distribution. The validity of the method when dealing with large deformations has been experimentally shown over datasets composed of handwritten shapes. It has been applied to signature verification and shape recognition tasks demonstrating high accuracy and low computational cost.
|
|
|
|
Marçal Rusiñol, David Aldavert, Ricardo Toledo, & Josep Llados. (2011). Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. In 11th International Conference on Document Analysis and Recognition (pp. 63–67).
Abstract: In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts.
|
|
|
|
Volkmar Frinken, Andreas Fischer, Horst Bunke, & Alicia Fornes. (2011). Co-training for Handwritten Word Recognition. In 11th International Conference on Document Analysis and Recognition (pp. 314–318).
Abstract: To cope with the tremendous variations of writing styles encountered between different individuals, unconstrained automatic handwriting recognition systems need to be trained on large sets of labeled data. Traditionally, the training data has to be labeled manually, which is a laborious and costly process. Semi-supervised learning techniques offer methods to utilize unlabeled data, which can be obtained cheaply in large amounts in order, to reduce the need for labeled data. In this paper, we propose the use of Co-Training for improving the recognition accuracy of two weakly trained handwriting recognition systems. The first one is based on Recurrent Neural Networks while the second one is based on Hidden Markov Models. On the IAM off-line handwriting database we demonstrate a significant increase of the recognition accuracy can be achieved with Co-Training for single word recognition.
|
|
|
|
Muhammad Muzzamil Luqman, Jean-Yves Ramel, Josep Llados, & Thierry Brouard. (2011). Subgraph Spotting Through Explicit Graph Embedding: An Application to Content Spotting in Graphic Document Images. In 11th International Conference on Document Analysis and Recognition (pp. 870–874).
Abstract: We present a method for spotting a subgraph in a graph repository. Subgraph spotting is a very interesting research problem for various application domains where the use of a relational data structure is mandatory. Our proposed method accomplishes subgraph spotting through graph embedding. We achieve automatic indexation of a graph repository during off-line learning phase, where we (i) break the graphs into 2-node sub graphs (a.k.a. cliques of order 2), which are primitive building-blocks of a graph, (ii) embed the 2-node sub graphs into feature vectors by employing our recently proposed explicit graph embedding technique, (iii) cluster the feature vectors in classes by employing a classic agglomerative clustering technique, (iv) build an index for the graph repository and (v) learn a Bayesian network classifier. The subgraph spotting is achieved during the on-line querying phase, where we (i) break the query graph into 2-node sub graphs, (ii) embed them into feature vectors, (iii) employ the Bayesian network classifier for classifying the query 2-node sub graphs and (iv) retrieve the respective graphs by looking-up in the index of the graph repository. The graphs containing all query 2-node sub graphs form the set of result graphs for the query. Finally, we employ the adjacency matrix of each result graph along with a score function, for spotting the query graph in it. The proposed subgraph spotting method is equally applicable to a wide range of domains, offering ease of query by example (QBE) and granularity of focused retrieval. Experimental results are presented for graphs generated from two repositories of electronic and architectural document images.
|
|
|
|
Anjan Dutta, Josep Llados, & Umapada Pal. (2011). Symbol Spotting in Line Drawings Through Graph Paths Hashing. In 11th International Conference on Document Analysis and Recognition (pp. 982–986).
Abstract: In this paper we propose a symbol spotting technique through hashing the shape descriptors of graph paths (Hamiltonian paths). Complex graphical structures in line drawings can be efficiently represented by graphs, which ease the accurate localization of the model symbol. Graph paths are the factorized substructures of graphs which enable robust recognition even in the presence of noise and distortion. In our framework, the entire database of the graphical documents is indexed in hash tables by the locality sensitive hashing (LSH) of shape descriptors of the paths. The hashing data structure aims to execute an approximate k-NN search in a sub-linear time. The spotting method is formulated by a spatial voting scheme to the list of locations of the paths that are decided during the hash table lookup process. We perform detailed experiments with various dataset of line drawings and the results demonstrate the effectiveness and efficiency of the technique.
|
|
|
|
Lluis Pere de las Heras, Joan Mas, Gemma Sanchez, & Ernest Valveny. (2011). Wall Patch-Based Segmentation in Architectural Floorplans. In 11th International Conference on Document Analysis and Recognition (pp. 1270–1274).
Abstract: Segmentation of architectural floor plans is a challenging task, mainly because of the large variability in the notation between different plans. In general, traditional techniques, usually based on analyzing and grouping structural primitives obtained by vectorization, are only able to handle a reduced range of similar notations. In this paper we propose an alternative patch-based segmentation approach working at pixel level, without need of vectorization. The image is divided into a set of patches and a set of features is extracted for every patch. Then, each patch is assigned to a visual word of a previously learned vocabulary and given a probability of belonging to each class of objects. Finally, a post-process assigns the final label for every pixel. This approach has been applied to the detection of walls on two datasets of architectural floor plans with different notations, achieving high accuracy rates.
|
|
|
|
Dimosthenis Karatzas, Sergi Robles, Joan Mas, Farshad Nourbakhsh, & Partha Pratim Roy. (2011). ICDAR 2011 Robust Reading Competition – Challege 1: Reading Text in Born-Digital Images (Web and Email). In 11th International Conference on Document Analysis and Recognition (pp. 1485–1490).
Abstract: This paper presents the results of the first Challenge of ICDAR 2011 Robust Reading Competition. Challenge 1 is focused on the extraction of text from born-digital images, specifically from images found in Web pages and emails. The challenge was organized in terms of three tasks that look at different stages of the process: text localization, text segmentation and word recognition. In this paper we present the results of the challenge for all three tasks, and make an open call for continuous participation outside the context of ICDAR 2011.
|
|
|
|
Alicia Fornes, Anjan Dutta, Albert Gordo, & Josep Llados. (2011). The ICDAR 2011 Music Scores Competition: Staff Removal and Writer Identification. In 11th International Conference on Document Analysis and Recognition (pp. 1511–1515).
Abstract: In the last years, there has been a growing interest in the analysis of handwritten music scores. In this sense, our goal has been to foster the interest in the analysis of handwritten music scores by the proposal of two different competitions: Staff removal and Writer Identification. Both competitions have been tested on the CVC-MUSCIMA database: a ground-truth of handwritten music score images. This paper describes the competition details, including the dataset and ground-truth, the evaluation metrics, and a short description of the participants, their methods, and the obtained results.
|
|
|
|
David Geronimo, Frederic Lerasle, & Antonio Lopez. (2012). State-driven particle filter for multi-person tracking. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 467–478). Heidelberg: Springer.
Abstract: Multi-person tracking can be exploited in applications such as driver assistance, surveillance, multimedia and human-robot interaction. With the help of human detectors, particle filters offer a robust method able to filter noisy detections and provide temporal coherence. However, some traditional problems such as occlusions with other targets or the scene, temporal drifting or even the lost targets detection are rarely considered, making the systems performance decrease. Some authors propose to overcome these problems using heuristics not explained
and formalized in the papers, for instance by defining exceptions to the model updating depending on tracks overlapping. In this paper we propose to formalize these events by the use of a state-graph, defining the current state of the track (e.g., potential , tracked, occluded or lost) and the transitions between states in an explicit way. This approach has the advantage of linking track actions such as the online underlying models updating, which gives flexibility to the system. It provides an explicit representation to adapt the multiple parallel trackers depending on the context, i.e., each track can make use of a specific filtering strategy, dynamic model, number of particles, etc. depending on its state. We implement this technique in a single-camera multi-person tracker and test
it in public video sequences.
Keywords: human tracking
|
|
|
|
Yainuvis Socarras, David Vazquez, Antonio Lopez, David Geronimo, & Theo Gevers. (2012). Improving HOG with Image Segmentation: Application to Human Detection. In J. Blanc-Talon et al. (Ed.), 11th International Conference on Advanced Concepts for Intelligent Vision Systems (Vol. 7517, pp. 178–189). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Keywords: Segmentation; Pedestrian Detection
|
|
|
|
E. Bondi, L. Sidenari, Andrew Bagdanov, & Alberto del Bimbo. (2014). Real-time people counting from depth imagery of crowded environments. In 11th IEEE International Conference on Advanced Video and Signal based Surveillance (pp. 337–342).
Abstract: In this paper we describe a system for automatic people counting in crowded environments. The approach we propose is a counting-by-detection method based on depth imagery. It is designed to be deployed as an autonomous appliance for crowd analysis in video surveillance application scenarios. Our system performs foreground/background segmentation on depth image streams in order to coarsely segment persons, then depth information is used to localize head candidates which are then tracked in time on an automatically estimated ground plane. The system runs in real-time, at a frame-rate of about 20 fps. We collected a dataset of RGB-D sequences representing three typical and challenging surveillance scenarios, including crowds, queuing and groups. An extensive comparative evaluation is given between our system and more complex, Latent SVM-based head localization for person counting applications.
|
|
|
|
Sergio Escalera, Oriol Pujol, & Petia Radeva. (2006). Decoding of Ternary Error Correcting Output Codes. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 753–763.
|
|
|
|
Michael Villamizar, A. Sanfeliu, & Juan Andrade. (2006). Orientation Invariant Features for Multiclass Object Recognition.
|
|
|
|
Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, Neus Salvatella, & Petia Radeva. (2006). In-Vivo IVUS Tissue Classification: A Comparison Between RF Signal Analysis and Reconstructed Images. In 11th Iberoamerican Congress on Pattern Recognition (CIARP´06), LNCS 4225: 137–146.
|
|
