|
|
Albert Gordo, Marçal Rusiñol, Dimosthenis Karatzas, & Andrew Bagdanov. (2013). Document Classification and Page Stream Segmentation for Digital Mailroom Applications. In 12th International Conference on Document Analysis and Recognition (pp. 621–625).
Abstract: In this paper we present a method for the segmentation of continuous page streams into multipage documents and the simultaneous classification of the resulting documents. We first present an approach to combine the multiple pages of a document into a single feature vector that represents the whole document. Despite its simplicity and low computational cost, the proposed representation yields results comparable to more complex methods in multipage document classification tasks. We then exploit this representation in the context of page stream segmentation. The most plausible segmentation of a page stream into a sequence of multipage documents is obtained by optimizing a statistical model that represents the probability of each segmented multipage document belonging to a particular class. Experimental results are reported on a large sample of real administrative multipage documents.
|
|
|
|
L. Rothacker, Marçal Rusiñol, & G.A. Fink. (2013). Bag-of-Features HMMs for segmentation-free word spotting in handwritten documents. In 12th International Conference on Document Analysis and Recognition (pp. 1305–1309).
Abstract: Recent HMM-based approaches to handwritten word spotting require large amounts of learning samples and mostly rely on a prior segmentation of the document. We propose to use Bag-of-Features HMMs in a patch-based segmentation-free framework that are estimated by a single sample. Bag-of-Features HMMs use statistics of local image feature representatives. Therefore they can be considered as a variant of discrete HMMs allowing to model the observation of a number of features at a point in time. The discrete nature enables us to estimate a query model with only a single example of the query provided by the user. This makes our method very flexible with respect to the availability of training data. Furthermore, we are able to outperform state-of-the-art results on the George Washington dataset.
|
|
|
|
Marçal Rusiñol, & Josep Llados. (2014). Boosting the Handwritten Word Spotting Experience by Including the User in the Loop. PR - Pattern Recognition, 47(3), 1063–1072.
Abstract: In this paper, we study the effect of taking the user into account in a query-by-example handwritten word spotting framework. Several off-the-shelf query fusion and relevance feedback strategies have been tested in the handwritten word spotting context. The increase in terms of precision when the user is included in the loop is assessed using two datasets of historical handwritten documents and two baseline word spotting approaches both based on the bag-of-visual-words model. We finally present two alternative ways of presenting the results to the user that might be more attractive and suitable to the user's needs than the classic ranked list.
Keywords: Handwritten word spotting; Query by example; Relevance feedback; Query fusion; Multidimensional scaling
|
|
|
|
Marçal Rusiñol, Lluis Pere de las Heras, & Oriol Ramos Terrades. (2014). Flowchart Recognition for Non-Textual Information Retrieval in Patent Search. IR - Information Retrieval, 17(5-6), 545–562.
Abstract: Relatively little research has been done on the topic of patent image retrieval and in general in most of the approaches the retrieval is performed in terms of a similarity measure between the query image and the images in the corpus. However, systems aimed at overcoming the semantic gap between the visual description of patent images and their conveyed concepts would be very helpful for patent professionals. In this paper we present a flowchart recognition method aimed at achieving a structured representation of flowchart images that can be further queried semantically. The proposed method was submitted to the CLEF-IP 2012 flowchart recognition task. We report the obtained results on this dataset.
Keywords: Flowchart recognition; Patent documents; Text/graphics separation; Raster-to-vector conversion; Symbol recognition
|
|
|
|
Ernest Valveny, Oriol Ramos Terrades, Joan Mas, & Marçal Rusiñol. (2013). Interactive Document Retrieval and Classification. In Angel Sappa, & Jordi Vitria (Eds.), Multimodal Interaction in Image and Video Applications (Vol. 48, pp. 17–30). Springer Berlin Heidelberg.
Abstract: In this chapter we describe a system for document retrieval and classification following the interactive-predictive framework. In particular, the system addresses two different scenarios of document analysis: document classification based on visual appearance and logo detection. These two classical problems of document analysis are formulated following the interactive-predictive model, taking the user interaction into account to make easier the process of annotating and labelling the documents. A system implementing this model in a real scenario is presented and analyzed. This system also takes advantage of active learning techniques to speed up the task of labelling the documents.
|
|
|
|
Jiaolong Xu, Sebastian Ramos, Xu Hu, David Vazquez, & Antonio Lopez. (2013). Multi-task Bilinear Classifiers for Visual Domain Adaptation. In Advances in Neural Information Processing Systems Workshop.
Abstract: We propose a method that aims to lessen the significant accuracy degradation
that a discriminative classifier can suffer when it is trained in a specific domain (source domain) and applied in a different one (target domain). The principal reason for this degradation is the discrepancies in the distribution of the features that feed the classifier in different domains. Therefore, we propose a domain adaptation method that maps the features from the different domains into a common subspace and learns a discriminative domain-invariant classifier within it. Our algorithm combines bilinear classifiers and multi-task learning for domain adaptation.
The bilinear classifier encodes the feature transformation and classification
parameters by a matrix decomposition. In this way, specific feature transformations for multiple domains and a shared classifier are jointly learned in a multi-task learning framework. Focusing on domain adaptation for visual object detection, we apply this method to the state-of-the-art deformable part-based model for cross domain pedestrian detection. Experimental results show that our method significantly avoids the domain drift and improves the accuracy when compared to several baselines.
Keywords: Domain Adaptation; Pedestrian Detection; ADAS
|
|
|
|
Hany Salah Eldeen. (2009). Colour Naming in Context through a Perceptual Model (Vol. 130). Master's thesis, , Bellaterra, Barcelona.
|
|
|
|
Naila Murray. (2009). Perceptual Feature Detection (Vol. 131). Master's thesis, , Bellaterra, Barcelona.
|
|
|
|
Josep M. Gonfaus. (2009). Semantic Segmentation of Images Using Random Ferns (Vol. 132). Master's thesis, , Bellaterra, Barcelona.
|
|
|
|
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
|
|
|
|
Alejandro Gonzalez Alzate. (2011). Evaluation of spatiotemporal descriptors for pedestrian detection in video sequences (Vol. 166). Master's thesis, , .
|
|
|
|
Yainuvis Socarras. (2011). Image segmentation for improving pedestrian detection (Vol. 167). Master's thesis, , .
|
|
|
|
Maria del Camp Davesa. (2011). Human action categorization in image sequences (Vol. 169). Master's thesis, , .
|
|
|
|
Monica Piñol. (2010). Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning (Vol. 162). Master's thesis, , .
|
|
|
|
Javier Marin, David Geronimo, David Vazquez, & Antonio Lopez. (2012). Pedestrian Detection: Exploring Virtual Worlds. In Handbook of Pattern Recognition: Methods and Application (Vol. 5, pp. 145–162). iConcept Press.
Abstract: Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition.
Keywords: Virtual worlds; Pedestrian Detection; Domain Adaptation
|
|
