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Fernando Vilariño; Panagiota Spyridonos; Petia Radeva; Jordi Vitria; Fernando Azpiroz; Juan Malagelada |
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Title |
Method for automatic classification of in vivo images |
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Patent |
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2010 |
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US 2010/0046816 |
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A method for automatically detecting a post-duodenal boundary in an image stream of the gastrointestinal (GI) tract. The image stream is sampled to obtain a reduced set of images for processing. The reduced set of images is filtered to remove non-valid frames or non-valid portions of frames, thereby generating a filtered set of valid images. A polar representation of the valid images is generated. Textural features of the polar representation are processed to detect the post-duodenal boundary of the GI tract. |
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800 |
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MV;OR;MILAB;SIAI |
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IAM @ iam @ VSR2010 |
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1702 |
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Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications |
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Conference Article |
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2010 |
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European Conference on Machine Learning |
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119-128 |
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ECML |
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MILAB; OR;HUPBA;MV |
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Admin @ si @ BEB2010 |
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1775 |
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Oriol Ramos Terrades; Alejandro Hector Toselli; Nicolas Serrano; Veronica Romero; Enrique Vidal; Alfons Juan |
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Interactive layout analysis and transcription systems for historic handwritten documents |
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Conference Article |
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2010 |
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10th ACM Symposium on Document Engineering |
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219–222 |
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Handwriting recognition; Interactive predictive processing; Partial supervision; Interactive layout analysis |
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The amount of digitized legacy documents has been rising dramatically over the last years due mainly to the increasing number of on-line digital libraries publishing this kind of documents, waiting to be classified and finally transcribed into a textual electronic format (such as ASCII or PDF). Nevertheless, most of the available fully-automatic applications addressing this task are far from being perfect and heavy and inefficient human intervention is often required to check and correct the results of such systems. In contrast, multimodal interactive-predictive approaches may allow the users to participate in the process helping the system to improve the overall performance. With this in mind, two sets of recent advances are introduced in this work: a novel interactive method for text block detection and two multimodal interactive handwritten text transcription systems which use active learning and interactive-predictive technologies in the recognition process. |
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Manchester, United Kingdom |
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ACM |
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DAG |
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Admin @ si @RTS2010 |
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1857 |
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N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan |
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Title |
The GIDOC Prototype |
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Conference Article |
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2010 |
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10th International Workshop on Pattern Recognition in Information Systems |
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82-89 |
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Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions. |
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Funchal, Portugal |
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978-989-8425-14-0 |
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PRIS |
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Admin @ si @ STP2010 |
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1868 |
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Author |
Joan Arnedo-Moreno; Agata Lapedriza |
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Title |
Visualizing key authenticity: turning your face into your public key |
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Conference Article |
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2010 |
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6th China International Conference on Information Security and Cryptology |
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605-618 |
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Biometric information has become a technology complementary to cryptography, allowing to conveniently manage cryptographic data. Two important needs are ful lled: rst of all, making such data always readily available, and additionally, making its legitimate owner easily identi able. In this work we propose a signature system which integrates face recognition biometrics with and identity-based signature scheme, so the user's face e ectively becomes his public key and system ID. Thus, other users may verify messages using photos of the claimed sender, providing a reasonable trade-o between system security and usability, as well as a much more straightforward public key authenticity and distribution process. |
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Inscrypt |
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OR;MV |
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Admin @ si @ ArL2010c |
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2149 |
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