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| Author | Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Ruben Ballester; Claudio Battiloro; Guillermo Bernardez; Tolga Birdal; Aiden Brent; Peter Chin; Sergio Escalera; Simone Fiorellino; Odin Hoff Gardaa; Gurusankar Gopalakrishnan; Devendra Govil; Josef Hoppe; Maneel Reddy Karri; Jude Khouja; Manuel Lecha; Neal Livesay; Jan Meibner; Soham Mukherjee; Alexander Nikitin; Theodore Papamarkou; Jaro Prilepok; Karthikeyan Natesan Ramamurthy; Paul Rosen; Aldo Guzman-Saenz; Alessandro Salatiello; Shreyas N. Samaga; Simone Scardapane; Michael T. Schaub; Luca Scofano; Indro Spinelli; Lev Telyatnikov; Quang Truong; Robin Walters; Maosheng Yang; Olga Zaghen; Ghada Zamzmi; Ali Zia; Nina Miolane | ||||
| Title | TopoX: A Suite of Python Packages for Machine Learning on Topological Domains | Type | Miscellaneous | ||
| Year | 2024 | Publication | Arxiv | Abbreviated Journal | |
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Abstract  |
We introduce TopoX, a Python software suite that provides reliable and user-friendly building blocks for computing and machine learning on topological domains that extend graphs: hypergraphs, simplicial, cellular, path and combinatorial complexes. TopoX consists of three packages: TopoNetX facilitates constructing and computing on these domains, including working with nodes, edges and higher-order cells; TopoEmbedX provides methods to embed topological domains into vector spaces, akin to popular graph-based embedding algorithms such as node2vec; TopoModelx is built on top of PyTorch and offers a comprehensive toolbox of higher-order message passing functions for neural networks on topological domains. The extensively documented and unit-tested source code of TopoX is available under MIT license at this https URL. | ||||
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| Notes | HUPBA | Approved | no | ||
| Call Number | Admin @ si @ HPF2024 | Serial | 4021 | ||
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| Author | Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol | ||||
| Title | STEP – Towards Structured Scene-Text Spotting | Type | Conference Article | ||
| Year | 2024 | Publication | Winter Conference on Applications of Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 883-892 | ||
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| Abstract |
We introduce the structured scene-text spotting task, which requires a scene-text OCR system to spot text in the wild according to a query regular expression. Contrary to generic scene text OCR, structured scene-text spotting seeks to dynamically condition both scene text detection and recognition on user-provided regular expressions. To tackle this task, we propose the Structured TExt sPotter (STEP), a model that exploits the provided text structure to guide the OCR process. STEP is able to deal with regular expressions that contain spaces and it is not bound to detection at the word-level granularity. Our approach enables accurate zero-shot structured text spotting in a wide variety of real-world reading scenarios and is solely trained on publicly available data. To demonstrate the effectiveness of our approach, we introduce a new challenging test dataset that contains several types of out-of-vocabulary structured text, reflecting important reading applications of fields such as prices, dates, serial numbers, license plates etc. We demonstrate that STEP can provide specialised OCR performance on demand in all tested scenarios. | ||||
| Address | Waikoloa; Hawai; USA; January 2024 | ||||
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| Area | Expedition | Conference | WACV | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ GKR2024 | Serial | 3992 | ||
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| Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
| Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages | 125-133 | ||
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| Abstract |
We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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| Address | Virtual; January 2021 | ||||
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| Area | Expedition | Conference | WACVW | ||
| Notes | ADAS; 600.118; 600.124 | Approved | no | ||
| Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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| Author | Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Debora Gil; Cristina Rodriguez de Miguel; Fernando Vilariño | ||||
| Title | WM-DOVA Maps for Accurate Polyp Highlighting in Colonoscopy: Validation vs. Saliency Maps from Physicians | Type | Journal Article | ||
| Year | 2015 | Publication | Computerized Medical Imaging and Graphics | Abbreviated Journal | CMIG |
| Volume | 43 | Issue | Pages | 99-111 | |
| Keywords | Polyp localization; Energy Maps; Colonoscopy; Saliency; Valley detection | ||||
| Abstract |
We introduce in this paper a novel polyp localization method for colonoscopy videos. Our method is based on a model of appearance for polyps which defines polyp boundaries in terms of valley information. We propose the integration of valley information in a robust way fostering complete, concave and continuous boundaries typically associated to polyps. This integration is done by using a window of radial sectors which accumulate valley information to create WMDOVA1 energy maps related with the likelihood of polyp presence. We perform a double validation of our maps, which include the introduction of two new databases, including the first, up to our knowledge, fully annotated database with clinical metadata associated. First we assess that the highest value corresponds with the location of the polyp in the image. Second, we show that WM-DOVA energy maps can be comparable with saliency maps obtained from physicians' fixations obtained via an eye-tracker. Finally, we prove that our method outperforms state-of-the-art computational saliency results. Our method shows good performance, particularly for small polyps which are reported to be the main sources of polyp miss-rate, which indicates the potential applicability of our method in clinical practice. | ||||
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| ISSN | 0895-6111 | ISBN | Medium | ||
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| Notes | MV; IAM; 600.047; 600.060; 600.075;SIAI | Approved | no | ||
| Call Number | Admin @ si @ BSF2015 | Serial | 2609 | ||
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| Author | Alexey Dosovitskiy; German Ros; Felipe Codevilla; Antonio Lopez; Vladlen Koltun | ||||
| Title | CARLA: An Open Urban Driving Simulator | Type | Conference Article | ||
| Year | 2017 | Publication | 1st Annual Conference on Robot Learning. Proceedings of Machine Learning | Abbreviated Journal | |
| Volume | 78 | Issue | Pages | 1-16 | |
| Keywords | Autonomous driving; sensorimotor control; simulation | ||||
| Abstract |
We introduce CARLA, an open-source simulator for autonomous driving research. CARLA has been developed from the ground up to support development, training, and validation of autonomous urban driving systems. In addition to open-source code and protocols, CARLA provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions. We use CARLA to study the performance of three approaches to autonomous driving: a classic modular pipeline, an endto-end
model trained via imitation learning, and an end-to-end model trained via reinforcement learning. The approaches are evaluated in controlled scenarios of increasing difficulty, and their performance is examined via metrics provided by CARLA, illustrating the platform’s utility for autonomous driving research. |
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| Address | Mountain View; CA; USA; November 2017 | ||||
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| Area | Expedition | Conference | CORL | ||
| Notes | ADAS; 600.085; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DRC2017 | Serial | 2988 | ||
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| Author | Axel Barroso-Laguna; Edgar Riba; Daniel Ponsa; Krystian Mikolajczyk | ||||
| Title | Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters | Type | Conference Article | ||
| Year | 2019 | Publication | 18th IEEE International Conference on Computer Vision | Abbreviated Journal | |
| Volume | Issue | Pages | 5835-5843 | ||
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| Abstract |
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture. Handcrafted filters provide anchor structures for learned filters, which localize, score and rank repeatable features. Scale-space representation is used within the network to extract keypoints at different levels. We design a loss function to detect robust features that exist across a range of scales and to maximize the repeatability score. Our Key.Net model is trained on data synthetically created from ImageNet and evaluated on HPatches benchmark. Results show that our approach outperforms state-of-the-art detectors in terms of repeatability, matching performance and complexity. | ||||
| Address | Seul; Corea; October 2019 | ||||
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| Area | Expedition | Conference | ICCV | ||
| Notes | MSIAU; 600.122 | Approved | no | ||
| Call Number | Admin @ si @ BRP2019 | Serial | 3290 | ||
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| Author | David Masip; Michael S. North ; Alexander Todorov; Daniel N. Osherson | ||||
| Title | Automated Prediction of Preferences Using Facial Expressions | Type | Journal Article | ||
| Year | 2014 | Publication | PloS one | Abbreviated Journal | Plos |
| Volume | 9 | Issue | 2 | Pages | e87434 |
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| Abstract |
We introduce a computer vision problem from social cognition, namely, the automated detection of attitudes from a person's spontaneous facial expressions. To illustrate the challenges, we introduce two simple algorithms designed to predict observers’ preferences between images (e.g., of celebrities) based on covert videos of the observers’ faces. The two algorithms are almost as accurate as human judges performing the same task but nonetheless far from perfect. Our approach is to locate facial landmarks, then predict preference on the basis of their temporal dynamics. The database contains 768 videos involving four different kinds of preferences. We make it publically available. | ||||
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| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MNT2014 | Serial | 2453 | ||
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| Author | David Berga; Marc Masana; Joost Van de Weijer | ||||
| Title | Disentanglement of Color and Shape Representations for Continual Learning | Type | Conference Article | ||
| Year | 2020 | Publication | ICML Workshop on Continual Learning | Abbreviated Journal | |
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We hypothesize that disentangled feature representations suffer less from catastrophic forgetting. As a case study we perform explicit disentanglement of color and shape, by adjusting the network architecture. We tested classification accuracy and forgetting in a task-incremental setting with Oxford-102 Flowers dataset. We combine our method with Elastic Weight Consolidation, Learning without Forgetting, Synaptic Intelligence and Memory Aware Synapses, and show that feature disentanglement positively impacts continual learning performance. | ||||
| Address | Virtual; July 2020 | ||||
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| Area | Expedition | Conference | ICMLW | ||
| Notes | LAMP; 600.120 | Approved | no | ||
| Call Number | Admin @ si @ BMW2020 | Serial | 3506 | ||
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| Author | Carolina Malagelada; Michal Drozdzal; Santiago Segui; Sara Mendez; Jordi Vitria; Petia Radeva; Javier Santos; Anna Accarino; Juan R. Malagelada; Fernando Azpiroz | ||||
| Title | Classification of functional bowel disorders by objective physiological criteria based on endoluminal image analysis | Type | Journal Article | ||
| Year | 2015 | Publication | American Journal of Physiology-Gastrointestinal and Liver Physiology | Abbreviated Journal | AJPGI |
| Volume | 309 | Issue | 6 | Pages | G413--G419 |
| Keywords | capsule endoscopy; computer vision analysis; functional bowel disorders; intestinal motility; machine learning | ||||
| Abstract |
We have previously developed an original method to evaluate small bowel motor function based on computer vision analysis of endoluminal images obtained by capsule endoscopy. Our aim was to demonstrate intestinal motor abnormalities in patients with functional bowel disorders by endoluminal vision analysis. Patients with functional bowel disorders (n = 205) and healthy subjects (n = 136) ingested the endoscopic capsule (Pillcam-SB2, Given-Imaging) after overnight fast and 45 min after gastric exit of the capsule a liquid meal (300 ml, 1 kcal/ml) was administered. Endoluminal image analysis was performed by computer vision and machine learning techniques to define the normal range and to identify clusters of abnormal function. After training the algorithm, we used 196 patients and 48 healthy subjects, completely naive, as test set. In the test set, 51 patients (26%) were detected outside the normal range (P < 0.001 vs. 3 healthy subjects) and clustered into hypo- and hyperdynamic subgroups compared with healthy subjects. Patients with hypodynamic behavior (n = 38) exhibited less luminal closure sequences (41 ± 2% of the recording time vs. 61 ± 2%; P < 0.001) and more static sequences (38 ± 3 vs. 20 ± 2%; P < 0.001); in contrast, patients with hyperdynamic behavior (n = 13) had an increased proportion of luminal closure sequences (73 ± 4 vs. 61 ± 2%; P = 0.029) and more high-motion sequences (3 ± 1 vs. 0.5 ± 0.1%; P < 0.001). Applying an original methodology, we have developed a novel classification of functional gut disorders based on objective, physiological criteria of small bowel function. | ||||
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| Publisher | American Physiological Society | Place of Publication | Editor | ||
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| Notes | MILAB; OR;MV | Approved | no | ||
| Call Number | Admin @ si @ MDS2015 | Serial | 2666 | ||
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| Author | C. Alejandro Parraga; Javier Vazquez; Maria Vanrell | ||||
| Title | A new cone activation-based natural images dataset | Type | Journal Article | ||
| Year | 2009 | Publication | Perception | Abbreviated Journal | PER |
| Volume | 36 | Issue | Pages | 180 | |
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| Abstract |
We generated a new dataset of digital natural images where each colour plane corresponds to the human LMS (long-, medium-, short-wavelength) cone activations. The images were chosen to represent five different visual environments (eg forest, seaside, mountain snow, urban, motorways) and were taken under natural illumination at different times of day. At the bottom-left corner of each picture there was a matte grey ball of approximately constant spectral reflectance (across the camera's response spectrum,) and nearly Lambertian reflective properties, which allows to compute (and remove, if necessary) the illuminant's colour and intensity. The camera (Sigma Foveon SD10) was calibrated by measuring its sensor's spectral responses using a set of 31 spectrally narrowband interference filters. This allowed conversion of the final camera-dependent RGB colour space into the Smith and Pokorny (1975) cone activation space by means of a polynomial transformation, optimised for a set of 1269 Munsell chip reflectances. This new method is an improvement over the usual 3 × 3 matrix transformation which is only accurate for spectrally-narrowband colours. The camera-to-LMS transformation can be recalculated to consider other non-human visual systems. The dataset is available to download from our website. | ||||
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| Notes | CIC | Approved | no | ||
| Call Number | CAT @ cat @ PVV2009 | Serial | 1193 | ||
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| Author | Joan M. Nuñez; Jorge Bernal; Miquel Ferrer; Fernando Vilariño | ||||
| Title | Impact of Keypoint Detection on Graph-based Characterization of Blood Vessels in Colonoscopy Videos | Type | Conference Article | ||
| Year | 2014 | Publication | CARE workshop | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | Colonoscopy; Graph Matching; Biometrics; Vessel; Intersection | ||||
| Abstract |
We explore the potential of the use of blood vessels as anatomical landmarks for developing image registration methods in colonoscopy images. An unequivocal representation of blood vessels could be used to guide follow-up methods to track lesions over different interventions. We propose a graph-based representation to characterize network structures, such as blood vessels, based on the use of intersections and endpoints. We present a study consisting of the assessment of the minimal performance a keypoint detector should achieve so that the structure can still be recognized. Experimental results prove that, even by achieving a loss of 35% of the keypoints, the descriptive power of the associated graphs to the vessel pattern is still high enough to recognize blood vessels. | ||||
| Address | Boston; USA; September 2014 | ||||
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| Area | Expedition | Conference | CARE | ||
| Notes | MV; DAG; 600.060; 600.047; 600.077;SIAI | Approved | no | ||
| Call Number | Admin @ si @ NBF2014 | Serial | 2504 | ||
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| Author | Olivier Penacchio | ||||
| Title | Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane | Type | Journal Article | ||
| Year | 2011 | Publication | Mathematische Nachrichten | Abbreviated Journal | MN |
| Volume | 284 | Issue | 4 | Pages | 526-542 |
| Keywords | Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25 | ||||
| Abstract |
We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim | ||||
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| Publisher | WILEY-VCH Verlag | Place of Publication | Editor | R. Mennicken | |
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| ISSN | 1522-2616 | ISBN | Medium | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Pen2011 | Serial | 1721 | ||
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| Author | Nicola Bellotto; Eric Sommerlade; Ben Benfold; Charles Bibby; I. Reid; Daniel Roth; Luc Van Gool; Carles Fernandez; Jordi Gonzalez | ||||
| Title | A Distributed Camera System for Multi-Resolution Surveillance | Type | Conference Article | ||
| Year | 2009 | Publication | 3rd ACM/IEEE International Conference on Distributed Smart Cameras | Abbreviated Journal | |
| Volume | Issue | Pages | |||
| Keywords | 10.1109/ICDSC.2009.5289413 | ||||
| Abstract |
We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance. | ||||
| Address | Como, Italy | ||||
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| Area | Expedition | Conference | ICDSC | ||
| Notes | Approved | no | |||
| Call Number | ISE @ ise @ BSB2009 | Serial | 1205 | ||
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| Author | Fahad Shahbaz Khan; Joost Van de Weijer; Andrew Bagdanov; Maria Vanrell | ||||
| Title | Portmanteau Vocabularies for Multi-Cue Image Representation | Type | Conference Article | ||
| Year | 2011 | Publication | 25th Annual Conference on Neural Information Processing Systems | Abbreviated Journal | |
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We describe a novel technique for feature combination in the bag-of-words model of image classification. Our approach builds discriminative compound words from primitive cues learned independently from training images. Our main observation is that modeling joint-cue distributions independently is more statistically robust for typical classification problems than attempting to empirically estimate the dependent, joint-cue distribution directly. We use Information theoretic vocabulary compression to find discriminative combinations of cues and the resulting vocabulary of portmanteau words is compact, has the cue binding property, and supports individual weighting of cues in the final image representation. State-of-the-art results on both the Oxford Flower-102 and Caltech-UCSD Bird-200 datasets demonstrate the effectiveness of our technique compared to other, significantly more complex approaches to multi-cue image representation | ||||
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| Area | Expedition | Conference | NIPS | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ KWB2011 | Serial | 1865 | ||
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| Author | Ernest Valveny; Enric Marti | ||||
| Title | Deformable Template Matching within a Bayesian Framework for Hand-Written Graphic Symbol Recognition | Type | Journal Article | ||
| Year | 2000 | Publication | Graphics Recognition Recent Advances | Abbreviated Journal | |
| Volume | 1941 | Issue | Pages | 193-208 | |
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| Abstract |
We describe a method for hand-drawn symbol recognition based on deformable template matching able to handle uncertainty and imprecision inherent to hand-drawing. Symbols are represented as a set of straight lines and their deformations as geometric transformations of these lines. Matching, however, is done over the original binary image to avoid loss of information during line detection. It is defined as an energy minimization problem, using a Bayesian framework which allows to combine fidelity to ideal shape of the symbol and flexibility to modify the symbol in order to get the best fit to the binary input image. Prior to matching, we find the best global transformation of the symbol to start the recognition process, based on the distance between symbol lines and image lines. We have applied this method to the recognition of dimensions and symbols in architectural floor plans and we show its flexibility to recognize distorted symbols. | ||||
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| Corporate Author | Springer Verlag | Thesis | |||
| Publisher | Springer Verlag | Place of Publication | Editor | ||
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| Notes | DAG;IAM; | Approved | no | ||
| Call Number | IAM @ iam @ MVA2000 | Serial | 1655 | ||
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