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Josep Llados; Jaime Lopez-Krahe; Enric Marti |
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Title |
A system to understand hand-drawn floor plans using subgraph isomorphism and Hough transform |
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1997 |
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Machine Vision and Applications |
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10 |
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3 |
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150-158 |
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Line drawings – Hough transform – Graph matching – CAD systems – Graphics recognition |
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Presently, man-machine interface development is a widespread research activity. A system to understand hand drawn architectural drawings in a CAD environment is presented in this paper. To understand a document, we have to identify its building elements and their structural properties. An attributed graph structure is chosen as a symbolic representation of the input document and the patterns to recognize in it. An inexact subgraph isomorphism procedure using relaxation labeling techniques is performed. In this paper we focus on how to speed up the matching. There is a building element, the walls, characterized by a hatching pattern. Using a straight line Hough transform (SLHT)-based method, we recognize this pattern, characterized by parallel straight lines, and remove from the input graph the edges belonging to this pattern. The isomorphism is then applied to the remainder of the input graph. When all the building elements have been recognized, the document is redrawn, correcting the inaccurate strokes obtained from a hand-drawn input. |
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DAG;IAM |
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IAM @ iam @ LLM1997a |
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1566 |
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Author |
Josep Llados; Jaime Lopez-Krahe; Enric Marti |
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Title |
Hand drawn document understanding using the straight line Hough transform and graph matching |
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Conference Article |
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1996 |
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Proceedings of the 13th International Pattern Recognition Conference (ICPR’96) |
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2 |
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497-501 |
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This paper presents a system to understand hand drawn architectural drawings in a CAD environment. The procedure is to identify in a floor plan the building elements, stored in a library of patterns, and their spatial relationships. The vectorized input document and the patterns to recognize are represented by attributed graphs. To recognize the patterns as such, we apply a structural approach based on subgraph isomorphism techniques. In spite of their value, graph matching techniques do not recognize adequately those building elements characterized by hatching patterns, i.e. walls. Here we focus on the recognition of hatching patterns and develop a straight line Hough transform based method in order to detect the regions filled in with parallel straight fines. This allows not only to recognize filling patterns, but it actually reduces the computational load associated with the subgraph isomorphism computation. The result is that the document can be redrawn by editing all the patterns recognized |
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Vienna , Austria |
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DAG;IAM; |
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IAM @ iam @ LLM1996 |
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1579 |
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Author |
Josep Llados; Jaime Lopez-Krahe; Gemma Sanchez; Enric Marti |
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Title |
Interprétation de cartes et plans par mise en correspondance de graphes de attributs |
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Conference Article |
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2000 |
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12 Congrès Francophone AFRIF–AFIA |
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3 |
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225-234 |
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RFIA |
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DAG;IAM |
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IAM @ iam @ LLS2000 |
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1567 |
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Author |
Josep Llados;Horst Bunke; Enric Marti |
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Title |
Using Cyclic String Matching to Find Rotational and Reflectional Symmetries in Shapes |
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1997 |
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Intelligent Robots: Sensing, Modeling and Planning |
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164-179 |
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Dagstuhl Workshop |
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World Scientific Press |
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9810231857 |
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DAG;IAM; |
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IAM @ iam @ LBM1997b |
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1563 |
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Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
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Title |
BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation |
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2023 |
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Computer Methods and Programs in Biomedicine |
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CMPB |
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228 |
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107241 |
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Videobronchoscopy guiding; Deep learning; Architecture optimization; Datasets; Standardized evaluation framework; Pose estimation |
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Vision-based bronchoscopy (VB) models require the registration of the virtual lung model with the frames from the video bronchoscopy to provide effective guidance during the biopsy. The registration can be achieved by either tracking the position and orientation of the bronchoscopy camera or by calibrating its deviation from the pose (position and orientation) simulated in the virtual lung model. Recent advances in neural networks and temporal image processing have provided new opportunities for guided bronchoscopy. However, such progress has been hindered by the lack of comparative experimental conditions.
In the present paper, we share a novel synthetic dataset allowing for a fair comparison of methods. Moreover, this paper investigates several neural network architectures for the learning of temporal information at different levels of subject personalization. In order to improve orientation measurement, we also present a standardized comparison framework and a novel metric for camera orientation learning. Results on the dataset show that the proposed metric and architectures, as well as the standardized conditions, provide notable improvements to current state-of-the-art camera pose estimation in video bronchoscopy. |
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Elsevier |
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IAM; |
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Admin @ si @ BSC2023 |
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3702 |
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Author |
Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil |
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Title |
A benchmark for the evaluation of computational methods for bronchoscopic navigation |
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2022 |
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International Journal of Computer Assisted Radiology and Surgery |
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IJCARS |
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17 |
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1 |
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IAM |
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Admin @ si @ BSC2022 |
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3832 |
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Author |
Katerine Diaz; Aura Hernandez-Sabate; Antonio Lopez |
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Title |
A reduced feature set for driver head pose estimation |
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Journal Article |
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2016 |
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Applied Soft Computing |
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ASOC |
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45 |
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98-107 |
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Head pose estimation; driving performance evaluation; subspace based methods; linear regression |
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Evaluation of driving performance is of utmost importance in order to reduce road accident rate. Since driving ability includes visual-spatial and operational attention, among others, head pose estimation of the driver is a crucial indicator of driving performance. This paper proposes a new automatic method for coarse and fine head's yaw angle estimation of the driver. We rely on a set of geometric features computed from just three representative facial keypoints, namely the center of the eyes and the nose tip. With these geometric features, our method combines two manifold embedding methods and a linear regression one. In addition, the method has a confidence mechanism to decide if the classification of a sample is not reliable. The approach has been tested using the CMU-PIE dataset and our own driver dataset. Despite the very few facial keypoints required, the results are comparable to the state-of-the-art techniques. The low computational cost of the method and its robustness makes feasible to integrate it in massive consume devices as a real time application. |
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ADAS; 600.085; 600.076;;IAM |
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Admin @ si @ DHL2016 |
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2760 |
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Author |
Katerine Diaz; Francesc J. Ferri; Aura Hernandez-Sabate |
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An overview of incremental feature extraction methods based on linear subspaces |
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2018 |
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Knowledge-Based Systems |
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KBS |
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145 |
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219-235 |
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With the massive explosion of machine learning in our day-to-day life, incremental and adaptive learning has become a major topic, crucial to keep up-to-date and improve classification models and their corresponding feature extraction processes. This paper presents a categorized overview of incremental feature extraction based on linear subspace methods which aim at incorporating new information to the already acquired knowledge without accessing previous data. Specifically, this paper focuses on those linear dimensionality reduction methods with orthogonal matrix constraints based on global loss function, due to the extensive use of their batch approaches versus other linear alternatives. Thus, we cover the approaches derived from Principal Components Analysis, Linear Discriminative Analysis and Discriminative Common Vector methods. For each basic method, its incremental approaches are differentiated according to the subspace model and matrix decomposition involved in the updating process. Besides this categorization, several updating strategies are distinguished according to the amount of data used to update and to the fact of considering a static or dynamic number of classes. Moreover, the specific role of the size/dimension ratio in each method is considered. Finally, computational complexity, experimental setup and the accuracy rates according to published results are compiled and analyzed, and an empirical evaluation is done to compare the best approach of each kind. |
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0950-7051 |
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ADAS; 600.118;IAM |
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Admin @ si @ DFH2018 |
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3090 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate |
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Title |
Decremental generalized discriminative common vectors applied to images classification |
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2017 |
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Knowledge-Based Systems |
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KBS |
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131 |
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46-57 |
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Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification |
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In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model. |
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ADAS; 600.118; 600.121;IAM |
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Admin @ si @ DMH2017a |
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3003 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |
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Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
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Journal Article |
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2018 |
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IEEE Access |
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ACCESS |
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6 |
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18325 - 18334 |
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Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
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In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
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2169-3536 |
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ADAS; 600.118;IAM |
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Admin @ si @ DMH2018b |
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3091 |
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