|
|
Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2014). Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. In Graphics Recognition. Current Trends and Challenges (Vol. 8746, pp. 109–121). LNCS. Springer Berlin Heidelberg.
Abstract: In this paper we present a wall segmentation approach in floor plans that is able to work independently to the graphical notation, does not need any pre-annotated data for learning, and is able to segment multiple-shaped walls such as beams and curved-walls. This method results from the combination of the wall segmentation approaches [3, 5] presented recently by the authors. Firstly, potential straight wall segments are extracted in an unsupervised way similar to [3], but restricting even more the wall candidates considered in the original approach. Then, based on [5], these segments are used to learn the texture pattern of walls and spot the lost instances. The presented combination of both methods has been tested on 4 available datasets with different notations and compared qualitatively and quantitatively to the state-of-the-art applied on these collections. Additionally, some qualitative results on floor plans directly downloaded from the Internet are reported in the paper. The overall performance of the method demonstrates either its adaptability to different wall notations and shapes, and to document qualities and resolutions.
Keywords: Graphics recognition; Floor plan analysis; Object segmentation
|
|
|
|
Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2013). Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies. In 10th IAPR International Workshop on Graphics Recognition.
|
|
|
|
Lluis Pere de las Heras, Ernest Valveny, & Gemma Sanchez. (2013). Combining structural and statistical strategies for unsupervised wall detection in floor plans. In 10th IAPR International Workshop on Graphics Recognition.
Abstract: This paper presents an evolution of the first unsupervised wall segmentation method in floor plans, that was presented by the authors in [1]. This first approach, contrarily to the existing ones, is able to segment walls independently to their notation and without the need of any pre-annotated data
to learn their visual appearance. Despite the good performance of the first approach, some specific cases, such as curved shaped walls, were not correctly segmented since they do not agree the strict structural assumptions that guide the whole methodology in order to be able to learn, in an unsupervised way, the structure of a wall. In this paper, we refine this strategy by dividing the
process in two steps. In a first step, potential wall segments are extracted unsupervisedly using a modification of [1], by restricting even more the areas considered as walls in a first moment. In a second step, these segments are used to learn and spot lost instances based on a modified version of [2], also presented by the authors. The presented combined method have been tested on
4 datasets with different notations and compared with the stateof-the-art applyed on the same datasets. The results show its adaptability to different wall notations and shapes, significantly outperforming the original approach.
|
|
|
|
Xu Hu. (2012). Real-Time Part Based Models for Object Detection (Vol. 171). Master's thesis, , .
|
|
|
|
Thanh Ha Do, Salvatore Tabbone, & Oriol Ramos Terrades. (2016). Spotting Symbol over Graphical Documents Via Sparsity in Visual Vocabulary. In Recent Trends in Image Processing and Pattern Recognition (Vol. 709).
|
|
|
|
Mario Hernandez, Joao Sanchez, & Jordi Vitria. (2012). Selected papers from Iberian Conference on Pattern Recognition and Image Analysis (Vol. 45).
|
|
|
|
Daniel Hernandez, Lukas Schneider, P. Cebrian, A. Espinosa, David Vazquez, Antonio Lopez, et al. (2019). Slanted Stixels: A way to represent steep streets. IJCV - International Journal of Computer Vision, 127, 1643–1658.
Abstract: This work presents and evaluates a novel compact scene representation based on Stixels that infers geometric and semantic information. Our approach overcomes the previous rather restrictive geometric assumptions for Stixels by introducing a novel depth model to account for non-flat roads and slanted objects. Both semantic and depth cues are used jointly to infer the scene representation in a sound global energy minimization formulation. Furthermore, a novel approximation scheme is introduced in order to significantly reduce the computational complexity of the Stixel algorithm, and then achieve real-time computation capabilities. The idea is to first perform an over-segmentation of the image, discarding the unlikely Stixel cuts, and apply the algorithm only on the remaining Stixel cuts. This work presents a novel over-segmentation strategy based on a fully convolutional network, which outperforms an approach based on using local extrema of the disparity map. We evaluate the proposed methods in terms of semantic and geometric accuracy as well as run-time on four publicly available benchmark datasets. Our approach maintains accuracy on flat road scene datasets while improving substantially on a novel non-flat road dataset.
|
|
|
|
Lluis Pere de las Heras, Oriol Ramos Terrades, Sergi Robles, & Gemma Sanchez. (2015). CVC-FP and SGT: a new database for structural floor plan analysis and its groundtruthing tool. IJDAR - International Journal on Document Analysis and Recognition, 18(1), 15–30.
Abstract: Recent results on structured learning methods have shown the impact of structural information in a wide range of pattern recognition tasks. In the field of document image analysis, there is a long experience on structural methods for the analysis and information extraction of multiple types of documents. Yet, the lack of conveniently annotated and free access databases has not benefited the progress in some areas such as technical drawing understanding. In this paper, we present a floor plan database, named CVC-FP, that is annotated for the architectural objects and their structural relations. To construct this database, we have implemented a groundtruthing tool, the SGT tool, that allows to make specific this sort of information in a natural manner. This tool has been made for general purpose groundtruthing: It allows to define own object classes and properties, multiple labeling options are possible, grants the cooperative work, and provides user and version control. We finally have collected some of the recent work on floor plan interpretation and present a quantitative benchmark for this database. Both CVC-FP database and the SGT tool are freely released to the research community to ease comparisons between methods and boost reproducible research.
|
|
|
|
Antonio Hernandez, Miguel Reyes, Victor Ponce, & Sergio Escalera. (2012). GrabCut-Based Human Segmentation in Video Sequences. SENS - Sensors, 12(11), 15376–15393.
Abstract: In this paper, we present a fully-automatic Spatio-Temporal GrabCut human segmentation methodology that combines tracking and segmentation. GrabCut initialization is performed by a HOG-based subject detection, face detection, and skin color model. Spatial information is included by Mean Shift clustering whereas temporal coherence is considered by the historical of Gaussian Mixture Models. Moreover, full face and pose recovery is obtained by combining human segmentation with Active Appearance Models and Conditional Random Fields. Results over public datasets and in a new Human Limb dataset show a robust segmentation and recovery of both face and pose using the presented methodology.
Keywords: segmentation; human pose recovery; GrabCut; GraphCut; Active Appearance Models; Conditional Random Field
|
|
|
|
Rain Eric Haamer, Eka Rusadze, Iiris Lusi, Tauseef Ahmed, Sergio Escalera, & Gholamreza Anbarjafari. (2018). Review on Emotion Recognition Databases. In Human-Robot Interaction: Theory and Application.
Abstract: Over the past few decades human-computer interaction has become more important in our daily lives and research has developed in many directions: memory research, depression detection, and behavioural deficiency detection, lie detection, (hidden) emotion recognition etc. Because of that, the number of generic emotion and face databases or those tailored to specific needs have grown immensely large. Thus, a comprehensive yet compact guide is needed to help researchers find the most suitable database and understand what types of databases already exist. In this paper, different elicitation methods are discussed and the databases are primarily organized into neat and informative tables based on the format.
Keywords: emotion; computer vision; databases
|
|
|
|
Lluis Pere de las Heras, Oriol Ramos Terrades, & Josep Llados. (2017). Ontology-Based Understanding of Architectural Drawings. In International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges (Vol. 9657, pp. 75–85). LNCS.
Abstract: In this paper we present a knowledge base of architectural documents aiming at improving existing methods of floor plan classification and understanding. It consists of an ontological definition of the domain and the inclusion of real instances coming from both, automatically interpreted and manually labeled documents. The knowledge base has proven to be an effective tool to structure our knowledge and to easily maintain and upgrade it. Moreover, it is an appropriate means to automatically check the consistency of relational data and a convenient complement of hard-coded knowledge interpretation systems.
Keywords: Graphics recognition; Floor plan analysi; Domain ontology
|
|
|
|
Lluis Pere de las Heras, Oriol Ramos Terrades, & Josep Llados. (2015). Attributed Graph Grammar for floor plan analysis. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 726–730).
Abstract: In this paper, we propose the use of an Attributed Graph Grammar as unique framework to model and recognize the structure of floor plans. This grammar represents a building as a hierarchical composition of structurally and semantically related elements, where common representations are learned stochastically from annotated data. Given an input image, the parsing consists on constructing that graph representation that better agrees with the probabilistic model defined by the grammar. The proposed method provides several advantages with respect to the traditional floor plan analysis techniques. It uses an unsupervised statistical approach for detecting walls that adapts to different graphical notations and relaxes strong structural assumptions such are straightness and orthogonality. Moreover, the independence between the knowledge model and the parsing implementation allows the method to learn automatically different building configurations and thus, to cope the existing variability. These advantages are clearly demonstrated by comparing it with the most recent floor plan interpretation techniques on 4 datasets of real floor plans with different notations.
|
|
|
|
Lluis Pere de las Heras, Oriol Ramos Terrades, Josep Llados, David Fernandez, & Cristina Cañero. (2015). Use case visual Bag-of-Words techniques for camera based identity document classification. In 13th International Conference on Document Analysis and Recognition ICDAR2015 (pp. 721–725).
Abstract: Nowadays, automatic identity document recognition, including passport and driving license recognition, is at the core of many applications within the administrative and service sectors, such as police, hospitality, car renting, etc. In former years, the document information was manually extracted whereas today this data is recognized automatically from images obtained by flat-bed scanners. Yet, since these scanners tend to be expensive and voluminous, companies in the sector have recently turned their attention to cheaper, small and yet computationally powerful scanners: the mobile devices. The document identity recognition from mobile images enclose several new difficulties w.r.t traditional scanned images, such as the loss of a controlled background, perspective, blurring, etc. In this paper we present a real application for identity document classification of images taken from mobile devices. This classification process is of extreme importance since a prior knowledge of the document type and origin strongly facilitates the subsequent information extraction. The proposed method is based on a traditional Bagof-Words in which we have taken into consideration several key aspects to enhance recognition rate. The method performance has been studied on three datasets containing more than 2000 images from 129 different document classes.
|
|
|
|
Ivan Huerta, Marco Pedersoli, Jordi Gonzalez, & Alberto Sanfeliu. (2015). Combining where and what in change detection for unsupervised foreground learning in surveillance. PR - Pattern Recognition, 48(3), 709–719.
Abstract: Change detection is the most important task for video surveillance analytics such as foreground and anomaly detection. Current foreground detectors learn models from annotated images since the goal is to generate a robust foreground model able to detect changes in all possible scenarios. Unfortunately, manual labelling is very expensive. Most advanced supervised learning techniques based on generic object detection datasets currently exhibit very poor performance when applied to surveillance datasets because of the unconstrained nature of such environments in terms of types and appearances of objects. In this paper, we take advantage of change detection for training multiple foreground detectors in an unsupervised manner. We use statistical learning techniques which exploit the use of latent parameters for selecting the best foreground model parameters for a given scenario. In essence, the main novelty of our proposed approach is to combine the where (motion segmentation) and what (learning procedure) in change detection in an unsupervised way for improving the specificity and generalization power of foreground detectors at the same time. We propose a framework based on latent support vector machines that, given a noisy initialization based on motion cues, learns the correct position, aspect ratio, and appearance of all moving objects in a particular scene. Specificity is achieved by learning the particular change detections of a given scenario, and generalization is guaranteed since our method can be applied to any possible scene and foreground object, as demonstrated in the experimental results outperforming the state-of-the-art.
Keywords: Object detection; Unsupervised learning; Motion segmentation; Latent variables; Support vector machine; Multiple appearance models; Video surveillance
|
|
|
|
Mustafa Hajij, Mathilde Papillon, Florian Frantzen, Jens Agerberg, Ibrahem AlJabea, Ruben Ballester, et al. (2024). TopoX: A Suite of Python Packages for Machine Learning on Topological Domains.
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.
|
|
