Fuzzy Intervals for Designing Structural Signature: An Application to Graphic Symbol Recognition

Revised selected papers from Eighth IAPR International Workshop on Graphics RECognition (GREC) 2009.The motivation behind our work is to present a new methodology for symbol recognition. The proposed method employs a structural approach for representing visual associations in symbols and a statistical classifier for recognition. We vectorize a graphic symbol, encode its topological and geometrical information by an attributed relational graph and compute a signature from this structural graph. We have addressed the sensitivity of structural representations to noise, by using data adapted fuzzy intervals. The joint probability distribution of signatures is encoded by a Bayesian network, which serves as a mechanism for pruning irrelevant features and choosing a subset of interesting features from structural signatures of underlying symbol set. The Bayesian network is deployed in a supervised learning scenario for recognizing query symbols. The method has been evaluated for robustness against degradations & deformations on pre-segmented 2D linear architectural & electronic symbols from GREC databases, and for its recognition abilities on symbols with context noise i.e. cropped symbols

Partial shape matching using CCP map and weighted graph transformation matching

La détection de la similarité ou de la différence entre les images et leur mise en correspondance sont des problèmes fondamentaux dans le traitement de l'image. Pour résoudre ces problèmes, on utilise, dans la littérature, différents algorithmes d'appariement. Malgré leur nouveauté, ces algorithmes sont pour la plupart inefficaces et ne peuvent pas fonctionner correctement dans les situations d’images bruitées. Dans ce mémoire, nous résolvons la plupart des problèmes de ces méthodes en utilisant un algorithme fiable pour segmenter la carte des contours image, appelée carte des CCPs, et une nouvelle méthode d'appariement. Dans notre algorithme, nous utilisons un descripteur local qui est rapide à calculer, est invariant aux transformations affines et est fiable pour des objets non rigides et des situations d’occultation. Après avoir trouvé le meilleur appariement pour chaque contour, nous devons vérifier si ces derniers sont correctement appariés. Pour ce faire, nous utilisons l'approche « Weighted Graph Transformation Matching » (WGTM), qui est capable d'éliminer les appariements aberrants en fonction de leur proximité et de leurs relations géométriques. WGTM fonctionne correctement pour les objets à la fois rigides et non rigides et est robuste aux distorsions importantes. Pour évaluer notre méthode, le jeu de données ETHZ comportant cinq classes différentes d'objets (bouteilles, cygnes, tasses, girafes, logos Apple) est utilisé. Enfin, notre méthode est comparée à plusieurs méthodes célèbres proposées par d'autres chercheurs dans la littérature. Bien que notre méthode donne un résultat comparable à celui des méthodes de référence en termes du rappel et de la précision de localisation des frontières, elle améliore significativement la précision moyenne pour toutes les catégories du jeu de données ETHZ.Matching and detecting similarity or dissimilarity between images is a fundamental problem in image processing. Different matching algorithms are used in literature to solve this fundamental problem. Despite their novelty, these algorithms are mostly inefficient and cannot perform properly in noisy situations. In this thesis, we solve most of the problems of previous methods by using a reliable algorithm for segmenting image contour map, called CCP Map, and a new matching method. In our algorithm, we use a local shape descriptor that is very fast, invariant to affine transform, and robust for dealing with non-rigid objects and occlusion. After finding the best match for the contours, we need to verify if they are correctly matched. For this matter, we use the Weighted Graph Transformation Matching (WGTM) approach, which is capable of removing outliers based on their adjacency and geometrical relationships. WGTM works properly for both rigid and non-rigid objects and is robust to high order distortions. For evaluating our method, the ETHZ dataset including five diverse classes of objects (bottles, swans, mugs, giraffes, apple-logos) is used. Finally, our method is compared to several famous methods proposed by other researchers in the literature. While our method shows a comparable result to other benchmarks in terms of recall and the precision of boundary localization, it significantly improves the average precision for all of the categories in the ETHZ dataset

Complex scene modeling and segmentation with deformable simplex meshes

In this thesis we present a system for 3D reconstruction and segmentation of complex real world scenes. The input to the system is an unstructured cloud of 3D points. The output is a 3D model for each object in the scene. The system starts with a model that encloses the input point cloud. A deformation process is applied to the initial model so it gets close to the point cloud in terms of distance, geometry and topology. Once the deformation stops the model is analyzed to check if more than one object is present in the point cloud. If necessary a segmentation process splits the model into several parts that correspond to each object in the scene. Using this segmented model the point cloud is also segmented. Each resulting sub-cloud is treated as a new input to the system. If, after the deformation process, the model is not segmented a refinement process improves the objective and subjective quality of the model by concentrating vertices around high curvature areas. The simplex mesh reconstruction algorithm was modified and extended to suit our application. A novel segmentation algorithm was designed to be applied on the simplex mesh. We test the system with synthetic and real data obtained from single objects, simple. and complex scenes. In the case of the synthetic data different levels of noise are added to examine the performance of the system. The results show that the systems performs well for either of the three cases and also in the presence of low levels of noise

Symbol Recognition: Current Advances and Perspectives

Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content

Active shape models with focus on overlapping problems applied to plant detection and soil pore analysis

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Embodied Interactions for Spatial Design Ideation: Symbolic, Geometric, and Tangible Approaches

Computer interfaces are evolving from mere aids for number crunching into active partners in creative processes such as art and design. This is, to a great extent, the result of mass availability of new interaction technology such as depth sensing, sensor integration in mobile devices, and increasing computational power. We are now witnessing the emergence of maker culture that can elevate art and design beyond the purview of enterprises and professionals such as trained engineers and artists. Materializing this transformation is not trivial; everyone has ideas but only a select few can bring them to reality. The challenge is the recognition and the subsequent interpretation of human actions into design intent

Fast and accurate NN approach for multi-event annotation of time series

technical reportSimilarity search in time-series subsequences is an important time series data mining task. Searching in time series subsequences for matches for a set of shapes is an extension of this task and is equally important. In this work we propose a simple but efficient approach for finding matches for a group of shapes or events in a given time series using a Nearest Neighbor approach. We provide various improvements of this approach including one using the GNAT data structure. We also propose a technique for finding similar shapes of widely varying temporal width. Both of these techniques for primitive shape matching allow us to more accurately and efficiently form an event representation of a time-series, leading in turn to finding complex events which are composites of primitive events. We demonstrate the robustness of our approaches in detecting complex shapes even in the presence of ?don?t care? symbols. We evaluate the success of our approach in detecting both primitive and complex shapes using a data set from the Fluid Dynamics domain. We also show a speedup of up to 5 times over a na?ve nearest neighbor approach

Relation Bag-of-Features for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on relation bag-of-features (BOFs) which are computed between the extracted visual primitives. Our feature consists of pairwise spatial relations from all possible combina tions of individual visual primitives. The key characteristic of the overall process is to use topological information to guide directional relations. Consequently, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using two different datasets. Experimental tests provide interesting results by establishing user-friendly symbol retrieval application