A novel method for extracting and recognizing logos

Nowadays, the high volume of archival documents has made it exigent to store documents in electronic databases. A text logo represents the ownership of the text, and different texts can be categorized by it; for this reason, different methods have been presented for extracting and recognizing logos. The methods presented earlier, suffer problems such as, error of logo detection and recognition and slow speed. The proposed method of this study is composed of three sections: In the first section, the exact position of the logo can be identified by the pyramidal tree structure and horizontal and vertical analysis, and in the second section, the logo can be extracted through the algorithm of the boundary extension of feature rectangles. In the third section, after normalizing the size of the logo and eliminating the skew angle, for feature extraction, we first blocked the region encompassing the logo, and then we extract a particular feature by the parameter of the center of gravity of connected component each block. Finally, we use the KNN classification for the recognition of the logo.DOI:http://dx.doi.org/10.11591/ijece.v2i5.129

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

RECALAGE RIGIDE DE RELEVÉS LASER PAR MISE EN CORRESPONDANCE ROBUSTE BASÉE SUR DES SEGMENTS

International audienceLe recalage de relevés laser se révèle indispensable pour assembler des données précises devant servir à l'analyse, à la documentation et à la reconstruction tridimensionnelle d'environnements. Ce problème apparaît lorsqu'une zone d'intérêt est numérisée, au fil de temps, deux ou plusieurs fois, ou quand sa complexité nécessite un accroissement du nombre de stations de scanner laser fixe. Aussi, en raison de la variété des techniques disponibles d'acquisition, l'intégration multi-données devient une question importante puisqu'elle permet de mettre en cohérence des données contenant souvent une information complémentaire. La vaste majorité des algorithmes existants s'appuient sur les éléments ponctuels. C'est pourquoi les approches basées sur l'ICP demeurent actuellement les plus répandues. Cet article propose l'utilisation des arêtes sous forme d'intersections entre les plans modelés, pour le recalage rigide des nuages de points mobiles avec d'autres données, qu'elles soient 2D ou 3D. Ces primitives peuvent être aisément extraites, même si les données laser sont peu denses. Quelques méthodes de recalage basées sur les entités linéaires ont été examinées afin de vérifier leur précision et robustesse au bruit. Définie en tant que distance modifiée de Hausdorff entre deux jeux de segments, le critère d'exactitude a été employé pour les besoins d'une analyse quantitative. Au vu de ces éléments, la transformation rigide décrivant le meilleur alignement peut être déterminée avec l'algorithme FMII. Étant donné que la mise en correspondance automatique d'entités linéaires est ardue et influence l'estimation des paramètres de passage, une méthode d'appariement étudiant la similitude relative a été suggérée. Tous ces composants ont été ensuite intégrés pour créer une approche combinée RANSAC-FMII. Enfin, la précision de cette méthode de recalage avec appariements explicites itérés basant sur les segments a été évaluée et discutée. Abstract In the processes of analyzing, documenting and modelling the surrounding environment, an accurate registration of point clouds is necessary in order to obtain high-quality data. This procedure arises whenever a particular area is scanned by a laser more than once or from several different positions. Due to the variety of surveying techniques available, fusing the multi-source, complementary information data into one model is a very important matter. The vast majority of existing registration algorithms operate on feature points, hence ICP-like methods are the prevalent approach. This article proposes the use of edges obtained from intersecting planes modelled within individual point clouds for the accurate registration of mobile laser scans with other data, whether 2D or 3D. This type of primitives can be easily extracted, even from low-density point clouds. Using simulated data, several existing line-based registration algorithms have been evaluated in terms of reliability and robustness to noise. For the purpose of quantitative assessment, an accuracy criterion taking advantage of a modified Hausdorff distance between two sets of lines has been employed. Having regard to these elements, the rigid body transformation that gives the best alignment can be calculated with FMII algorithm. Since the automatic pairing of line segments is a challenging task influencing the accuracy of the estimated transformation parameters, a method that considers the relative similarity is proposed. All these components are used to form an approach combining RANSAC-FMII algorithms. Finally, the accuracy of this line-based registration method with an explicit iterative matching is evaluated and discussed

Examining Factors of Sports Brand Logo Design that Influence Purchase Intentions : Undergraduate Students Perspective

Logo is vital for a company as it plays the huge role for the growth of business. Every company owner needs and desires their organization to be recognisable and boost the profit of company well. To achieve an impressive success in this competitive and challenging market, they must be one step ahead of their competitors. Thus, a company's meaningful logo can have a major effect on the market and attract new customers to boost revenue. As logo is the essentially branding tool, it is a company's or business's first point of communication and exposure with the world. This research study demonstrates the Signalling Theory which indicates the relationship between colour combination, shapes, lines and fonts used in logo towards purchase intention in sport’s brand product from University Malaysia Sarawak (Unimas) undergraduate students’ perspective. 374 questionnaires were distributed to respondents which are undergraduate students from Unimas for the purpose of testing and analysis of the hypotheses in this research study. The data collected was evaluated with the Statistical Package for the Social Sciences (SPSS) version 26.0. In this research, it was found that the shape of the logo and the line used in the logo had a major positive relationship with purchase intention towards sport brand product from Unimas undergraduate students’ perspective, while the colour combination of the logo and the font used in the logo had a minimal impact on purchase intention towards sport brand product among Unimas undergraduate students. In contrast, the relatively insignificant impact of colour combination and font used in logo on sport brand product implementation tended to be influenced by other variables that yet to be determined

Adaptive Algorithms for Automated Processing of Document Images

Large scale document digitization projects continue to motivate interesting document understanding technologies such as script and language identification, page classification, segmentation and enhancement. Typically, however, solutions are still limited to narrow domains or regular formats such as books, forms, articles or letters and operate best on clean documents scanned in a controlled environment. More general collections of heterogeneous documents challenge the basic assumptions of state-of-the-art technology regarding quality, script, content and layout. Our work explores the use of adaptive algorithms for the automated analysis of noisy and complex document collections. We first propose, implement and evaluate an adaptive clutter detection and removal technique for complex binary documents. Our distance transform based technique aims to remove irregular and independent unwanted foreground content while leaving text content untouched. The novelty of this approach is in its determination of best approximation to clutter-content boundary with text like structures. Second, we describe a page segmentation technique called Voronoi++ for complex layouts which builds upon the state-of-the-art method proposed by Kise [Kise1999]. Our approach does not assume structured text zones and is designed to handle multi-lingual text in both handwritten and printed form. Voronoi++ is a dynamically adaptive and contextually aware approach that considers components' separation features combined with Docstrum [O'Gorman1993] based angular and neighborhood features to form provisional zone hypotheses. These provisional zones are then verified based on the context built from local separation and high-level content features. Finally, our research proposes a generic model to segment and to recognize characters for any complex syllabic or non-syllabic script, using font-models. This concept is based on the fact that font files contain all the information necessary to render text and thus a model for how to decompose them. Instead of script-specific routines, this work is a step towards a generic character and recognition scheme for both Latin and non-Latin scripts

APPROXIMATION ALGORITHMS FOR POINT PATTERN MATCHING AND SEARCHI NG

Point pattern matching is a fundamental problem in computational geometry. For given a reference set and pattern set, the problem is to find a geometric transformation applied to the pattern set that minimizes some given distance measure with respect to the reference set. This problem has been heavily researched under various distance measures and error models. Point set similarity searching is variation of this problem in which a large database of point sets is given, and the task is to preprocess this database into a data structure so that, given a query point set, it is possible to rapidly find the nearest point set among elements of the database. Here, the term nearest is understood in above sense of pattern matching, where the elements of the database may be transformed to match the given query set. The approach presented here is to compute a low distortion embedding of the pattern matching problem into an (ideally) low dimensional metric space and then apply any standard algorithm for nearest neighbor searching over this metric space. This main focus of this dissertation is on two problems in the area of point pattern matching and searching algorithms: (i) improving the accuracy of alignment-based point pattern matching and (ii) computing low-distortion embeddings of point sets into vector spaces. For the first problem, new methods are presented for matching point sets based on alignments of small subsets of points. It is shown that these methods lead to better approximation bounds for alignment-based planar point pattern matching algorithms under the Hausdorff distance. Furthermore, it is shown that these approximation bounds are nearly the best achievable by alignment-based methods. For the second problem, results are presented for two different distance measures. First, point pattern similarity search under translation for point sets in multidimensional integer space is considered, where the distance function is the symmetric difference. A randomized embedding into real space under the L1 metric is given. The algorithm achieves an expected distortion of O(log2 n). Second, an algorithm is given for embedding Rd under the Earth Mover's Distance (EMD) into multidimensional integer space under the symmetric difference distance. This embedding achieves a distortion of O(log D), where D is the diameter of the point set. Combining this with the above result implies that point pattern similarity search with translation under the EMD can be embedded in to real space in the L1 metric with an expected distortion of O(log2 n log D)

Empirical exploration of air traffic and human dynamics in terminal airspaces

Air traffic is widely known as a complex, task-critical techno-social system, with numerous interactions between airspace, procedures, aircraft and air traffic controllers. In order to develop and deploy high-level operational concepts and automation systems scientifically and effectively, it is essential to conduct an in-depth investigation on the intrinsic traffic-human dynamics and characteristics, which is not widely seen in the literature. To fill this gap, we propose a multi-layer network to model and analyze air traffic systems. A Route-based Airspace Network (RAN) and Flight Trajectory Network (FTN) encapsulate critical physical and operational characteristics; an Integrated Flow-Driven Network (IFDN) and Interrelated Conflict-Communication Network (ICCN) are formulated to represent air traffic flow transmissions and intervention from air traffic controllers, respectively. Furthermore, a set of analytical metrics including network variables, complex network attributes, controllers' cognitive complexity, and chaotic metrics are introduced and applied in a case study of Guangzhou terminal airspace. Empirical results show the existence of fundamental diagram and macroscopic fundamental diagram at the route, sector and terminal levels. Moreover, the dynamics and underlying mechanisms of "ATCOs-flow" interactions are revealed and interpreted by adaptive meta-cognition strategies based on network analysis of the ICCN. Finally, at the system level, chaos is identified in conflict system and human behavioral system when traffic switch to the semi-stable or congested phase. This study offers analytical tools for understanding the complex human-flow interactions at potentially a broad range of air traffic systems, and underpins future developments and automation of intelligent air traffic management systems.Comment: 30 pages, 28 figures, currently under revie