Integral geometry, hypergroups, and I.M. Gelfand's question

This note is an attempt to give an answer for the following old I.M. Gelfand's question: why some important problems of integral geometry (e.g., the Radon transform and others) are related to harmonic analysis on groups, but for other quite similar problems such relations are not clear? In the note we examine standard problems of integral geometry generating harmonic analysis (the Plancherel theorem etc.) on pairs of commutative hypergroups that are in a duality of Pontryagin's type. As a result new meaningful examples of hypergroups are constructed.Comment: 10 pages, to be published in Doklady Mathematics, 201

Spatio-structural Symbol Description with Statistical Feature Add-on

The original publication is available at www.springerlink.comInternational audienceIn this paper, we present a method for symbol description based on both spatio-structural and statistical features computed on elementary visual parts, called 'vocabulary'. This extracted vocabulary is grouped by type (e.g., circle, corner ) and serves as a basis for an attributed relational graph where spatial relational descriptors formalise the links between the vertices, formed by these types, labelled with global shape descriptors. The obtained attributed relational graph description has interesting properties that allows it to be used efficiently for recognising structure and by comparing its attribute signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

One  of  the  most  important  methods  to  solve  traffic  congestion  is  to detect the incident state of a roadway. This paper describes the development of a method  for  road  traffic  monitoring  aimed  at  the  acquisition  and  analysis  of remote  sensing  imagery.  We  propose  a  strategy  for  road  extraction,  vehicle detection  and incident detection  from remote sensing imagery using techniques based on neural networks, Radon transform  for angle detection and traffic-flow measurements.  Traffic-bottleneck  detection  is  another  method  that  is  proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection  method had a detection rate of 87.5%

Otomatisasi Pembacaan Plat Nomor Tentara Nasional Indonesia Angkatan Darat Menggunakan Transformasi Radon Berbasis Pengolahan Citra Digital

ABSTRAKSI: Pada Tugas Akhir ini dibuat suatu sistem untuk pembacaan plat nomor kendaraan TNI-AD dengan memanfaatkan ciri karakter angka 0-9 dan strip (-) menggunakan algoritma Transformasi Radon.Untuk menganalisis dan membaca karakter yang terdapat pada plat nomor kendaraan TNI-AD dalam sebuah citra digital yang diakuisisi dengan membangkitkan citra dari komputer yang diambil dengan menggunakan kamera, dilakukan perbaikan kualitas citra, cropping, dan segmentasi karakter. Kemudian pembacaan karakter pada plat nomor kendaraan TNI-AD dianalisis berdasarkan jumlah, letak dan pola titik-titik pada daerah bayangan hasil Transformasi Radon yang kemudian diklasifikasikan dengan bantuan JST-backpropagation. Pengujian ini dilakukan berdasarkan hasil akuisisi objek dengan perbedaan jarak, perbedaan intensitas cahaya, dan perbedaan fungsi Transformasi Radon yang digunakan. Pada citra dengan derau, ditambahkan derau salt & pepper sebelum proses akuisisi.Dalam menguji tingkat performansi algoritma dan aplikasi yang diimplementasikan, maka diujikan 170 citra plat nomor kendaraan TNI-AD. Akurasi sistem paling baik dihasilkan pengujian menggunakan Transformasi Radon maksimum dengan akurasi 90,83%, pengambilan dengan jarak 100 cm dari kamera dengan akurasi 90%, dan pengambilan pada kondisi cahaya pukul 13.00 – 14.00 dengan akurasi 90,32%. Berdasarkan hasil uji secara keseluruhan, aplikasi ini dapat membaca karakter pada plat nomor kendaraan TNI-AD dengan akurasi 80,53% dengan waktu komputasi rata-rata 10,2 detik.Kata Kunci : Kata Kunci: Plat nomor kendaraan TNI-AD, algoritma Transformasi Radon, JSTbackpropagation.ABSTRACT: This final project is made for automation reading of Indonesian army vehicle license plate using Radon Transform algorithm by utilizing the character trait numbers 0-9 and dashes (-).To analyze and detect the characters on Indonesian army vehicle license plate in a digital image by generating images from a computer and by capturing from the camera, the image quality are improved, cropped, and segmented character. The character that transformed in Radon domain become dots to be analyzed in dots amount, location and formation then, were classified by backpropagation. This test performed based on the results of objects with differences in function Radon Transform algorithm, distances, and under different light intensities. For the noise image, it added noise salt & pepper before the acquisition.170 results of image acquisition were tested for reading of Indonesian army vehicle license plate. Accuracy testing of the system are best generated using maximum Radon Transform with an accuracy value of 90,83%, making a distance of 100 cm from the camera with an accuracy value of 90%, and making the light at 13:00 to 14:00 with an accuracy value of 90,32%. Based on all the test results, the Radon Transform algorithm provides an accuracy value of 80,53%. Average computing time Radon Transform algorithm 10,2 seconds.Keyword: Keywords: Indonesian army vehicle license plate, Radon transform algorithm

BoR: Bag-of-Relations for Symbol Retrieval

International audienceIn this paper, we address a new scheme for symbol retrieval based on bag-of-relations (BoRs) which are computed between extracted visual primitives (e.g. circle and corner). Our features consist of pairwise spatial relations from all possible combinations of individual visual primitives. The key characteristic of the overall process is to use topological relation information indexed in bags-of-relations and use this for recognition. As a consequence, directional relation matching takes place only with those candidates having similar topological configurations. A comprehensive study is made by using several different well known datasets such as GREC, FRESH and SESYD, and includes a comparison with state-of-the-art descriptors. Experiments provide interesting results on symbol spotting and other user-friendly symbol retrieval applications

Integrating Vocabulary Clustering with Spatial Relations for Symbol Recognition

International audienceThis paper develops a structural symbol recognition method with integrated statistical features. It applies spatial organization descriptors to the identified shape features within a fixed visual vocabulary that compose a symbol. It builds an attributed relational graph expressing the spatial relations between those visual vocabulary elements. In order to adapt the chosen vocabulary features to multiple and possible specialized contexts, we study the pertinence of unsupervised clustering to capture significant shape variations within a vocabulary class and thus refine the discriminative power of the method. This unsupervised clustering relies on cross-validation between several different cluster indices. The resulting approach is capable of determining part of the pertinent vocabulary and significantly increases recognition results with respect to the state-of-the-art. It is experimentally validated on complex electrical wiring diagram symbols

Fundamentos del análisis matemático sobre grupos localmente compactos abelianos

En este trabajo se exponen los fundamentos de la teoría abstracta de la medida de Haar, la transformada de Fourier, y la dualidad de Pontryagin sobre grupos topológicos localmente compactos abelianos. Se trata de un tema que abarca aspectos algebraicos, topológicos, de teoría de la medida y de análisis de Fourier

Spatio-structural Symbol Description with Statistical Feature Add-on

International audienceIn this paper, we present a method for symbol description based on spatio-structural as well as statistical features of visual elementary parts called 'vocabulary'. The extracted vocabulary is first organised into different groups based on their types (e.g., circle, corner). This vocabulary is used as a basis for an Attributed Relational Graph (ARG) where spatial relational descriptors formalise the links between the types, labelled with global shape descriptors. The description is used to globally recognise structure by comparing the signatures. The method is experimentally validated in the context of electrical symbol recognition from wiring diagrams

Symbol Recognition using Spatial Relations

International audienceIn this paper, we present a method for symbol recognition based on the spatio-structural description of a 'vocabulary' of extracted visual elementary parts. It is applied to symbols in electrical wiring diagrams. The method consists of first identifying vocabulary elements into different groups based on their types (e.g., circle, corner ). We then compute spatial relations between the possible pairs of labelled vocabulary types which are further used as a basis for building an Attributed Relational Graph that fully describes the symbol. These spatial relations integrate both topology and directional information. The experiments reported in this paper show that this approach, used for recognition, significantly outperforms both structural and signal-based state-of-the-art methods