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

De l'appariement de graphes symboliques à l'appariement de graphes numériques : Application à la reconnaissance de symboles

Les représentations sous forme de graphes structurels ont été appliquées dans un grand nombre de problèmes en vision par ordinateur et en reconnaissance de formes. Néanmoins, lors de l'étape d'appariement de graphes, les algorithmes classiques d'isomorphisme de graphes sont peu performants quand l'image est dégradée par du bruit ou des distorsions vectorielles. Cet article traite de la reconnaissance de symboles graphiques grâce à la formulation d'une nouvelle mesure de similarité entre leur représentation sous forme de graphes étiquetés. Dans l'approche proposée, les symboles sont d'abord décomposés en primitives structurelles et un graphe attribué est alors généré pour décrire chaque symbole. Les nœuds du graphe représentent les primitives structurelles tandis que les arcs décrivent les relations topologiques entre les primitives. L'utilisation d'attributs numériques pour caractériser les primitives et leurs relations permet d'allier précision et, invariance à la rotation et au changement d'échelle. Nous proposons également une nouvelle technique d'appariement de graphes basée sur notre fonction de similarité qui utilise les valeurs numériques des attributs pour produire un score de similarité. Cette mesure de similarité a de nombreuses propriétés intéressantes comme un fort pouvoir de discrimination, une invariance aux transformations affines et une faible sensibilité au bruit

Biometric Applications Based on Multiresolution Analysis Tools

This dissertation is dedicated to the development of new algorithms for biometric applications based on multiresolution analysis tools. Biometric is a unique, measurable characteristic of a human being that can be used to automatically recognize an individual or verify an individual\u27s identity. Biometrics can measure physiological, behavioral, physical and chemical characteristics of an individual. Physiological characteristics are based on measurements derived from direct measurement of a part of human body, such as, face, fingerprint, iris, retina etc. We focussed our investigations to fingerprint and face recognition since these two biometric modalities are used in conjunction to obtain reliable identification by various border security and law enforcement agencies. We developed an efficient and robust human face recognition algorithm for potential law enforcement applications. A generic fingerprint compression algorithm based on state of the art multiresolution analysis tool to speed up data archiving and recognition was also proposed. Finally, we put forth a new fingerprint matching algorithm by generating an efficient set of fingerprint features to minimize false matches and improve identification accuracy. Face recognition algorithms were proposed based on curvelet transform using kernel based principal component analysis and bidirectional two-dimensional principal component analysis and numerous experiments were performed using popular human face databases. Significant improvements in recognition accuracy were achieved and the proposed methods drastically outperformed conventional face recognition systems that employed linear one-dimensional principal component analysis. Compression schemes based on wave atoms decomposition were proposed and major improvements in peak signal to noise ratio were obtained in comparison to Federal Bureau of Investigation\u27s wavelet scalar quantization scheme. Improved performance was more pronounced and distinct at higher compression ratios. Finally, a fingerprint matching algorithm based on wave atoms decomposition, bidirectional two dimensional principal component analysis and extreme learning machine was proposed and noteworthy improvements in accuracy were realized

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

Consideraciones acerca de la viabilidad de un sensor plenóptico en dispositivos de consumo

Doctorado en Ingeniería IndustrialPassive distance measurement of the objects in an image gives place to interesting applications that have the potential to revolutionize the field of photography. In this thesis a prototype of plenoptic camera for mobile devices was created and studied. This technique has two main disadvantages: the need for modifying the camera module and the loss of resolution. Because of this, the prototype was discarded in order to utilize another technique: depth from focus. In this technique the capture method consists in taking several images while varying the focus distance. The set of images is called focal-stack. Different focus operators are studied, which give a measure of defocus per pixel and plane of the focal-stack. The curvelet based focus operator is chosen as the most adequate. It is computationally more intensive than other operators but it is capable of decomposing natural images using few coefficients. In order to make viable its usage in mobile devices a new curvelet transform based on the discrete Radon transform is built. The discrete Radon transform has logarithmic complexity, does not use the Fourier transform and uses only integer sums. Lastly, different versions of the Radon transform are analyzed with the goal of achieving an even faster transform. These transforms are implemented to be executed on mobile devices. Additionally, an application of the Radon transform is presented. It consists in the detection of bar-codes that have any orientation in an image.La medida pasiva de distancia a los objetos en una imagen da lugar a interesantes aplicaciones con capacidad para revolucionar la fotografía. En esta tesis se creó y estudió un prototipo de cámara plenóptica para dispositivos móviles. Esta técnica presenta dos inconvenientes: la necesidad de modificar el módulo de cámara y la pérdida de resolución. Por ello, el prototipo fue descartado para utilizar otra técnica: la profundidad a partir del desenfoque. En esta técnica el método de captura consiste en tomar varias imagenes variando la distancia de enfoque. El conjunto de imágenes se denomina focal-stack. Se estudian distintos operadores de desenfoque, que dan una medida de desenfoque por pixel y por plano del focal-stack. Siendo elegido como óptimo el operador de desenfoque curvelet, que es computacionalmente más intensivo que otros operadores pero es capaz de descomponer imagenes naturales utilizando muy pocos coeficientes. Para hacer posible su uso en dispositivos móviles se construye una nueva transformada curvelet basada en la transformada discreta de Radon. La transformada discreta de Radon tiene complejidad linearítmica, no utiliza la transformada de Fourier y usa sólo sumas de enteros. Por último, se analizan distintas versiones de la transformada de Radon con el objetivo de conseguir una transformada aún más rápida y se implementan para ser ejecutadas en dispositivos móviles. Además se presenta una aplicación de la transformada de Radon consistente en la detección de códigos de barras con cualquier orientación en una imagen

Directional multiresolution image representations

Efficient representation of visual information lies at the foundation of many image processing tasks, including compression, filtering, and feature extraction. Efficiency of a representation refers to the ability to capture significant information of an object of interest in a small description. For practical applications, this representation has to be realized by structured transforms and fast algorithms. Recently, it has become evident that commonly used separable transforms (such as wavelets) are not necessarily best suited for images. Thus, there is a strong motivation to search for more powerful schemes that can capture the intrinsic geometrical structure of pictorial information. This thesis focuses on the development of new "true" two-dimensional representations for images. The emphasis is on the discrete framework that can lead to algorithmic implementations. The first method constructs multiresolution, local and directional image expansions by using non-separable filter banks. This discrete transform is developed in connection with the continuous-space curvelet construction in harmonic analysis. As a result, the proposed transform provides an efficient representation for two-dimensional piecewise smooth signals that resemble images. The link between the developed filter banks and the continuous-space constructions is set up in a newly defined directional multiresolution analysis. The second method constructs a new family of block directional and orthonormal transforms based on the ridgelet idea, and thus offers an efficient representation for images that are smooth away from straight edges. Finally, directional multiresolution image representations are employed together with statistical modeling, leading to powerful texture models and successful image retrieval systems

Pattern detection and recognition using over-complete and sparse representations

Recent research in harmonic analysis and mammalian vision systems has revealed that over-complete and sparse representations play an important role in visual information processing. The research on applying such representations to pattern recognition and detection problems has become an interesting field of study. The main contribution of this thesis is to propose two feature extraction strategies - the global strategy and the local strategy - to make use of these representations. In the global strategy, over-complete and sparse transformations are applied to the input pattern as a whole and features are extracted in the transformed domain. This strategy has been applied to the problems of rotation invariant texture classification and script identification, using the Ridgelet transform. Experimental results have shown that better performance has been achieved when compared with Gabor multi-channel filtering method and Wavelet based methods. The local strategy is divided into two stages. The first one is to analyze the local over-complete and sparse structure, where the input 2-D patterns are divided into patches and the local over-complete and sparse structure is learned from these patches using sparse approximation techniques. The second stage concerns the application of the local over-complete and sparse structure. For an object detection problem, we propose a sparsity testing technique, where a local over-complete and sparse structure is built to give sparse representations to the text patterns and non-sparse representations to other patterns. Object detection is achieved by identifying patterns that can be sparsely represented by the learned. structure. This technique has been applied. to detect texts in scene images with a recall rate of 75.23% (about 6% improvement compared with other works) and a precision rate of 67.64% (about 12% improvement). For applications like character or shape recognition, the learned over-complete and sparse structure is combined. with a Convolutional Neural Network (CNN). A second text detection method is proposed based on such a combination to further improve (about 11% higher compared with our first method based on sparsity testing) the accuracy of text detection in scene images. Finally, this method has been applied to handwritten Farsi numeral recognition, which has obtained a 99.22% recognition rate on the CENPARMI Database and a 99.5% recognition rate on the HODA Database. Meanwhile, a SVM with gradient features achieves recognition rates of 98.98% and 99.22% on these databases respectivel

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Efficient architectures and power modelling of multiresolution analysis algorithms on FPGA

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.In the past two decades, there has been huge amount of interest in Multiresolution Analysis Algorithms (MAAs) and their applications. Processing some of their applications such as medical imaging are computationally intensive, power hungry and requires large amount of memory which cause a high demand for efficient algorithm implementation, low power architecture and acceleration. Recently, some MAAs such as Finite Ridgelet Transform (FRIT) Haar Wavelet Transform (HWT) are became very popular and they are suitable for a number of image processing applications such as detection of line singularities and contiguous edges, edge detection (useful for compression and feature detection), medical image denoising and segmentation. Efficient hardware implementation and acceleration of these algorithms particularly when addressing large problems are becoming very chal-lenging and consume lot of power which leads to a number of issues including mobility, reliability concerns. To overcome the computation problems, Field Programmable Gate Arrays (FPGAs) are the technology of choice for accelerating computationally intensive applications due to their high performance. Addressing the power issue requires optimi- sation and awareness at all level of abstractions in the design flow. The most important achievements of the work presented in this thesis are summarised here. Two factorisation methodologies for HWT which are called HWT Factorisation Method1 and (HWTFM1) and HWT Factorasation Method2 (HWTFM2) have been explored to increase number of zeros and reduce hardware resources. In addition, two novel efficient and optimised architectures for proposed methodologies based on Distributed Arithmetic (DA) principles have been proposed. The evaluation of the architectural results have shown that the proposed architectures results have reduced the arithmetics calculation (additions/subtractions) by 33% and 25% respectively compared to direct implementa-tion of HWT and outperformed existing results in place. The proposed HWTFM2 is implemented on advanced and low power FPGA devices using Handel-C language. The FPGAs implementation results have outperformed other existing results in terms of area and maximum frequency. In addition, a novel efficient architecture for Finite Radon Trans-form (FRAT) has also been proposed. The proposed architecture is integrated with the developed HWT architecture to build an optimised architecture for FRIT. Strategies such as parallelism and pipelining have been deployed at the architectural level for efficient im-plementation on different FPGA devices. The proposed FRIT architecture performance has been evaluated and the results outperformed some other existing architecture in place. Both FRAT and FRIT architectures have been implemented on FPGAs using Handel-C language. The evaluation of both architectures have shown that the obtained results out-performed existing results in place by almost 10% in terms of frequency and area. The proposed architectures are also applied on image data (256 £ 256) and their Peak Signal to Noise Ratio (PSNR) is evaluated for quality purposes. Two architectures for cyclic convolution based on systolic array using parallelism and pipelining which can be used as the main building block for the proposed FRIT architec-ture have been proposed. The first proposed architecture is a linear systolic array with pipelining process and the second architecture is a systolic array with parallel process. The second architecture reduces the number of registers by 42% compare to first architec-ture and both architectures outperformed other existing results in place. The proposed pipelined architecture has been implemented on different FPGA devices with vector size (N) 4,8,16,32 and word-length (W=8). The implementation results have shown a signifi-cant improvement and outperformed other existing results in place. Ultimately, an in-depth evaluation of a high level power macromodelling technique for design space exploration and characterisation of custom IP cores for FPGAs, called func-tional level power modelling approach have been presented. The mathematical techniques that form the basis of the proposed power modeling has been validated by a range of custom IP cores. The proposed power modelling is scalable, platform independent and compares favorably with existing approaches. A hybrid, top-down design flow paradigm integrating functional level power modelling with commercially available design tools for systematic optimisation of IP cores has also been developed. The in-depth evaluation of this tool enables us to observe the behavior of different custom IP cores in terms of power consumption and accuracy using different design methodologies and arithmetic techniques on virous FPGA platforms. Based on the results achieved, the proposed model accuracy is almost 99% true for all IP core's Dynamic Power (DP) components.Thomas Gerald Gray Charitable Trus

Geologic, Geochemical, and Geophysical Characterization of the Gold Deposits of the Horseshoe Bend Mining District, Idaho: Building a Four Dimensional Model for Ore Exploration

The Eocene aged Trans-Challis Fault System of central Idaho provides the tectonic and magmatic framework for a series of Au-Ag and Cu-Mo ore deposits. From its northernmost extension near Butte, Montana to its southwestern terminus in the Boise Basin of south-central Idaho the Trans-Challis Fault System is associated with some of the richest precious metal deposits found in Idaho. However, the southernmost tip of the Trans-Challis Fault System, composed of the Horseshoe Bend and Pearl mining districts, remains understudied, receiving little economic or academic attention. As a result, how the Pearl to Horseshoe Bend mining districts fit within the established framework of the Trans-Challis Fault System and associated mineralization is poorly characterized. Significantly, no high-resolution mapping or modern geochemical and geophysical techniques have been applied to areas within these historically productive mining districts. This study employs detailed bedrock mapping, high-precision U/Pb geochronology, high-resolution soil geochemistry, ground-based magnetic anomaly mapping, and electrical resistivity and induced polarization geophysical imaging to characterize spatial patterns to create a model for structurally controlled mineralization within the Horseshoe Bend Mining District. Integration of these datasets with knowledge gained from other studies along the Trans-Challis Fault System has led to the characterization of the structural framework hosting mineralization near Horseshoe Bend, Idaho. Geologic mapping reveals NE-SW and E-W trending dike swarms and associated en echelon mineralized vein systems oriented sub-parallel to the NE trend of the Trans-Challis Fault System. U/Pb ages on zircon grains within the dikes date emplacement during the late Early Eocene to the Early Oligocene. Surficial geochemistry surveys reveal east-west oriented, en echelon, zones of anomalously high gold concentrations with subordinate north-south oriented arms. Magnetic anomaly mapping reveals lineaments of sharp magnetic gradients spatially correlated with mapped dike patterns, as well as zones of magnetic lows spatially correlated with surface geochemical gold concentration anomalies. Electrical resistivity and induced polarization subsurface imaging techniques outline a series of east-west oriented, northeast stepping, conductivity, chargeability, and metal factor highs that correlate with a similarly oriented magnetic anomaly over the survey area, and en echelon mineralized vein systems mapped in adjacent bedrock. The Early Oligocene age of the andesite dike phase reported to follow mineralization either extends the duration, or changes the timing, of the mineralizing events associated with this section of the Trans-Challis Fault System. Mapping, geochemical and geophysical data strongly suggest the controlling factor in mineralization location and geometry is the underlying structural framework of the system. Based on these geometries and orientations, a dextral Riedel shear array oriented 070° is proposed to adequately model the structural architecture controlling mineralization within the Horseshoe Bend Mining District