Self-Similar Anisotropic Texture Analysis: the Hyperbolic Wavelet Transform Contribution

Textures in images can often be well modeled using self-similar processes while they may at the same time display anisotropy. The present contribution thus aims at studying jointly selfsimilarity and anisotropy by focusing on a specific classical class of Gaussian anisotropic selfsimilar processes. It will first be shown that accurate joint estimates of the anisotropy and selfsimilarity parameters are performed by replacing the standard 2D-discrete wavelet transform by the hyperbolic wavelet transform, which permits the use of different dilation factors along the horizontal and vertical axis. Defining anisotropy requires a reference direction that needs not a priori match the horizontal and vertical axes according to which the images are digitized, this discrepancy defines a rotation angle. Second, we show that this rotation angle can be jointly estimated. Third, a non parametric bootstrap based procedure is described, that provides confidence interval in addition to the estimates themselves and enables to construct an isotropy test procedure, that can be applied to a single texture image. Fourth, the robustness and versatility of the proposed analysis is illustrated by being applied to a large variety of different isotropic and anisotropic self-similar fields. As an illustration, we show that a true anisotropy built-in self-similarity can be disentangled from an isotropic self-similarity to which an anisotropic trend has been superimposed

A gradient-based weighted averaging method for estimation of fingerprint orientation fields

Estimation of orientation fields is an essential module in a fingerprint recognition system. Conventional gradient based approaches are popular but very sensitive to noise. In this paper, we propose a new implementation that is more resistant to noise. Our basic idea is to conduct redundant estimation over four overlapping neighborhoods for each target block. Following this idea, we devise a weighted averaging scheme operated on the base blocks directly. Thus, each block (including the target one) in the overlapping neighborhoods has different impact on estimation of the dominant orientation fields. Our preliminary experiment results suggest that the proposed weighted averaging algorithm is more robust against noise in comparison with other gradient based methods

FLAG : the fault-line analytic graph and fingerprint classification

Fingerprints can be classified into millions of groups by quantitative measurements of their new representations - Fault-Line Analytic Graphs (FLAG), which describe the relationship between ridge flows and singular points. This new model is highly mathematical, therefore, human interpretation can be reduced to a minimum and the time of identification can be significantly reduced. There are some well known features on fingerprints such as singular points, cores and deltas, which are global features which characterize the fingerprint pattern class, and minutiae which are the local features which characterize an individual fingerprint image. Singular points are more important than minutiae when classifying fingerprints because the geometric relationship among the singular points decide the type of fingerprints. When the number of fingerprint records becomes large, the current methods need to compare a large number of fingerprint candidates to identify a given fingerprint. This is the result of having a few synthetic types to classify a database with millions of fingerprints. It has been difficult to enlarge the minter of classification groups because there was no computational method to systematically describe the geometric relationship among singular points and ridge flows. In order to define a more efficient classification method, this dissertation also provides a systematic approach to detect singular points with almost pinpoint precision of 2x2 pixels using efficient algorithms

A survey of fingerprint classification Part II: experimental analysis and ensemble proposal

In the first part of this paper we reviewed the fingerprint classification literature from two different perspectives: the feature extraction and the classifier learning. Aiming at answering the question of which among the reviewed methods would perform better in a real implementation we end up in a discussion which showed the difficulty in answering this question. No previous comparison exists in the literature and comparisons among papers are done with different experimental frameworks. Moreover, the difficulty in implementing published methods was stated due to the lack of details in their description, parameters and the fact that no source code is shared. For this reason, in this paper we will go through a deep experimental study following the proposed double perspective. In order to do so, we have carefully implemented some of the most relevant feature extraction methods according to the explanations found in the corresponding papers and we have tested their performance with different classifiers, including those specific proposals made by the authors. Our aim is to develop an objective experimental study in a common framework, which has not been done before and which can serve as a baseline for future works on the topic. This way, we will not only test their quality, but their reusability by other researchers and will be able to indicate which proposals could be considered for future developments. Furthermore, we will show that combining different feature extraction models in an ensemble can lead to a superior performance, significantly increasing the results obtained by individual models.This work was supported by the Research Projects CAB(CDTI), TIN2011-28488, and TIN2013-40765-P

A method to guide local physical adaptations in a robot based on phase portraits

In this paper, we propose a method that shows how phase portraits rendered by a controller can inform the development of a physical adaptation at a single degree of freedom (DoF) for a given control task. This approach has the advantage of having physical adaptations sharing the responsibility of control to accomplish a task. We use an inverted pendulum which is reminiscent of the trunk of a biped walker to conduct numerical simulations and hardware experiments to show how our method can innovate a physical adaptation at the pivot joint to reduce the control effort. Our method discovered that a torsional spring at the pivot joint would lead to a lower input effort by the regulator type feedback controller. The method can tune the spring to minimize the total cost of control up to about 32.81%. This physical adaptation framework allows multiple degrees of freedom robotic system to suggest local physical adaptations to accomplish a given control objective

Non Uniform Multiresolution Method for Optical Flow and Phase Portrait Models: Environmental Applications

Projet AIRIn this paper we define a complete framework for processing large image sequences for a global monitoring of short range oceanographic and atmospheric processes. This framework is based on the use of a non quadratic regularization technique in optical flow computation that preserves flow discontinuities. We also show that using an appropriate tessellation of the image according to an estimate of the motion field can improve optical flow accuracy and yields more reliable flows. This method defines a non uniform multiresolution approach for coarse to fine grid generation. It allows to locally increase the resolution of the grid according to the studied problem. Each added node refines the grid in a region of interest and increases the numerical accuracy of the solution in this region. We make use of such a method for solving the optical flow equation with a non quadratic regularization scheme allowing the computation of optical flow field while preserving its discontinuities. The second part of the paper deals with the interpretation of the obtained displacement field. We make use of a phase portrait model with a new formulation of the approximation of an oriented flow field allowing to consider arbitrary polynomial phase portrait models for characterizing salient flow features. This new framework is used for processing oceanographic and atmospheric image sequences and presents an alternative to complex physical modeling techniques

Quantifying the Frequency and Orientation of Mitoses in Embryonic Epithelia

The miraculous birth of a new life starts by the formation of an embryo. The process by which an embryo is formed, embryogenesis, has been studied and shown to consist of three types of processes: mitosis, cell differentiation and morphogenetic movements. Scientists and medical doctors are still at a loss to explain the fundamental forces driving embryo development and the causes of birth defects remain largely unknown. Recent efforts by the Embryo Biomechanics Lab at the University of Waterloo have shown a relationship between morphogenetic movements that occur during embryo formation and the frequency and orientation of mitosis. To further study this relationship a means of automatically identifying the frequency and orientation of mitosis on time-lapse images of embryo epithelia is needed. Past efforts at identifying mitosis have been limited to the study of cell cultures and stained tissue segments. Two methods for identifying mitosis in contiguous sheets of cells are developed. The first method is based on local motion analysis and the second method is based on intensity analysis. These algorithms were tested on images of early and late stage embryos of the axolotl (Ambystoma mexicanum), a type of amphibian. The performance of the algorithms were measured using the F-Measure. The F-Measure determines the performance of the algorithm as the true mitosis detection rate penalized by the false mitosis detection rate. The motion based algorithm had performance rates of 68.2% on an early stage image set and 66.7% on a late stage image set, whereas the intensity based algorithm had a performance rates of 73.9% on early stage image set and 90.0% on late stage image set. The mitosis orientation errors for the motion based algorithm were 27.3 degrees average error with a standard deviation (std.) of 19.8 degrees for early stage set and 34.8 degrees average error with a std. of 23.5 degrees for the late stage set. For the intensity based algorithm the orientation errors were 39.8 degrees average with std. of 28.9 degrees for the early stage image set and 15.7 degrees average with std. of 18.9 degrees for the late stage image set. The intensity based algorithm had the best performance of the two algorithms presented, and the intensity based algorithm performs best on high-magnification images. Its performance is limited by mitoses in adjacent cells and by the presence of natural cell pigment variations. The algorithms presented here offer a powerful new set of tools for evaluating the role of mitoses in embryo morphogenesis

Ridge orientation modeling and feature analysis for fingerprint identification

This thesis systematically derives an innovative approach, called FOMFE, for fingerprint ridge orientation modeling based on 2D Fourier expansions, and explores possible applications of FOMFE to various aspects of a fingerprint identification system. Compared with existing proposals, FOMFE does not require prior knowledge of the landmark singular points (SP) at any stage of the modeling process. This salient feature makes it immune from false SP detections and robust in terms of modeling ridge topology patterns from different typological classes. The thesis provides the motivation of this work, thoroughly reviews the relevant literature, and carefully lays out the theoretical basis of the proposed modeling approach. This is followed by a detailed exposition of how FOMFE can benefit fingerprint feature analysis including ridge orientation estimation, singularity analysis, global feature characterization for a wide variety of fingerprint categories, and partial fingerprint identification. The proposed methods are based on the insightful use of theory from areas such as Fourier analysis of nonlinear dynamic systems, analytical operators from differential calculus in vector fields, and fluid dynamics. The thesis has conducted extensive experimental evaluation of the proposed methods on benchmark data sets, and drawn conclusions about strengths and limitations of these new techniques in comparison with state-of-the-art approaches. FOMFE and the resulting model-based methods can significantly improve the computational efficiency and reliability of fingerprint identification systems, which is important for indexing and matching fingerprints at a large scale