Design principles of columnar organization in visual cortex

Visual space is represented by cortical cells in an orderly manner. Only little variation in the cell behavior is found with changing depth below the cortical surface, that is, all cells in a column with axis perpendicular to the cortical plane have approximately the same properties (Hubel and Wiesel 1962, 1963, 1968). Therefore, the multiple features of the visual space (e.g., position in visual space, preferred orientation, and orientation tuning strength) are mapped on a two-dimensional space, the cortical plane. Such a dimension reduction leads to complex maps (Durbin and Mitchison 1990) that so far have evaded an intuitive understanding. Analyzing optical imaging data (Blasdel 1992a, b; Blasdel and Salama 1986; Grinvald et al. 1986) using a theoretical approach we will show that the most salient features of these maps can be understood from a few basic design principles: local correlation, modularity, isotropy, and homogeneity. These principles can be defined in a mathematically exact sense in the Fourier domain by a rather simple annulus-like spectral structure. Many of the models that have been developed to explain the mapping of the preferred orientations (Cooper et al. 1979; Legendy 1978; Linsker 1986a, b; Miller 1992; Nass and Cooper 1975; Obermayer et al. 1990, 1992; Soodak 1987; Swindale 1982, 1985, 1992; von der Malsburg 1973; von der Malsburg and Cowan 1982) are quite successful in generating maps that are close to experimental maps. We suggest that this success is due to these principles, which are common properties of the models and of biological maps

Polarization singularities in the clear sky

Ideas from singularity theory provide a simple account of the pattern of polarization directions in daylight. The singularities (two near the Sun and two near the anti-Sun) are points in the sky where the polarization line pattern has index +1/2 and the intensity of polarization is zero. The singularities are caused by multiple scattering that splits into two each of the unstable index +1 singularities at the Sun and anti-Sun, which occur in the single-dipole scattering (Rayleigh) theory. The polarization lines are contours of an elliptic integral. For the intensity of polarization (unnormalized degree), it is necessary to incorporate the strong depolarizing effect of multiple scattering near the horizon. Singularity theory is compared with new digital images of sky polarization, and gives an excellent description of the pattern of polarization directions. For the intensity of polarization, the theory can reproduce not only the zeros but also subtle variations in the polarization maxima

Characteristic and necessary minutiae in fingerprints

Fingerprints feature a ridge pattern with moderately varying ridge frequency (RF), following an orientation field (OF), which usually features some singularities. Additionally at some points, called minutiae, ridge lines end or fork and this point pattern is usually used for fingerprint identification and authentication. Whenever the OF features divergent ridge lines (e.g., near singularities), a nearly constant RF necessitates the generation of more ridge lines, originating at minutiae. We call these the necessary minutiae. It turns out that fingerprints feature additional minutiae which occur at rather arbitrary locations. We call these the random minutiae or, since they may convey fingerprint individuality beyond the OF, the characteristic minutiae. In consequence, the minutiae point pattern is assumed to be a realization of the superposition of two stochastic point processes: a Strauss point process (whose activity function is given by the divergence field) with an additional hard core, and a homogeneous Poisson point process, modelling the necessary and the characteristic minutiae, respectively. We perform Bayesian inference using an Markov-Chain-Monte-Carlo (MCMC)-based minutiae separating algorithm (MiSeal). In simulations, it provides good mixing and good estimation of underlying parameters. In application to fingerprints, we can separate the two minutiae patterns and verify by example of two different prints with similar OF that characteristic minutiae convey fingerprint individuality

An automatic fingerprint classification technique based on global features

Fingerprint classification is an important stage in automatic fingerprint identification system (AFIS) because it significantly reduces the processing time to search and retrieve in a large-scale fingerprint database. However, its performance is heavily relied on image quality that comes in various forms such as low contrast, wet, dry, bruise, cuts, stains, etc. This paper proposed an automatic fingerprint classification scheme based on singular points and structural shape of orientation fields. It involves several steps, amongst others: firstly, fingerprint foreground is extracted and then noise patches in the foreground are detected and enhanced. Next, the orientation fields are estimated, and a corrective procedure is performed on the false ones. Afterward, an orientation image is created and singular points are detected. Based on the number of core and delta and their locations, an exclusive membership of the fingerprint can be discovered. Should it fail, the structural shape of the orientation fields neighboring the core or delta is analyzed. The performance of the proposed method is tested using 27,000 fingerprints of NIST Special Database 14. The results obtained are very encouraging with an accuracy rate of 89.31% that markedly outperformed the latest work

Characteristic and necessary minutiae in fingerprints

Fingerabdrücke sind Abbilder der Papillarlinien, welche ein ungerichtetes Orientierungsfeld (OF) induzieren. Dieses weist in der Regel einige Singularitäten auf. Die Linien variieren in ihrer Breite und induzieren so eine mäßig variierende Linienfrequenz (LF). Bei der Fingerabdruckserkennung wird ein Fingerabdruck üblicherweise auf ein Punktmuster reduziert, das aus Minutien besteht, das sind Punkte, an denen die Papillarlinien enden oder sich verzweigen. Geometrisch können Minutien durch divergierende Papillarlinien bei nahezu konstanter LF oder bei nahezu parallelen Linien durch Verbreiterung der Zwischenräume entstehen, in welchen neue Linien entstehen, welche in Minutien entspringen (und natürlich Kombinationen aus beiden Effekten). Wir nennen diese die geometrisch notwendigen Minutien. In dieser Arbeit stellen wir ein mathematisches Rahmenkonzept basierend auf Vektorfeldern bereit, in dem Orientierungsfelder, Linienfrequenz sowie die Anzahl der geometrisch notwendigen Minutien mathematisch konkret und leicht mit den bereitgestellten Algorithmen und dazugehöriger Software berechenbar werden. Es stellt sich heraus, dass echte Fingerabdrücke zusätzliche Minutien aufweisen, die an recht zufälligen Stellen auftreten. Wir nennen diese die zufälligen Minutien oder, da sie zur Fingerabdrucksindividualität über OF und LF hinaus beitragen können, die charakteristischen Minutien. In der Folge wird angenommen, dass ein Minutien-Punktmuster eine Realisierung der Überlagerung zweier stochastischer Punktprozesse ist: einem Strauss-Punktprozess (dessen Aktivitätsfunktion durch das Divergenzfeld gegeben ist) mit einem zusätzlichen Hard-core und einem homogenen Poisson-Punktprozess, welche die notwendigen bzw. die charakteristischen Minutien modellieren. Für ein gegebenes Minutienmuster streben wir nach einer Methode, die sowohl die Separation der Minutien als auch Inferenz für die Modellparameter ermöglicht. Wir betrachten das Problem aus zwei Perspektiven. Aus frequentistischer Sicht betrachten wir zunächst lediglich die Schätzung der Modellparameter (ohne Trennung der Prozesse). Dazu legen wir die Grundlagen für parametrische Inferenz, indem wir die Dichte des überlagerten Prozesses herleiten und ein Identifizierbarkeitsergebnis liefern. Wir schlagen einen Ansatz zur Berechnung eines Maximum-Pseudolikelihood-Schätzers vor und zeigen Vor- und Nachteile dieses Schätzers für echte und simulierte Daten auf. Einem Bayesianischen Ansatz folgend, schlagen wir einen MCMC-basierten Minutien-Separationsalgorithmus (MiSeal) vor, der es ermöglicht, die zugrunde liegenden Modellparameter sowie die Posterior-Wahrscheinlichkeiten von Minutien charakteristisch zu sein zu schätzen. Für zwei verschiedene Fingerabdrücke mit ähnlichen OF und LF weisen wir empirisch nach, dass die charakteristischen Minutien tatsächlich individuelle Fingerabdrucksinformation beinhalten.Fingerprints feature a ridge line pattern inducing an undirected orientation field (OF) which usually features some singularities. Ridges vary in width, inducing a moderately varying ridge frequency (RF). In fingerprint recognition, a fingerprint is usually reduced to a point pattern consisting of minutiae, i.e. points where the ridge lines end or fork. Geometrically, minutiae can occur due to diverging ridge lines with a nearly constant RF or by widening of parallel ridges making space for new ridge lines originating at minutiae (and, indeed, combinations of both). We call these the geometrically necessary minutiae. In this thesis, we provide a mathematical framework based on vector fields in which orientation fields, ridge frequency as well as the number of geometrically necessary minutiae become tangible and easily computable using the provided algorithms and software. It turns out that fingerprints feature additional minutiae which occur at rather arbitrary locations. We call these the random minutiae, or, since they may convey fingerprint individuality beyond OF and RF, the characteristic minutiae. In consequence, a minutiae point pattern is assumed to be a realization of the superposition of two stochastic point processes: a Strauss point process (whose activity function is given by the divergence field) with an additional hard core, and a homogeneous Poisson point process, modelling the necessary and the characteristic minutiae, respectively. Given a minutiae pattern we strive for a method allowing for separation of minutiae and inference for the model parameters and consider the problem from two view points. From a frequentist point of view we first solely aim on estimating the model parameters (without separating the processes). To this end, we lay the foundations for parametric inference by deriving the density of the superimposed process and provide an identifiability result. We propose an approach for the computation of a maximum pseudolikelihood estimator and highlight benefits and drawbacks of this estimator on real and simulated data. Following a Bayesian approach we propose an MCMC-based minutiae separating algorithm (MiSeal) which allows for estimation of the underlying model parameters as well as of the posterior probabilities of minutiae being characteristic. In a proof of concept, we provide evidence that for two different prints with similar OF and RF the characteristic minutiae convey fingerprint individuality.2021-10-2

A Review of Wavelet Based Fingerprint Image Retrieval

A digital image is composed of pixels and information about brightness of image and RGB triples are used to encode color information. Image retrieval problem encountered when searching and retrieving images that is relevant to a user’s request from a database. In Content based image retrieval, input goes in the form of an image. In these images, different features are extracted and then the other images from database are retrieved accordingly. Biometric distinguishes the people by their physical or behavioral qualities. Fingerprints are viewed as a standout amongst the most solid for human distinguishment because of their uniqueness and ingenuity. To retrieve fingerprint images on the basis of their textural features,by using different wavelets. From the input fingerprint image, first of all center point area is selected and then its textural features are extracted and stored in database. When a query image comes then again its center point is selected and then its texture feature are extracted. Then these features are matched for similarity and then resultant image is displayed. DOI: 10.17762/ijritcc2321-8169.15026