Enhancing retinal images by nonlinear registration

Being able to image the human retina in high resolution opens a new era in many important fields, such as pharmacological research for retinal diseases, researches in human cognition, nervous system, metabolism and blood stream, to name a few. In this paper, we propose to share the knowledge acquired in the fields of optics and imaging in solar astrophysics in order to improve the retinal imaging at very high spatial resolution in the perspective to perform a medical diagnosis. The main purpose would be to assist health care practitioners by enhancing retinal images and detect abnormal features. We apply a nonlinear registration method using local correlation tracking to increase the field of view and follow structure evolutions using correlation techniques borrowed from solar astronomy technique expertise. Another purpose is to define the tracer of movements after analyzing local correlations to follow the proper motions of an image from one moment to another, such as changes in optical flows that would be of high interest in a medical diagnosis.Comment: 21 pages, 7 figures, submitted to Optics Communication

Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity

Projection-based image registration in the presence of fixed-pattern noise

A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator. While gradient-based shift estimation techniques are computationally efficient, they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise. The projection-based estimator, on the other hand, significantly reduces the computational complexity associated with the 2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise. To show the noise rejection capability of the projection-based shift estimator relative to the 2-D cross correlator, a figure-of-merit is developed and computed reflecting the signal-to-noise ratio (SNR) associated with each estimator. The two methods are also compared by means of computer simulation and tests using real image sequences

Photo repair and 3d structure from flatbed scanners

We introduce a technique that allows 3D information to be captured from a conventional flatbed scanner. The technique requires no hardware modification and allows untrained users to easily capture 3D datasets. Once captured, these datasets can be used for interactive relighting and enhancement of surface detail on physical objects. We have also found that the method can be used to scan and repair damaged photographs. Since the only 3D structure on these photographs will typically be surface tears and creases, our method provides an accurate procedure for automatically detecting these flaws without any user intervention. Once detected, automatic techniques, such as infilling and texture synthesis, can be leveraged to seamlessly repair such damaged areas. We first present a method that is able to repair damaged photographs with minimal user interaction and then show how we can achieve similar results using a fully automatic process

Visible and Infrared Image Registration Employing Line-Based Geometric Analysis

Abstract. We present a new method to register a pair of visible (ViS) and infrared (IR) images. Unlike most of existing systems that align interest points of two images, we align lines derived from edge pixels, because the interest points extracted from both images are not always identical, but most major edges detected from one image do appear in another image. To solve feature matching problem, we emphasize the geometric structure alignment of features (lines), instead of descriptor-based individual feature matching. This is due to the fact that image properties and patch statistics of corresponding features might be quite different, especially when one compares ViS image with long wave IR images (thermal information). However, the spatial layout of features for both images always preserves consistency. The last step of our algorithm is to compute the image transform matrix, given minimum 4 pairs of line correspondence. The comparative evaluation for algorithms demon-strates higher accuracy attained by our method when compared to the state-of-the-art approaches.

Visible and Infrared Image Registration Employing Line-Based Geometric Analysis

We present a new method to register a pair of visible (ViS) and infrared (IR) images. Unlike most of existing systems that align interest points of two images, we align lines derived from edge pixels, because the interest points extracted from both images are not always identical, but most major edges detected from one image do appear in another image. To solve feature matching problem, we emphasize the geometric structure alignment of features (lines), instead of descriptor-based individual feature matching. This is due to the fact that image properties and patch statistics of corresponding features might be quite different, especially when one compares ViS image with long wave IR images (thermal information). However, the spatial layout of features for both images always preserves consistency. The last step of our algorithm is to compute the image transform matrix, given minimum 4 pairs of line correspondence. The comparative evaluation for algorithms demonstrates higher accuracy attained by our method when compared to the state-of-the-art approaches

A personal identification biometric system based on back-of-hand vein patterns

This report describes research on the use of back-of-hand vein patterns as a means of uniquely identifying people. In particular it describes a prototype biometric system developed by the Australian Institute of Security and Applied Technology (AISAT). This system comprises an infrared cold source, a monochrome CCD camera, a monochrome frame-grabber, a personal computer, and custom image acquisition, processing, registration, and matching software. The image processing algorithms are based on Mathematical Morphology. Registration is performed using rotation and translation with respect to the centroid of the two-dimensional domain of a hand. Vein patterns are stored as medial axis representations. Matching involves comparing a given medial axis pattern against a library of patterns using constrained sequential correlation. The matching is two-fold: a newly acquired signature is matched against a dilated library signature, and then the library signature is matched against the dilated acquired signature; this is necessary because of the positional noise exhibited by the back-of-hand veins. The results of a cross-matching experiment for a sample of 20 adults and more than 100 hand images is detailed. In addition preliminary estimates of the false acceptance rate (FAR) and false rejection rate (FRR) for the prototype system are given. Fuzzy relaxation on an association graph is discussed as an alternative to sequential correlation for the matching of vein signatures. An example is provided (including a C program) illustrating the matching process for a pair of signatures obtained from the same hand. The example demonstrates the ability of the fuzzy relaxation method to deal with segmentation errors

A pilot study on discriminative power of features of superficial venous pattern in the hand

The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest