Marta Penas

Localization and extraction of the optic disc using the fuzzy circular hough transform

This paper presents an algorithm for automatic extraction of the optic disc in retinal images. The developed system consists of two main parts. Firstly, the localization of the region containing the optic disc is performed by means of a clustering algorithm. Then, in order to extract the optic disc, the fuzzy circular Hough transform is applied to the edges of the region. The optic disc might not be extracted since there are vessels in the inside of the optic disc. To avoid this, a crease extraction algorithm is applied to the retinal image. The vessels are extracted and the vessel edge points contained in the edge image are removed. The final system was tested by ophthalmologists. The localization of the region of interest is correct in 100% of the cases and the extraction of the optic disc is obtained in 98% of the cases.

Perceptual Primitives from an Extended 4D Hough Transform

Abstract Directional features extracted from Gabor wavelets responses were used to train a structure of self-organising maps, thus classifying each pixel in the image within a neuron-map. Resulting directional primitives were grouped into perceptual primitives introducing an extended 4D Hough transform to group pixels with similar directional features. These can then be used as perceptual primitives to detect salient structures. The proposed method has independently fixed parameters that do not need to be tuned for different kind or quality of images. We present results in application to noisy FLIR images and show that line primitives for complex structures, such as bridges, or simple structures, such as runways, can be found by this approach. We compare and demonstrate the quality of our results with those obtained through a parameter-dependent traditional Canny edge detector and Hough line finding process.

Autoorganised Structures for Extraction of Perceptual Primitives

In this work we have used directional features extracted form Gabor wavelet responses to compare different auto-organised networks in order to extract perceptual primitives without taking into account the kind of images to analyse. This is an adequate problem to prove the performance of these models because of the high dimensionality of the input space. Three different models have been analysed: self-organised maps, growing-cell structures and growing neural gas. Results have proved that growing-cell structures generalise better all directional perceptual primitives we are searching for, and they do not provide very noisy images.

A Color-Based Interest Operator

In this paper we propose a novel interest operator robust to photometric and geometric transformations. Our operator is closely related to the grayscale MSER but it works on the HSV color space, as opposed to the most popular operators in the literature, which are intensity based. It combines a fine and a coarse overlapped quantization of the HSV color space to find maximally stable extremal regions on each of its components and combine them into a final set of regions that are useful in images where intensity does not discriminate well. We evaluate the performance of our operator on two different applications: wide-baseline stereo matching and image annotation.

Retinal based authentication via distributed web application

Traditional authentication systems, employed to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). But emerging technologies allow for more reliable and comfortable for the user, authentication methods, most of them based on biometric parameters. Much work could be found in literature about biometric based authentication, using parameters like iris, voice, fingerprints, face characteristics, and others. We have developed a new methodology for personal authentication, where the biometric parameter employed for the authentication is the retinal vessel tree, acquired through a retinal angiography. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary in his life, so it is a very well suited identification characteristic. In this work we will present the design and implementation stages of an application which allows for a reliable personal authentication in high security environments based on the retinal authentication method.

Perceptual organization of directional primitives using a pseudocolor hough transform

This paper describes a computational framework developed for the extraction of low-level directional primitives present in an image, and subsequent organization using the laws of perceptual grouping. The system is divided in three stages. Extraction of the directional features in the image, through an efficient implementation of Gabor wavelet decomposition. Reduction of these high dimensionality results by means of growing cell structures. And extraction of the segments from the image by means of a Fuzzy Hough Transform.

Gabor Wavelets and Auto-organised Structures for Directional Primitive Extraction

This paper describes a computational framework developed for the extraction of low-level directional primitives present in an image, and subsequent organization using the laws of perceptual grouping. The system is divided in two stages. The first one consists on the extraction of the direction of pixels in the image, through an efficient implementation of Gabor wavelet decomposition. The second one consists on the reduction of these high dimensionality results by means of an auto-organized structure. For this second stage, three different auto-organized structures have been studied: self-organized maps (SOM), growing cell structures (GCS) and growing neural gas (GNG). Results have showed that GCS is the most appropriate structure in the context of this work.

A Neural Network Based Framework for Directional Primitive Extraction

This paper describes a computational framework for the extraction of low-level directional primitives in images. The system is divided in two stages. The first one consists of the low level directional primitive extraction, through the Gabor wavelet decomposition. The second one consists of the reduction of the high dimensionality of the Gabor decomposition results by means of auto-organised structures. The main advantages of the system introduced are two: it provides accurate and reliable information, and it produces good results on different image types without intervention of the final user. These advantages will be demonstrated by comparing our system with a classical edge detector.

Certainty measure of pairwise line segment perceptual relations using fuzzy logic

Perceptual grouping is an important part of many computer vision systems. When inferring a new grouping from the primitive features there is always an uncertainty degree on this detection, that might be useful in further reasonings. In this paper, we present a fuzzy logic based system for the computation of the certainties assigned to pairwise line segment relations and introduce its application to the detection of continuity, identity, junction, L-junction, incidence, T-junction and parallelism relations. The results presented show that the proposed method might be very promising for future applications.

Comparison of alternative frameworks for directional primitive extraction

This paper describes and compares three alternative computational frameworks for the extraction of the directional primitives present in an image. These frameworks are based on Gabor decomposition, Sobel filtering, and filtering through the first derivative of a Gaussian filter, respectively. They are divided into two stages: low-level primitive extraction and organization of low-level primitives by means of dynamical neural networks. These alternative frameworks are compared and their relative advantages and disadvantages are outlined.