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In this paper we present pre-processing steps and a voting scheme that improve the effectiveness of the spectroface approach. It consists on a series of pre-processing steps prior to spectroface together with a texture feature that are used independently. The classifier output for each of the 13 features is fused using a majority voting scheme coupled with rules for ties and strong features. Yale...
This paper presents an extensive evaluation of reservoir computing for the case of classification problems that do not depend on time.We discuss how it is possible to adapt the reservoir approach to learning for the case of static classification problems. Then we present a set of experiments against K-PLS, MLP with entropic cost function and LS-SVM showing that this approach is quite competitive and...
A new learning principle was introduced recently called the Zero-Error Density Maximization (Z-EDM) and was proposed in the framework of MLP backpropagation. In this paper we present the adaptation of this principle to online learning in recurrent neural networks, more precisely, to the Real Time Recurrent Learning (RTRL) approach. We show how to modify the RTRL learning algorithm in order to make...
This paper presents a comparison of texture based and color and position based methods for polyp detection in endoscopic video images. Two methods for texture feature extraction that presented good results in previous studies were implemented and their performance is compared against a simple combination of color and position features. Although this more simple approach produces a much higher number...
Textual data holds a number of properties that can be taken into account in order to improve compression. Pre-processing deals with these properties by applying a number of transformations that make the redundancy "more visible" to the compressor. One of the most commonly used concepts in text pre-processing is called capital conversion. Words with capital letters are converted to their...
Hierarchical clustering is a stepwise clustering method usually based on proximity measures between objects or sets of objects from a given data set. The most common proximity measures are distance measures. The derived proximity matrices can be used to build graphs, which provide the basic structure for some clustering methods. We present here a new proximity matrix based on an entropic measure and...
Continuous efforts have been made in searching for robust and effective iris coding methods, since Daugman's pioneering work on iris recognition was published. However, due to lack of robustness, the error rates of iris recognition systems significantly increase when images contain large portions of noise (reflections and iris obstructions), resultant from less constrained imaging conditions. Current...
This paper gives an overview of the NICE.I : Noisy Iris Challenge Evaluation -Part I contest. This contest differs from others in two fundamental points. First, instead of the complete iris recognition process, it exclusively evaluates the iris segmentation and noise detection stages, allowing the independent evaluation of one of the main recognition error sources. Second, it operates on highly noisy...
In this paper we address some open questions on the recently proposed Zero-Error Density Maximization algorithm for MLP training. We propose a new version of the cost function that solves a training problem encountered in previous work and prove that the use of a nonparametric density estimator preserves the optimal solution. Some experiments are reported comparing this cost function to the usual...
Iris recognition has been used for several purposes. However, current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, specially the false rejections, in these conditions. Several proposals have been made to access image quality and to identify noisy regions in iris images. In this paper we propose a method that measures the quality of each...
In this paper we propose a new method for the identification of noisy regions in normalized iris images. Starting from a normalized and dimensionless iris image in the polar coordinate system, our goal consists in the classification of every pixel as "noise" or "not noise". This classification could be helpful in the posterior feature extraction or feature comparison stages regarding...
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