Search results for "Feature extraction"
showing 10 items of 275 documents
A new minimum spanning tree-based method for shape description and matching working in Discrete Cosine space
2009
In this article, a new minimum spanning tree-based method for shape description and matching is proposed. Its properties are checked through the problem of graphical symbols recognition. Recognition invariance in front shift and multi-oriented noisy objects was studied in the context of small and low resolution binary images. The approach seems to have many desirable properties, even if the construction of graphs induces an expensive algorithmic cost. In order to reduce time computing, an alternative solution based on image compression concepts is provided. The recognition is realized in a compact space, namely the Discrete Cosine space. The use of block discrete cosine transform is discuss…
A fast recursive algorithm for the computation of axial moments
2002
This paper describes a fast algorithm to compute local axial moments used for the detection of objects of interest in images. The basic idea is grounded on the elimination of redundant operations while computing axial moments for two neighboring angles of orientation. The main result is that the complexity of recursive computation of axial moments becomes independent of the total number of computed moments in a given point, i.e. it is of the order O(N) where N is the data size. This result is of great importance in computer vision since many feature extraction methods are based on the computation of axial moments. The experimental results confirm the time complexity and accuracy predicted b…
Use of Guided Regularized Random Forest for Biophysical Parameter Retrieval
2018
This paper introduces a feature selection method based on random forest -the Guided Regularized Random Forest (GRRF)- which can be used in classification and regression tasks. The method is based on the regularization of the information gain in the random forest nodes to obtain a subset of relevant and non-redundant features. The proposed method is used as a preliminary step In the process of retrieving biophysical parameters from a hyperspectral image. Preliminary experiments show that we can reduce the RMSE of the retrievals by around 7% for the Leaf Area Index and around 8% for the fraction of vegetation cover when compared to the results using random forest features.
Search strategies for ensemble feature selection in medical diagnostics
2003
The goal of this paper is to propose, evaluate, and compare four search strategies for ensemble feature selection, and to consider their application to medical diagnostics, with a focus on the problem of the classification of acute abdominal pain. Ensembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to get higher accuracy, sensitivity, and specificity, which are often not achievable with single models. One technique, which proved to be effective for ensemble construction, is feature selection. Lately, several strategies for ensemble feature selection were proposed, including random subspacing, hill-climbing-based se…
A fuzzy approach to the evaluation of image complexity
2009
The inherently multidimensional problem of evaluating the complexity of an image is of a certain relevance in both computer science and cognitive psychology. Computer scientists usually analyze spatial dimensions in order to deal with automatic vision problems, such as feature extraction. Psychologists seem more interested in the temporal dimension of complexity, as a means to explore attentional models. Is it possible to define, by merging both approaches, a more general index of visual complexity? The aim of this paper is the definition of objective measures of image complexity that fits with the so named perceived time. Towards the end we have defined a fuzzy mathematical model of visual…
A Novel Iris Recognition System based on Micro-Features
2007
In this paper a novel approach for iris recognition system based on iris micro-features is proposed. The proposed system follows the minutiae based approach developed for fingerprint recognition systems. The proposed system uses four iris microfeatures, considered as minutiae, for identification. The individualized characteristics are nucleus, collarette, valleys and radius. Iris recognition is divided in three main phases: image preprocessing, micro-features extraction and matching. The algorithm has been tested on CASIA v1.0 iris image database obtaining an high accuracy. The obtained experimental results have been analyzed and compared with the Daugman based approach.
Performance evaluation of simple fingerprint minutiae extraction algorithm using crossing number on valley structure
2008
In fingerprint recognition system, performance of fingerprint feature extraction algorithm is important. We use visual analysis to evaluate this performance. 100 respondents fill a questionnaire consisting of 30 images from fingerprint feature extraction process. We get 12,3 % minutiae points missed by this algorithm. With BOZORTH3 minutiae matching algorithm, the distribution of matching score of 80-fingerprint images are presented and we obtain EER 5.89 % at threshold value 180.
Image enhancement in simple fingerprint minutiae extraction algorithm using crossing number on valley structure
2007
In fingerprint recognition system, fingerprint feature extraction algorithm requires good quality fingerprint images to produce good results. Therefore, one step in the preprocessing stage is image enhancement to improve the quality of poor fingerprint image, so the minutiae points can be detected with good results. In this paper, we present how this enhancement process in simple minutiae detection algorithm using crossing number on valley structure improves detection of true minutiae.
Simple Fingerprint Minutiae Extraction Algorithm Using Crossing Number On Valley Structure
2007
Most of the existing fingerprint extraction techniques currently available are based on ridge structure. The ridge usually has thicker structure than the valley, so that more processing time is needed to extract the ridge than extracting the valley. Taking the advantage of the thin structure of the valley, we proposed an algorithm that reduces the time needed for minutiae extraction. The algorithm was developed in Matlab environment using fingerprint images from FVC2004. In order to show the performance of the algorithm, numerical results are presented.
An Advanced Technique for User Identification Using Partial Fingerprint
2013
User identification is a very interesting and complex task. Invasive biometrics is based on traits uniqueness and immutability over time. In forensic field, fingerprints have always been considered an essential element for personal recognition. The traditional issue is focused on full fingerprint images matching. In this paper an advanced technique for personal recognition based on partial fingerprint is proposed. This system is based on fingerprint local analysis and micro-features, endpoints and bifurcations, extraction. The proposed approach starts from minutiae extraction from a partial fingerprint image and ends with the final matching score between fingerprint pairs. The computation o…