Browsing by Author "Leite, Neucimar"

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    Learning how to extract rotation-invariant and scale-invariant features from texture images
    (Scopus, 2008) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    Learning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system.
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    Novel approaches for exclusive and continuous fingerprint classification
    (Scopus, 2009) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    This paper proposes novel exclusive and continuous approaches to guide the search and the retrieval in fingerprint image databases. Both approaches are useful to perform a coarse level classification of fingerprint images before fingerprint authentication tasks. Our approaches are characterized by: (1) texture image descriptors based on pairs of multi-resolution decomposition methods that encode effectively global and local fingerprint information, with similarity measures used for fingerprint matching purposes, and (2) a novel multi-class object recognition method based on the Optimum Path Forest classifier. Experiments were carried out on the standard NIST-4 dataset aiming to study the discriminative and scalability capabilities of our approaches. The high classification rates allow us demonstrate the feasibility and validity of our approaches for characterizing fingerprint images accurately. © 2009 Springer Berlin Heidelberg.
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    Rotation-invariant and scale-invariant steerable pyramid decomposition for texture image retrieval
    (Scopus, 2007) Montoya Zegarra, Javier; Leite, Neucimar; da Silva Torres, Ricardo
    This paper proposes a new rotation-invariant and scale-invariant representation for texture image retrieval based on Steerable Pyramid Decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional Steerable Pyramid Decomposition, and a recent proposal for texture characterization based on Gabor Wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets. © 2007 IEEE.
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    Rotation-invariant texture recognition
    (Scopus, 2007) Montoya Zegarra, Javier; Paulo Papa, Joao; Leite, Neucimar; da Silva Torres, Ricardo; Falcao, Alexandre
    This paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. By combining the discriminating power of our image descriptor and classifier, our system uses small size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.
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    Wavelet-based fingerprint image retrieval
    (Scopus, 2009) Montoya Zegarra, Javier; Leite, Neucimar; da Silva Torres, Ricardo
    This paper presents a novel approach for personal identification based on a wavelet-based fingerprint retrieval system which encompasses three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing. We propose the use of different types of Wavelets for representing and describing the textural information presented in fingerprint images in a compact way. For that purpose, the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the wavelet domain. These feature vectors are used both to retrieve the most similar fingerprints, given a query image, and their indexation is used to reduce the search spaces of candidate images. The different types of Wavelets used in our study include: Gabor wavelets, tree-structured wavelet decomposition using both orthogonal and bi-orthogonal filter banks, as well as the steerable wavelets. To evaluate the retrieval accuracy of the proposed approach, a total number of eight different data sets were considered. We also took into account different combinations of the above wavelets with six similarity measures. The results show that the Gabor wavelets combined with the Square Chord similarity measure achieves the best retrieval effectiveness. © 2008 Elsevier B.V. All rights reserved.