Juan Villegas Cortez | Universidad Autónoma Metropolitana-Azcapotzalco (original) (raw)

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Papers by Juan Villegas Cortez

An Unsupervised Font clustering technique is proposed in this work. The new approach is based on ... more An Unsupervised Font clustering technique is proposed in this work. The new approach is based on global texture analysis, using high order statistic features, Gaussian classifier and a stochastic version of the EM algorithm. The font recognition is performed by taking the document as a simple image, where one or several types of fonts are present. The identification is not performed letter by letter as with conventional approaches. In the proposed method a window analysis is employed to obtain the features of the document, using fourth and third order moments. The new technique does not involve a study of local typography; therefore, it is content independent. A detailed study was performed with 8 types of fonts commonly used in the Spanish language. Each type of font can have four styles that lead, to 32 font combinations. The font recognition with clean images is 100% accurate.

Associative Memories (AMs) are mathematical structures specially designed to associate input patt... more Associative Memories (AMs) are mathematical structures specially designed to associate input patterns with output patterns within a single stage. Since the last fifty years all reported AMs have been manually designed. The paper describes a Genetic Programming based methodology able to create a process for the automatic synthesis of AMs. It paves a new area of research that permits for the first time to propose new AMs for solving specific problems. In order to test our methodology we study the application of AMs for real value patterns. The results illustrate that it is possible to automatically generate AMs that achieve good recall performance for problems commonly used in pattern recognition research.

Grigori Sidorov (Ed.) 74 Modelado computacional de habilidades lingüísticas y visuales 74 74 www.... more Grigori Sidorov (Ed.) 74 Modelado computacional de habilidades lingüísticas y visuales 74 74 www.rcs.cic.ipn.mx | | ---

Feature extraction is a key issue in Content Based Image Retrieval (CBIR). In the past, a number ... more Feature extraction is a key issue in Content Based Image Retrieval (CBIR). In the past, a number of describing features have been proposed in literature for this goal. In this work a feature extraction and classification methodology for the retrieval of natural images is described. The proposal combines fixed and random extracted points for feature extraction. The describing features are the mean, the standard deviation and the homogeneity (form the co-occurrence) of a sub-image extracted from the three channels: H, S and I. A K-MEANS algorithm and a 1-NN classifier are used to build an indexed database of 300 images. One of the advantages of the proposal is that we do not need to manually label the images for their retrieval. After performing our experimental results, we have observed that in average image retrieval using images not belonging to the training set is of 80.71% of accuracy. A comparison with two similar works is also presented. We show that our proposal performs better in both cases.

Page 1. Unsupervised Image Retrieval with Similar Lighting Conditions J. Félix Serrano1, Carlos A... more Page 1. Unsupervised Image Retrieval with Similar Lighting Conditions J. Félix Serrano1, Carlos Avilés2, Humberto Sossa3, Juan Villegas4, Gustavo Olague5. 1, 3 Centro de Investigación en Computación Instituto Politécnico Nacional (CIC-IPN) Av. ...

A robust Font Recognition (OFR) is proposed in this work; this is based on the analysis of textur... more A robust Font Recognition (OFR) is proposed in this work; this is based on the analysis of texture characteristics of document text using invariant moments (invariant to scale, rotation and translation). There is not need of explicit local analysis in our method since the central moments features are extracted as a global characteristic from each font. A printed text block with a unique font is suitable to provide the specific texture properties necessary for the process of recognition. The used fonts were: Courier, Arial, Bookman Old Style, Franklin Gothic Medium, Comic Sans, Impact, Modern and Times New Roman; and their respective styles: regular, italic, bold, italic with bold. The invariant moment technique is used in this study to extract the font characteristics by window size estimation; from an entry text set a data base was build for the learning stage, and then standard statistical classifiers were applied for the identification stage (combining Gaussian and KNN classifiers ). We found that the invariant moments change significantly when the textures are rotated and scaled as digital images; good recognition rate was obtained in font recognition with noise.

one reference channel. As a result we can see five channels over oscilloscope screen, and the sig... more one reference channel. As a result we can see five channels over oscilloscope screen, and the signal-noise radio was acceptable.