Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010

Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use

Получение шаблонов для распознавания символов автомобильного номера на основе взвешивания обучающего набора

В статье предлагается алгоритм получения шаблонов для решения задачи распознавания символов автомобильного номера методом сопоставления с эталоном. Предлагаемый алгоритм основан на взвешивании обучающего набора и позволяет получить шаблоны каждого класса символов на основе минимизации средней ошибки классификации по всем классам. Полученные предложенным методом шаблоны позволяют решать задачу классификации символов с меньшей ошибкой, чем при использовании в качестве шаблонов центров распределений классифицируемых классов, сохраняя при этом хорошую обобщающую способность метода сопоставления с эталоном.In article the algorithm of templates calculation for the decision of a problem of car plates symbols recognition based on template matching is offered. The offered algorithm is based on weighing of a training set and allows obtaining templates of each class of symbols on the basis of minimization of an average error of classification by all classes. The templates received by an offered method allow to solve a problem of symbols classification with a smaller error, than at use as templates of the centers of distributions of classified classes, keeping thus good generalizing ability of a template matching method

Enhancement in iris recognition system using FPGA

The growth of using the iris recognition over the globe for identification and for verification and the problem that faces the iris recognition from noise like eyelash and eyelid. This paper focus on choosing the right pattern to collect the traits. The algorithm of this paper is searching and working on different rectangle iris template to spotting the ultimate traits that lies within rectangle iris templates. The Ridge Energy Direction (RED) is used as algorithm to spot the features that lies within the template. The overall iris system is design, implemented and tested on the Field Programmable gate Area (FPGA)