Quality Assessment of Gaussian Blurred Images Using Symmetric Geometric Moments

A novel objective full-reference image quality assessment metric based on symmetric geometric moments (SGM) is proposed. SGM is used to represent the structural information in the reference and test images. The reference and test images are divided into (8 £ 8) blocks and the SGM up to fourth order for each block is computed. SGM of the corresponding blocks of the reference and test images are used to form the correlation index or quality metric of each block. The correlation index of the test image is then obtained by taking the average of all blocks. The performance of the proposed metric is validated through subjective evaluation by comparing with objective methods (PSNR and MSSIM) on a database of 174 Gaussian blurred images. The proposed metric performs better than PSNR and MSSIM by providing larger correlation coefficients and smaller errors after nonlinear regression fitting

Using genetic algorithm to identify the discriminatory subset of multi-channel spectral bands for visual response

In this paper, we propose a technique that uses genetic algorithm (GA) with Fuzzy ARTMAP (FA) classifier to identify the discriminatory subset of the feature set for classification of alcoholics and non-alcoholics using brain rhythm extracted during visual stimulus. In the experimental study, the feature set consists of seven spectral power ratios extracted from 61 visual evoked potential (VEP) channels. The seven spectral bands of VEP signals in the range of 2-50 Hz are extracted using constant gain and uniform bandwidth infinite impulse response (IIR) band-pass filters. Spectral power in these bands are obtained using Parseval's time-frequency energy equivalence theorem. The spectral power ratio for each band is obtained by dividing the spectral power of the band with the total spectral power of the channel. Classification experiments using FA and multilayer perceptron-backpropagation (MLP-BP) classifiers are carried out to confirm that the identified spectral power ratios and channels using the proposed technique are discriminatory. The classification results show that the difference of VEP signals between alcoholics and non-alcoholics can be observed using two spectral power ratios in gamma band (37-50 Hz) extracted from seven channels. This fact indicates that gamma band spectral power could be used to show evidence on the lasting effects of long-term use of alcohol on visual response though the studied alcoholics have been abstinent for a minimum period of 1 month

Mathematical models for prediction of active substance content in pharmaceutical tablets and moisture in wheat

In the prediction of active substance content in pharmaceutical tablets and moisture in wheat, a very large number of wavelengths were used. Hence, a method to identify a limited number of wavelengths was developed. We introduce a novel approach that uses the discrete cosine transform (DCT) for this purpose. The data was obtained using near infrared spectrometer. From the DCT coefficients, a limited number was chosen as predictor variables to be used in partial least square (PLS) regression. Likewise, a limited number of DFT coefficients were also used in the PLS regression. The performance of combining the DCT with PLS was compared with that of the PLS model using the full spectral data and with the discrete Fourier transform (DFT). The results showed that the PLS model using DCT coefficients produced lower root mean square error than using the full NIR spectral data with the PLS and also the DFT. (C) 2008 Elsevier B.V. All rights reserved

Arbitrarily-oriented multi-lingual text detection in video

© 2016, Springer Science+Business Media New York. Text detection in arbitrarily-oriented multi-lingual video is an emerging area of research because it plays a vital role for developing real-time indexing and retrieval systems. In this paper, we propose to explore moments for identifying text candidates. We introduce a novel idea for determining automatic windows to extract moments for tackling multi-font and multi-sized text in video based on stroke width information. The temporal information is explored to find deviations between moving and non-moving pixels in successive frames iteratively, which results in static clusters containing caption text and dynamic clusters containing scene text, as well as background pixels. The gradient directions of pixels in static and dynamic clusters are analyzed to identify the potential text candidates. Furthermore, boundary growing is proposed that expands the boundary of potential text candidates until it finds neighbor components based on the nearest neighbor criterion. This process outputs text lines appearing in the video. Experimental results on standard video data, namely, ICDAR 2013, ICDAR 2015, YVT videos and on our own English and Multi-lingual videos demonstrate that the proposed method outperforms the state-of-the-art methods

Image quality assessment by discrete orthogonal moments

This paper proposes a novel full-reference quality assessment (QA) metric that automatically assesses the quality of an image in the discrete orthogonal moments domain. This metric is constructed by representing the spatial information of an image using low order moments. The computation, up to fourth order moments, is performed on each individual (8 x 8) non-overlapping block for both the test and reference images. Then, the computed moments of both the test and reference images are combined in order to determine the moment correlation index of each block in each order. The number of moment correlation indices used in this study is nine. Next, the mean of each moment correlation index is computed and thereafter the single quality interpretation of the test image with respect to its reference is determined by taking the mean value of the computed means of all the moment correlation indices. The proposed objective metrics based on two discrete orthogonal moments. Tchebichef and Krawtchouk moments, are developed and their performances are evaluated by comparing them with subjective ratings on several publicly available databases. The proposed discrete orthogonal moments based metric performs competitively well with the state-of-the-art models in terms of quality prediction while outperforms them in terms of computational speed. (C) 2010 Elsevier Ltd. All rights reserved

A new brain-computer interface design using fuzzy ARTMAP

This paper proposes a new brain-computer interface (BCI) design using fuzzy ARTMAP (FA) neural network, as well as an application of the design. The objective of this BCI-FA design is to classify the best three of the five available mental tasks for each subject using power spectral density (PSD) values of electroencephalogram (EEG) signals. These PSD values are extracted using the Wiener-Khinchine and autoregressive methods. Ten experiments employing different triplets of mental tasks are studied for each subject. The findings show that the average BCI-FA outputs for four subjects gave less than 6% of error using the best triplets of mental tasks identified from the classification performances of FA. This implies that the BCI-FA can be successfully used with a tri-state switching device. As an application, a proposed tri-state Morse code scheme could be utilized to translate the outputs of this BCI-FA design into English letters. In this scheme, the three BCI-FA outputs correspond to a dot and a dash, which are the two basic Morse code alphabets and a space to denote the end (or beginning) of a dot or a dash. The construction of English letters using this tri-state Morse code scheme is determined only by the sequence of mental tasks and is independent of the time duration of each mental task. This is especially useful for constructing letters that are represented as multiple dots or dashes. This combination of BCI-FA design and the tri-state Morse code scheme could be developed as a communication system for paralyzed patients

A two-level partial least squares system for non-invasive blood glucose concentration prediction

In this study, we propose and demonstrate a novel two-Level Partial Least Squares (2L-PLS) architecture for non-invasive blood glucose concentration measurement. A total of 290 Near-Infrared (NIR) spectroscopy readings from six laser diodes with discrete wavelengths of between 1500 nm and 1800 nm are obtained together with blood glucose concentration readings collected via Oral Glucose Tolerance Test (OGTT) experiments from a healthy volunteer over 4 days. While the conventional approach to predicting the blood glucose concentrations is to use a single Partial Least Squares (PLS) or non-linear PLS model, these systems do not achieve a high level of accuracy. As such, a 2L-PLS system consisting of one PLS model at the first level and three at the second level is proposed to enhance the prediction accuracy. A non-linear 2L-PLS system based on the same structure is also investigated in this study. The proposed 2L-PLS systems show improvements of 10 to 12% in the number of predictions that fall below a 5% error margin as compared to single-level PLS systems. (C) 2010 Elsevier B.V. All rights reserved