Advances in Pattern Recognition Algorithms, Architectures, and Devices

Over the last decade, tremendous advances have been made in the general area of pattern recognition techniques, devices, and algorithms. We have had the distinct pleasure of witnessing this remarkable growth as evidenced through their dissemination in the previous Optical Engineering special sections we have jointly edited— January 1998, March 1998, May 2000, and January 2002. Twenty-six papers were finally accepted for this latest special section, encompassing the recent trends and advancements made in many different areas of pattern recognition techniques utilizing algorithms, architectures, implementations, and devices. These techniques include matched spatial filter based recognition, hit-miss transforms, invariant pattern recognition, joint transform correlator JTC based recognition, morphological processing based recognition, neural network based recognition, wavelet based recognition, fingerprint and face recognition, data fusion based recognition, and target tracking, as well as other techniques. These papers summarize the work of 70 researchers from eight countries

Possible detection of cervical spondylotic neuropathy using Distribution of F-latency (DFL), a new neurophysiological parameter

<p>Abstract</p> <p>Background</p> <p>We have previously reported a new nerve conduction measurement parameter which we named the Distribution of F-latency (DFL) and showed that this was an approximate mirror of the Distribution of Conduction Velocity (DCV) of motor nerve fibers. This work was performed using measurements on the 20 median nerves from 10 volunteers. The DFL showed a number of different patterns including single peaks, broad peaks and double peaks, the latter observed on subjects with Cervical Spondylosis (CS). It was thought that a retrospective analysis of these data could be worthwhile in determining whether Cervical Spondylotic neuropathy could be detected using the DFL.</p> <p>Findings</p> <p>The DFL from the 8 median nerves of 4 normal subjects had single peaks, which has been assumed to represent a normal pattern. The DFL from one side of 5 subjects diagnosed with or suspected to have CS had double peaks. Broad peaks were observed in 7 nerves of which 5 were from subjects who had double peaks in the DFL on the contra lateral side.</p> <p>Conclusions</p> <p>Based on these findings, double peaks in the DFL appear to be associated with CS neuropathy. These findings further suggest that broad peaks in the DFL could indicate the early stages of the disease. Differential compression of nerve branches at the spinal roots are being explored as possible causes. This study does not preclude other pathologies contributing to double or broad peaks, but does suggest that the DFL could form a screening tool for CS neuropathy.</p

Multiclass Object Detection with Single Query in Hyperspectral Imagery Using Class-Associative Spectral Fringe-Adjusted Joint Transform Correlation

We present a deterministic object detection algorithm capable of detecting multiclass objects in hyperspectral imagery (HSI) without any training or preprocessing. The proposed method, which is named class-associative spectral fringe-adjusted joint transform correlation (CSFJTC), is based on joint transform correlation (JTC) between object and nonobject spectral signatures to search for a similar match, which only requires one query (training-free) from the object\u27s spectral signature. Our method utilizes class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques in spectral correlation domain to perform the object detection task. The output of CSFJTC yields a pair of sharp correlation peaks for a matched target and negligible or no correlation peaks for a mismatch. Experimental results, in terms of receiver operating characteristic (ROC) curves and area-under-ROC (AUROC), on three popular real-world hyperspectral data sets demonstrate the superiority of the proposed CSFJTC technique over other well-known hyperspectral object detection approaches

A Robust Fringe-Adjusted Joint Transform Correlator for Efficient Object Detection

The fringe-adjusted joint transform correlation (FJTC) technique has been widely used for real-time optical pattern recognition applications. However, the classical FJTC technique suffers from target distortions due to noise, scale, rotation and illumination variations of the targets in input scenes. Several improvements of the FJTC have been proposed in the literature to accommodate these problems. Some popular techniques such as synthetic discriminant function (SDF) based FJTC was designed to alleviate the problems of scale and rotation variations of the target, whereas wavelet based FJTC has been found to yield better performance for noisy targets in the input scenes. While these techniques integrated with specific features to improve performance of the FJTC, a unified and synergistic approach to equip the FJTC with robust features is yet to be done. Thus, in this paper, a robust FJTC technique based on sequential filtering approach is proposed. The proposed method is developed in such a way that it is insensitive to rotation, scale, noise and illumination variations of the targets. Specifically, local phase (LP) features from monogenic signal is utilized to reduce the effect of background illumination thereby achieving illumination invariance. The SDF is implemented to achieve rotation and scale invariance, whereas the logarithmic fringe-adjusted filter (LFAF) is employed to reduce the noise effect. The proposed technique can be used as a real-time region-of-interest detector in wide-area surveillance for automatic object detection. The feasibility of the proposed technique has been tested on aerial imagery and has observed promising performance in detection accuracy

Multiple Object Detection in Hyperspectral Imagery Using Spectral Fringe-Adjusted Joint Transform Correlator

Hyperspectral imaging (HSI) sensors provide plenty of spectral information to uniquely identify materials by their reflectance spectra, and this information has been effectively used for object detection and identification applications. Joint transform correlation (JTC) based object detection techniques in HSI have been proposed in the literatures, such as spectral fringe-adjusted joint transform correlation (SFJTC) and with its several improvements. However, to our knowledge, the SFJTC based techniques were designed to detect only similar patterns in hyperspectral data cube and not for dissimilar patterns. Thus, in this paper, a new deterministic object detection approach using SFJTC is proposed to perform multiple dissimilar target detection in hyperspectral imagery. In this technique, input spectral signatures from a given hyperspectral image data cube are correlated with the multiple reference signatures using the classassociative technique. To achieve better correlation output, the concept of SFJTC and the modified Fourier-plane image subtraction technique are incorporated in the multiple target detection processes. The output of this technique provides sharp and high correlation peaks for a match and negligible or no correlation peaks for a mismatch. Test results using real-life hyperspectral data cube show that the proposed algorithm can successfully detect multiple dissimilar patterns with high discrimination

Joint Wavelet Transform Correlation with Separated Target and Reference Planes

In recent years, we realize the usefulness of feature extraction for optical correlator and hereby, we investigate the capability of Laplace operator in feature extraction of multiple targets. The first-order terms and the false alarm terms in the correlation output would be removed using electronic power spectrum subtraction technique. Most importantly, the entire magneto-optic SLM is completely utilized for displaying only targets in the input scene. A new cost efficient hardware implementation is proposed and aforementioned result of the proposed system is evaluated through computer simulation

Antioxidant, antimicrobial and cytotoxic activities of Corypha taliera Roxb

The methanol extract of Corypha taliera fruits as well as its n-hexane, carbon tetrachloride, dichloromethane and aqueous soluble fractions were subjected to screening for antioxidant, antimicrobial and cytotoxic activities. The methanolic crude extract exhibited the highest antioxidant activity (IC50 19.33 μg/ml as compared to 9.5 μg/ml for the standard agent, BHT). The crude methanol extract and its carbon tetrachloride, dichloromethane and aqueous soluble fractions showed mild to moderate inhibition of microbial growth against some of the tested organisms. All the extractives exhibited strong cytotoxic properties, among which the methanol extract revealed the strongest cytotixicity (LC50 = 0.43 μg/ml).Colegio de Farmacéuticos de la Provincia de Buenos Aire