Two new diterpene polyesters from Euphorbia decipiens

Two new myrsinol-type diterpene polyesters 3,5,13,17-tetra-O-acetyl-7-O-benzoyl-15-hydroxymyrsinol (1) and 3,5,13,17-tetra-O-acetyl-7-O-butanoyl-13-hydroxymyrsinol (2), with a tricyclic carbon skeleton have been isolated from Euphorbia decipiens Boiss. & Buhse. The structure elucidation of the isolated compounds was based primarily on HREIMS, EIMS, IR, UV, ID-, and 2D-NMR analyses, including COSY, HMQC, HMBC, and NOESY correlations. Compounds 1 and 2 also showed activity against urease enzyme.     

Robust iris biometric system for visible wavelength data

Commercial iris biometric systems exhibit good performance for near-infrared(NIR) images but poor performance for visible wavelength(VW) data.To address this problem,we propose an iris biometric system for VW data.The system includes localizing iris boundaries that use bimodal thresholding,Euclidean distance transform(EDT),and a circular pixel counting scheme(CPCS).Eyelids are localized using a parabolic pixel counting scheme(PPCS),and eyelashes,light reflections,and skin parts are adaptively detected using image intensity.Features are extracted using the log Gabor filter,and finally,matching is performed using Hamming distance(HD).The experimental results on UBIRIS and CASIA show that the proposed technique outperforms contemporary approaches.     

A non-circular iris localization algorithm using image projection function and gray level statistics

Iris recognition technology identifies an individual from its iris texture with great precision. A typical iris recognition system comprises eye image acquisition, iris segmentation, feature extraction, and matching. However, the system precision greatly depends on accurate iris localization in the segmentation module. In this paper, we propose a reliable iris localization algorithm. First, we locate a coarse eye location in an eye image using integral projection function (IPF). Next, we localize the pupillary boundary in a sub image using a reliable technique based on the histogram-bisection, image statistics, eccentricity, and object geometry. After that, we localize the limbic boundary using a robust scheme based on the radial gradients and an error distance transform. Finally, we regularize the actual iris boundaries using active contours. The proposed algorithm is tested on public iris databases: MMU V1.0, CASIA-IrisV1, and the CASIA-IrisV3-Lamp. Experimental results demonstrate superiority of the proposed algorithm over some of the contemporary techniques.     

Three new sesquiterpene hemiacetals from Achillea vermicularis

Three new sesquiterpene hemiacetals, tentatively named as achilleanone (1), vermiculone (2) and vermicularone (3) have been isolated from Achillea vermicularis, along with three other known compounds beta-amyrin, oleonolic acid and beta-sitosterol. The structure elucidation of new compounds was based primarily on two-dimensional (2D) NMR techniques including Nuclear Overhauser Effect/Enhancement (NOE), heteronuclear multiple quantum coherence (HMQC), heteronuclear multiple bond correlation (HMBC) and nuclear overhauser effect spectroscopy (NOESY) experiments. Compounds 1, 2 and 3 have displayed inhibitory potential against lipoxygenase enzyme in a concentration-dependent fashion with promising IC50 values.     

Iris localization based on the Hough transform,a radial-gradient operator,and the gray-level intensity

The iris biometric recognizes a human based on his/her iris texture, which is a stable and unique feature for every individual. A typical iris biometric system performs better for the ideal data, which is acquired under controlled conditions. However, its performance degrades when localizing iris in non-ideal data containing the noisy issues, e.g., the non-uniform illumination, defocus, and non-circular iris boundaries. This study proposes a reliable algorithm to localize iris in such images robustly. First, a small region containing the coarse location of iris is localized. Next, the pupillary boundary is extracted within this small region using an iterative-scheme comprising an adaptive binarization and a pupil location verification test. Following that, the limbic boundary is localized by reusing the Hough accumulator. The iris location is also verified through a gray-level test. After that, the pupillary and limbic boundaries are regularized by applying an enhanced method comprising a Radial-gradient operator (RGO), an error-transform (ET), and the Fourier series. Experimental results, obtained on the CASIA-IrisV3, CASIA-IrisV4, MMU V1.0, and MMU(new) V2.0 iris databases, show superiority of the proposed technique over some of the contemporary techniques.     

Iris segmentation for visible wavelength and near infrared eye images

State-of-the-art iris segmentation algorithms exhibit poor performance for non-ideal data, which is mainly because of the noise such as low contrast, non-uniform illumination, reflections, and among others. To address this issue, a robust iris segmentation scheme is proposed that includes the following: First, a set of the Seed-pixels in a preprocessed eye image is marked adaptively. Next, a two-fold scheme based on a Circu-differential accumulator (CDA) and gray statistics is adopted to localize coarse iris region robustly. Notably, the proposed CDA has close resemblance with the Hough transform; however, it consumes relatively less memory and is free from thresholding as well. Similarly, pupillary boundary is localized, which is verified through an intensity test as well. Next, a refine estimate for the limbic boundary is extracted. After that, iris boundaries are regularized using the Fourier series. Finally, the eyelids are localized using a Para-differential accumulator (PDA), and eyelashes and reflections are also localized adaptively in the polar form of iris. Experimental results on the near infrared (NIR) and visible wavelength (VW) iris databases show that the proposed technique outperforms contemporary approaches.     

Reliable iris localization using integral projection function and 2D-shape properties

Iris recognition technology recognizes a human based on his/her iris pattern.However,the accuracy of the iris recognition technology depends on accurate iris localization.Localizing a pupil region in the presence of other low-intensity regions,such as hairs,eyebrows,and eyelashes,is a challenging task.This study proposes an iris localization technique that includes a localizing pupillary boundary in a sub-image by using an integral projection function and two-dimensional shape properties(e.g.,area,geometry,and circularity).The limbic boundary is localized using gradients and an error distance transform,and the boundary is regularized with active contours.Experimental results obtained from public databases show the superiority of the proposed technique over contemporary methods.