Sirindhorn International Institute of Technology

About: Sirindhorn International Institute of Technology is a based out in . It is known for research contribution in the topics: Supply chain & Combustion. The organization has 1048 authors who have published 1678 publications receiving 30067 citations.

An image tamper detection and recovery method based on self-embedding dual watermarking

Abstract: This paper describes an image tamper detection and recovery method based on self-embedding dual watermarking. The watermark in the proposed method is a down-sampled version of an original image (cover image). Two identical watermarks are then decomposed into bit planes and embedded into the least-significant bit (LSB) plane of the cover image. This dual watermarking strategy ensures a robust performance in image tamper detection and recovery. Moreover, since the bit planes of the two identical watermarks are arranged in center-point-symmetric manner, image information is spread uniformly. This makes it possible to recover large area of tampering, such as, left, right, upper, or lower half of the cover image. Experimental results show that the proposed method is remarkably effective for detecting and recovering local image tampering, such as, image collage.

Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis.

Abstract: We propose vessel vector-based phase portrait analysis (VVPPA) and a hybrid between VVPPA and a clustering method proposed earlier for automatic optic disk (OD) detection called the vessel transform (VT). The algorithms are based primarily on the location and direction of retinal blood vessels and work equally well on fine and poor quality images. To localize the OD, the direction vectors derived from the vessel network are constructed, and points of convergence of the resulting vector field are examined by phase portrait analysis. The hybrid method (HM) uses a set of rules acquired from the decision model to alternate the use of VVPPA and VT. To identify the OD contour, the scale space (SS) approach is integrated with VVPPA, HM, and the circular approximation (SSVVPPAC and SSHMC). We test the proposed combination against state-of-the-art OD detection methods. The results show that the proposed algorithms outperform the benchmark methods, especially on poor quality images. Specifically, the HM gets the highest accuracy of 98% for localization of the OD regardless of the image quality. Testing the segmentation routines SSVVPPAC and SSHMC against the conventional methods shows that SSHMC performs better than the existing methods, achieving the highest PPV of 71.81% and the highest sensitivity of 70.67% for poor quality images. Furthermore, the HM can supplement practically any segmentation model as long as it offers multiple OD candidates. In order to prove this claim, we test the efficiency of the HM in detecting retinal abnormalities in a real clinical setting. The images have been obtained by portable lens connected to a smart phone. In detecting the abnormalities related to diabetic retinopathy (DR), the algorithm provided 94.67 and 98.13% for true negatives and true positives, respectively.

A comparative study of image quality assessment

Abstract: Image quality assessment (IQA) plays an important role in many image processing tasks. Although several IQA methods have been developed for decades, each matric has its individual characteristic for evaluation. In this paper, we compare popular IQA methods including peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean structural similarity index (MSSIM) and feature similarity index (FSIM) on validating 3 different test images with various image degradation factors. The results of comparative experiment show that although each method has different sensitivity for different distortion, MSSIM is a good metric among them in overall performance. It can be used for a wide variety of image degradation. The parameters can be adjusted for different purposes.

A total Lagrangian Timoshenko beam formulation for geometrically nonlinear isogeometric analysis of planar curved beams

Abstract: This paper presents a new total Lagrangian Timoshenko beam formulation with the isogeometric analysis approach for a geometrically nonlinear analysis of planar curved beams. The proposed formulation is derived from the two-dimensional continuum theory, and a beam configuration is characterized by the beam axis and the director vectors of cross sections. Non-uniform rational B-spline (NURBS) curves are used for the geometric representation of the beam axis and the discretization of the unknown kinematics, i.e., the translational displacements of the beam axis and the cross-sectional rotation angle. Several well-established examples covering the analysis of many types of beam, i.e., straight, curved, and free-form beams with varying curvature, are considered. The obtained results by the proposed formulation are compared with those in the literature, and the accuracy and efficiency of the proposed formulation are assessed. The prominent properties of using NURBS curves in analysis, i.e., effective reduction in locking effects, higher accuracy per degree-of-freedom, and better geometric representations, are also verified for the proposed beam formulation.