Analysis of bit-plane images by using principal component on face and palmprint database

The bit-plane feature extraction approach has lately been introduced for face and palm-print recognition. This approach decomposes an 8-bit grey level image into eight groups of bit layers. The assumption of this approach is that the highest order of a bit-plane decomposition, which has the most significant bits of all pixels, contains the most biometric features. Nonetheless, most research has identified bit-plane images illustratively. Hence, in order to endorse the assumption, we performed an analysis on face and palm-print images to identify the bit-plane that contributes most significantly to the recognition performance. Analysis was done based on Principal Component Analysis (PCA). The first principal component was applied as it is defined for the largest possible variance of the data. Next, Euclidean distance was calculated for matching performance. It was observed that bit-plane 6 and 7 contributed significantly to recognition performance. © 2016 Universiti Putra Malaysia Press

Image noise severity metric

We propose in this paper an image noise severity measurement method that correlates well with human's quality perception on the presence of noise in images. In our approach, a 32x32 pixels mask is used to compute the differences between the original and noise-degraded images in terms of the statistical means and outlier values. These differences are formulated and then compared to the quality scores from the subjective evaluations. The degraded images were distorted by two common types of random noise for images - Gaussian white noise and impulse noise. Experiment results showed that this approach obtained higher correlation compare to classical Peak Signal to Noise Ratio (PSNR) method

Full-Reference and No-reference Image Blur Assessment Based on Edge Information

Blur images are often subjected to the loss of high frequency content during acquisition, compression and multimedia transmission. Hence, objective blur assessment is implemented to identify and quantify image quality degradation by blurriness artifact in order to maintain and control the quality of the images. In this paper, objective full-reference and no-reference blur assessments using edge information are presented with the aim to provide computational models that can automatically measure the amount of blurriness artifact such as Gaussian blur on the images. The amount of Gaussian blur on an image, also known as the final blur measurement is determined by averaging the sum of edge width over all detected edges which satisfy the edge criteria. The final blur measurement for all test images based on full-reference and no-reference implementations are also validated with subjective results. The validation results show that the objective full-reference and no-reference blur assessments correlate closely to perceptual image quality

Video quality assessment based on a modified mean squared error

Video quality is defined as how much the similarity of the video to its original undistorted video. In order to judge the video quality without human interception, video quality assessment (VQA) methods are proposed. Most of the VQA were developed from the popular image quality assessment (IQA) methods such as mean squared error (MSE) or peak signal to noise ratio (PSNR), structural similarity (SSIM) and multi-scale SSIM (MSSIM). However, different from the theory of IQA, VQA involves temporal distortions and effects. Due to the temporal effect, videos are affected by more distortions or errors as compared to images. Nevertheless, image or spatial distortions still affect the video quality. The inclusion of temporal effects with the spatial effects will make the overall distortion becomes perceptually less or more severe to the observer depending on when the temporal effects appear in a video. A new VQA method, dubbed Modified-MSE (Mod-MSE) is proposed in this paper. Mod-MSE is an improved version of MSE with the inclusion of temporal effect. From the results, the proposed VQA method gives acceptable performance when evaluated with the LIVE video database

Phase Change and Corrosion Effect on the Performance of a High Voltage Transmission Conductor

High voltage transmission conductors used worldwide are produced in several configurations. One such configuration is composed of 26 aluminum spiral shaped strands placed at the outer ring and 7 steel strands which are supposed to hold the weight of the conductor, at the inside of the aluminium strands. The present work investigates the effect of time, temperature fluctuations and weather on the properties of such a conductor, over a long period of time (over 25 years). The temperature and load fluctuations create stresses due to expansion and contraction and the protective coating disintegrates due to the friction between the surfaces of the strands. It is also observed that a gradual reduction in diameter exists approaching the failure point which is attributed to the third stage of creep. X ray studies reveal oxidation and a change in grain size which supports the existence of creep. The conductor performance is deteriorated due to a combined effect of corrosion, disintegration of protective coating, reduction in diameter, increase in temperature due to high load and the resulting thermal stresses

Detection of Neovascularization in Diabetic Retinopathy

Diabetic retinopathy has become an increasingly important cause of blindness. Nevertheless, vision loss can be prevented from early detection of diabetic retinopathy and monitor with regular examination. Common automatic detection of retinal abnormalities is for microaneurysms, hemorrhages, hard exudates, and cotton wool spot. However, there is a worse case of retinal abnormality, but not much research was done to detect it. It is neovascularization where new blood vessels grow due to extensive lack of oxygen in the retinal capillaries. This paper shows that various combination of techniques such as image normalization, compactness classifier, morphology-based operator, Gaussian filtering, and thresholding techniques were used in developing of neovascularization detection. A function matrix box was added in order to classify the neovascularization from natural blood vessel. A region-based neovascularization classification was attempted as a diagnostic accuracy. The developed method was tested on images from different database sources with varying quality and image resolution. It shows that specificity and sensitivity results were 89.4% and 63.9%, respectively. The proposed approach yield encouraging results for future development

Preface

Advanced Materials Research383-390