Vilas H. Gaidhane

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.

A new approach for flame image edges detection

In this paper, a simple and robust approach for flame image analysis is presented. It is based on the Local Binary Patterns and thresholding techniques. The main features of an image are obtained by Local Binary patterns, and two level thresholding is used to make the edges visible and clear. The simplicity and robustness of the proposed approach in noisy environment makes it suitable for the subsequent analysis of the flame features. Various experimentations are carried out on synthetic as well as real images. The results show the proposed approach gives good localization and effective edge detection as compared to the existing methods.

Image focus measure based on polynomial coefficients and spectral radius

In this paper, a new measure of image focus based on the statistical properties of polynomial coefficients and spectral radius is proposed. Spectral radius captures the dominant features and represents the important dynamics of an image. It is shown that the proposed focus measure is monotonic and unimodal with respect to the degree of defocusation, noise and blurring effects. Moreover, it is sufficiently invariant to contrast changes occur due to the variations in intensities of illumination. The noise studies show that the proposed focus measure is robust under the different noisy and blurring conditions. The performance of proposed focus measure is gauged by comparing with the existing image focus measures. Experimental results using synthetic as well as real-time images with known and unknown distortion conditions show the wider working capability and higher prediction consistency of the proposed focus measure. Moreover, the performance of the proposed approach is validated with most popular five image quality databases: TID2008, LIVE, CSIQ, IVC and Cornell-A57. Experimentation on the databases shows that the proposed metric provides the comparatively higher correlation with ideal mean observer score than the existing metrics.

An efficient similarity measure approach for PCB surface defect detection

In this paper, an efficient similarity measure method is proposed for printed circuit board (PCB) surface defect detection. The advantage of the presented approach is that the measurement of similarity between the scene image and the reference image of PCB surface is taken without computing image features such as eigenvalues and eigenvectors. In the proposed approach, a symmetric matrix is calculated using the companion matrices of two compared images. Further, the rank of a symmetric matrix is used as similarity measure metric for defect detection. The numerical value of rank is zero for the defectless images and distinctly large for defective images. It is reliable and well tolerated to local variations and misalignment. The various experiments are carried out on the different PCB images. Moreover, the presented approach is tested in the presence of varying illumination and noise effect. Experimental results have shown the effectiveness of the proposed approach for detecting and locating the local defects in a complicated component-mounted PCB images.

A New Approach for Stability Analysis of Discrete Systems

ABSTRACT In this paper, a new approach is presented to calculate the stability margin of the discrete systems. It is based on the algorithm for reduced conservatism of eigenvalues and the Gerschgorin circle theorem. The necessary and sufficient condition for conservatism of eigenvalues of matrix is stated and proved mathematically. Moreover, the proposed approach is illustrated with an example and it is compared with the other existing methods.

An efficient edge extraction approach for flame image analysis

In this paper, a simple and robust approach for flame and fire image analysis is proposed. It is based on the local binary patterns, double thresholding and Levenberg–Marquardt optimization technique. The presented algorithm detects the sharp edges and removes the noise and irrelevant artifacts. The autoadaptive nature of the algorithm ensures the primary edges of the flame and fire are identified in the different conditions. Moreover, a graphical approach is presented which can be used to calculate the combustion furnace flame temperature. The various experimentations are carried out on synthetic as well as real flame and fire images which validate the efficacy and robustness of the proposed approach.

Daubechies wavelet-based local feature descriptor for multimodal medical image registration

A new local feature descriptor recursive Daubechies pattern (RDbW) is developed by defining and encoding the Daubechies wavelet decomposed center–neighbour pixel relationship in the local texture. RDbW features are applied in spatial alignment (registration) of multimodal medical images using a Procrustes analysis (PA)-based affine transformation function and the registered images are further fused by employing a wavelet-based fusion method. A significant amount of experiments is conducted and the registration and fusion accuracy of the proposed feature descriptor is compared with the prominent existing methods such as local binary patterns (LBP), local tetra pattern (LTrP), local diagonal extrema pattern (LDEP), and local diagonal Laplacian pattern (LDLP). Experimental results show the present registration method improves the average registration accuracy by 38, 47, 71, and 76% in contrast to LDLP, LDEP, LTrP, and LBP, respectively. Further, the fusion results of the current approach exhibit an average improvement in entropy by 11%, standard deviation by 6% edge strength by 12%, sharpness by 23%, and average gradient by 16% when compared with all other feature descriptors used for registering the images. Concepts presented here can be used widely in analysing the combined information present in multimodal medical images.

Medical image rigid registration using a novel binary feature descriptor and modified affine transform

Robust and reliable features with noise immunity, rotation-invariance, and low-dimensionality are the challenging aspects of pattern recognition. In this study, the authors presented a novel low-dimensional binary feature descriptor local diagonal Laplacian pattern (LDLP) for medical image registration. LDLP method is developed by defining the local relationship between a centre pixel and its diagonal neighbours and encoding it to a binary feature vector. The idea of centre-diagonal pixel correlation has drastically reduced the length of the feature vector without compromising the quality of local texture analysis. In the proposed work, first, the LDLP feature histograms of computed tomography (CT), magnetic resonance (MR), and ultrasound images are obtained. Further, these LDLP features of individual medical images are considered as target/fixed objects while their corresponding rotated and noisy features are considered as moving/floating objects to perform mono-modal rigid registration using an improved Procrustes analysis-based affine transform. The registration quality is examined by calculating the squared intensity error and the results are compared with the existing binary patterns such as local binary patterns, local tetra patterns, and local diagonal extrema patterns. The proposed LDLP feature descriptor-based rigid registration has attained relatively better performance in terms of registration accuracy and computational complexity.

An improved edge detection approach and its application in defect detection

In this paper, a simple and efficient edge detection technique is proposed. It is based on the properties of the hyper smoothing function and the fundamentals of modified local binary pattern and hence, called as HY-LBP. The main advantage of the proposed approach is that the relationship between surrounding pixels and centric one is calculated effectively and extract the surrounding information discriminatively. The counting scheme that counts the number of image points whose pixel values are greater or equal than that of the central pixel help to reduce the noise and blurring effects. Thus, HY-LBP can effectively deal with the noises, blurring, and contrast variation. The effectuality and feasibility of the proposed approach is demonstrated on various synthetic and real time images. Experimental results have shown the effectiveness as well as the better performance of the proposed approach for edge detection as compared to the other existing methods. The presented method can be used in metal sheet defect detection applications.