Articles written in Sadhana
Volume 37 Issue 6 December 2012 pp 709-721
In this paper, an efﬁcient similarity measure technique is proposed for medical image registration. The proposed approach is based on the Gerschgorin circles theorem. In this approach, image registration is carried out by considering Gerschgorin bounds of a covariance matrix of two compared images with normalized energy. The beauty of this approach is that there is no need to calculate image features like eigenvalues and eigenvectors. This technique is superior to other well-known techniques such as normalized cross-correlation method and eigenvalue-based similarity measures since it avoids the false registration and requires less computation. The proposed approach is sensitive to small defects and robust to change in illuminations and noise. Experimental results on various synthetic medical images have shown the effectiveness of the proposed technique for detecting and locating the disease in the complicated medical images.
Volume 39 Issue 2 April 2014 pp 317-331
Image registration using template matching is an important step in image processing. In this paper, a simple, robust and computationally efficient approach is presented. The proposed approach is based on the properties of a normalized covariance matrix. The main advantage of the proposed approach is that the image matching can be achieved without calculating eigenvalues and eigenvectors of a covariance matrix, hence reduces the computational complexity. The experimental results show that the proposed approach performs better in the presence of various noises and rigid geometric transformations.
Volume 41 Issue 4 April 2016 pp 415-423
In this paper, a simple and computationally efficient approach is proposed for person independent facial emotion recognition. The proposed approach is based on the significant features of an image, i.e., the collection of few largest eigenvalues (LE). Further, a Levenberg–Marquardt algorithm-based neural network (LMNN) is applied for multiclass emotions classification. This leads to a new facial emotion recognition approach (LE-LMNN) which is systematically examined on JAFFE and Cohn–Kanade databases. Experimental results illustrate that the LE-LMNN approach is effective and computationally efficient for facial emotion recognition. The robustness of the proposed approach is also tested on low-resolution facial emotion images.The performance of the proposed approach is found to be superior as compared to the various existing methods.