Human age recognition from face image relies highly on a reasonable aging description. Considering the disparate and complex face-aging variation of each person, aging description needs to be defined carefully with detailed local information. However, aging description relies highly on the appropriate definition of different aging-affiliated textures. Wrinkles are considered as the most discernible textures in this regard owing to their significant visual appearance in human aging. Most of the existing image-descriptors, however, fail short to preserve diverse variations of wrinkles, such as a) characterizing stronger and smoother wrinkles, appropriately, b) distinguishing wrinkles from non-wrinkle patterns, and c) characterizing the proper texture-structures of the pixels belonging to the same wrinkle. In this paper, we address these issues by presenting a new local descriptor, Local Edge-Prototypic Pattern (LEPP) with the notion that LEPP preserves different variations of wrinkle-patterns appropriately in representing the aging description. In the coding, LEPP sets prototypic restrictions for each neighboring pixel using their relation with center pixel when they belong to an inlying-edge, and utilize such restrictions, afterwards, to prioritize specific neighbors showing significant edge-signature. This strategy appropriately encodes the inlying edge structure of aging-affiliated textures and simultaneously, avoids featureless texture. We visualize the stability of LEPP in terms of its robustness under noise. Our experiments show that LEPP preserves discernible aging variations yielding better accuracies than the state-of-the-art methods in popular age-group datasets.

In this paper we present a novel technique that permits to extract the essential on information embedded in the product of sine polynomial and Gaussian envelope by simply knowing the vertices of the tetrahedral graph.  The study proves that the matrix of vertices of the tetrahedral graph and its variants are the building block of both Haar wavelets, Hadamard-Walsh transform, wavelets sets and tight frames. We also prove that the Berkeley B Gate is a function of the degree matrix and the adjacency matrix of the tetrahedral graph.  The latter is the Hermitian part of the unitary polar decomposition in terms of elementary gates for quantum computation [68] which reveals interesting properties of the tetrahedral graph in both quantum group, Lie group and Pauli group for wavelets sets, quantum image processing and quantum data compression. We explore the connection existing among graphs theory, wavelets, tight frames and quantum logic gates.

Solar batteries are the essential component in an off-grid solar photovoltaic generation system and used for the purpose of storage. Normally lead acid batteries are used for solar applications and are placed in a battery room where the temperature must be maintained with in safe working limits. The temperature of battery depends on several factors like ambient temperature, load current drawn by the battery, sulphur deposition terminals, charging and discharging cycles. When temperature of a battery increases beyond safe limit problems of heating arises. Heating reduces the battery life and may be one of the reasons of explosion in batteries. The ambient temperature measuring device used in battery room is not sufficient to measure the heat generated by batteries. Installation of a normal camera in battery room is capable to monitor the smoke or spark in battery components but not the amount of temperature so it is also not fulfilling the need. Thermal imaging camera captures heat coming out from a battery and produces a thermal image with temperatures associated with it. This thermal image is used for representing high and low temperatures with a range of maximum and minimum values of temperature. In this paper a heated battery is identified in a battery room for solar photovoltaic generation system and thermal image analysis is performed to determine the regions of high temperature with the help of image segmentation and 3D temperature plot. The image segmentation is done using marker based watershed transform technique to achieve the heated region of interest from thermal image and 3D temperature plot shows the area of maximum temperature with location in thermal image of heated solar battery.  

Sounds produced by acoustic activity of the heart are series (sequences) of quasi-periodic events which are repeated throughout life, one period (cycle) of these events lasts less than one second. The advancements in technology have enabled us to create various tools for audio and graphic representations of these events. Physicians, by using such tools, can more accurately determine diagnosis by interpretation of heart sound and/or by visual interpretation of graphic displays of heart sounds. This paper presents frequency parameters and graphic illustrations of heart sound signals for two groups of heart murmurs: innocent Still’s murmur and pathologic heart murmur of Ventricular Septal Defect (VSD). Also, on behalf of the frequency analysis of acoustic cardiac signals with Still’s murmur was given a medical explanation of cause and origin of Still’s murmur.

The authentication is used to ensure the authentication of the owner of the data. Currently, the data is available in multimedia format viz., audio, video, image and text. The present paper focuses on the image authentication. The watermarking methods are used for the image authentication. The present paper proposes a novel method of Kurtosis based Watermarking by using Wavelet Transformation (KWWT). The proposed method uses wavelet transformation. Further, the bands or the coefficients are divided into various non overlapped windows. For each of the approximation band windows, the kurtosis value will be estimated. Then the windows in all bands will be selected based on their kurtosis value. Then, the watermark image will be embedded into the selected windows of the bands. Finally, inverse wavelet transformation will be applied to get the resultant watermarked image. The proposed KWWT method is evaluated with 14 input images and 3 watermark images. Various performance measures are estimated and the results show the efficacy of the proposed method.

Facial expressions, usually has an adverse effect on the performance of a face recognition system. In this investigation, expression invariant face recognition algorithm is presented that converts input face image with an arbitrary expression into its corresponding neutral facial image. In the present study, deep learning algorithm is used to train classifiers for reference key-points, where key-points are located and deep neural network is trained to make the system able to locate the landmarks in test image. Create an intermediate triangular mesh from the test and reference image and then warp it using affine transform and take the average of the normalized faces. To extract the features presented in the result image shift invariant feature extraction technique is used. Finally, results are compared and the recognition accuracy is determined for different expressions. The present work is tested on three different databases: JAFFE, Cohn-Kanade (CK) and Yale database. Experimental results show that the expression invariant face recognition method is very robust to variety of expressions and recognition accuracy is found to be 97.8 %, 96.8% and 95.7% for CK, JAFFE and Yale databases respectively.

Image edge detection is a process where true edges of an image are identified. In past, gradient based methods in which first or second order pixel difference is used to find discontinuities and if magnitude value of gradient is higher than certain threshold then that pixel under observation is identified as edge pixel. These methods are full of error, because in addition to true edges they also find false edges and infect false edges are more in comparison to true edges. To solve such problem, swarm intelligence based ant colony optimization based edge detection method is detailed where numbers of falsely detected edges are very small. The performance of the ant colony optimization (ACO) is done in terms of Peak Signal to Noise Ratio, Performance Ratio and Efficiency.