Biometrics has gained significant popularity for individual identification in the last decades as a necessity of supporting especially the law enforcement and personal authentication required applications. The face is one of the distinctive biometrics that can be used to identify an individual. Henceforth, Face Recognition (FR) has attracted the great interest of the scientists and academicians. One of the most popular methods preferred for FR is extracting textual features from face images and subsequently performing classification according to these features. A substantial portion of the previous texture analysis and classification studies have based on extracting features from Gray Level Co-occurrence Matrix (GLCM). In this study, we present an alternative method that utilizes Gray Level Total Displacement Matrix (GLTDM) which holds statistical information about the Discrete Wavelet Transform (DWT) of the original face image. The approximation and three detail sub-bands of the image are first calculated. GLTDMs that are specific to these four matrices are subsequently generated. The Haralick features are extracted from those generated four GLTDMs. At the following stage, a new joint feature vector is formed using these four groups of Haralick features. Lastly, extracted features are classified by using K-NN algorithm. As demonstrated in the simulation results, the proposed approach performs promising results in the context of classification.

Sun is an ultimate source of energy, which is clean, inexhaustible and safe for environment. The energy obtained from sun is known as solar energy. When solar radiations fall on earth surface solar cells convert these solar radiations into electrical energy. Solar cells are one of the important components of solar panels. Many solar cells combine in series or in parallel to form solar module and solar panels. A solar photovoltaic array is a combination of solar panels and is installed in open atmospheric condition. The natural conditions such as dust and dirt, shade of tree affect operation of solar panels. These natural conditions cannot be avoided but can be analyzed using visual inspection and thermal camera. The visual inspection approach is useful only when the defects are visible by naked human eye but when defects cannot be visualized by naked human eye thermo-graphical approach is used. This paper discusses the identification of various defects in solar panels by applying image processing technique applied for thermal images under natural atmospheric conditions for visual inspection, shading effect of tree, dust and dirt deposition effects on solar panels using thermal imaging camera. 

Filter is vastly used to detect different human signal in real time. In this paper, a novel complete digital filter is proposed for the fast detection of EEG signals due to avoid the mixtures of different biomedical signals. This paper intends to design a digital complete filter based on Field Programmable Gate Array (FPGA) for the alleviation of unwanted frequency components in biomedical signals specially EEG signals. For this purpose, complete filter which is a combination of integrator filter and differentiator filter which supports both low and high noises and comparatively inexpensive than other signal processing methodologies can be used. For hardware implementation, FPGA board is used which is a combination of different logic gates which offers inexpensive and long lasting services.

This paper presents two segmentation algorithms for MR spine image segmentation helping in on time diagnosis of the spine hernia and surgical intervention whenever required. One is level set segmentation and another one is watershed segmentation algorithm. Both of these methods have been widely used before (Aslan, Farag, Arnold and Xiang, 2011) (Pan, et al., 2013) (Silvia, España, Antonio, Estanislao , and David, 2015) (Erdil, Argunşah, Ünay and Çetin, 2013) (Claudia. Et al, 2007). In our approach we have used the concept of variational level set method along with a signed distance function and is compared with the watershed segmentation which we have already implemented before on a different dataset (Hashia, Mir, 2014). In order to check the efficacy of the algorithm it is again implemented in this paper on the sagittal T2-weighted MR images of the spine. It can be seen that both these methods can become very much valuable to help the radiologists with the on time segmentation of the vertebral bodies as well as of the intervertebral disks with relatively much less effort. They both are later compared with the golden standard using dice and jaccard coefficients.

This work focuses on text dependent speaker verification system where a source feature specifically residual Mel frequency cepstral coefficients (RMFCC), has been extracted in addition to a vocal tract system feature namely Mel frequency cepstral coefficients (MFCC). The RMFCC features are derived from the LP residuals whereas MFCC features are derived from the cepstral analysis of the speech signal. Thus, these two features have different information about the speaker. A four cohort speaker’s set has been prepared using these two features and dynamic time warping (DTW) is used as the classifier. Performance comparison of the text dependent speaker verification model using MFCC and RMFCC features are enumerated. Experimental results shows that, using RMFCC feature alone do not give satisfactory results in comparison to MFCC. Also, the system’s performance obtained using the MFCC features, is not optimum. So, to improve the performance of the system, these two features are combined together using different combination algorithms. The proposed lowest ranking method yields good performance with an equal error rate (EER) of 7.50%. To further improve the efficiency of the system, the proposed method is combined along with the strength voting and weighted ranking method in the hierarchical combination method to obtain an EER of 3.75%. 

Digital Image Watermarking is a process of embedding a known data into an Image. Several techniques are developed to embed a watermark into a known cover image. Digital image watermarking provides security like copyright protection, ownership, and authentication to the images. In this paper, a new robust image watermarking and the watermark extraction algorithm is proposed using DWT-FWHT transformation. The watermarking algorithm further calculates the peak-signal to noise ratio(PSNR) values on the selected images and the extraction process involves the process of correlating the extracted watermark with the original watermark for various sub-bands of discrete wavelet transformation. The digital image watermarking algorithms using discrete wavelet transformation have been identified to be more prevalent as compared to those with the other watermarking algorithms. This is due to the wavelets high spatial localization, frequency spread, and multi-resolution characteristic features which are much similar to that of the theoretical models of the human visual system.

We present a new technique for content based image retrieval by deriving a Local motif pattern (LMP) code co-occurrence matrix (LMP-CM). This paper divides the image into 2 x 2 grids. On each 2 x 2 grid two different Peano scan motif (PSM) indexes are derived, one is initiated from top left most pixel and the other is initiated from bottom right most pixel. From these two different PSM indexes, this paper derived a unique LMP code for each 2 x 2 grid, ranges from 0 to 35. Each PSM minimizes the local gradient while traversing the 2 x 2 grid. A co-occurrence matrix is derived on LMP code and Grey level co-occurrence features are derived for efficient image retrieval. This paper is an extension of our previous MMCM approach [54]. Experimental results on popular databases reveal an improvement in retrieval rate than existing methods.