Image feature extraction is an important key in success of Content Based Image Retrieval (CBIR) systems . The low level visual features of an image can be extracted from color, texture & shape of the image. These features can be used during retrieval to compare query image and other images in the database. This paper describes the method for extraction of color & texture features of diseased leaves of maize. Color features are extracted by computing first, second & third order moments of HSV histogram of an image. The textures features like correlation, energy, inertia & homogeneity are obtained by computing gray level co-occurrence matrix of an image.

Different types of music evoke different feelings and emotions. Certain sounds produce joy, others grief and yet others affection and tenderness. According to Indian aesthetics, each poem or musical composition produces a certain rasa (emotion). Local Binary Patterns (LBP) have been well exploited for facial image analysis in the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. In this review paper we propose LBP Histogram (LBP) bins for the task of facial expression recognition while listening to Indian classical ragas. Our experiments illustrate that the selected LBP bins provide a compact and discriminative facial expression representation. The selected LBP bins will be used to obtain the best recognition performance rate on collected database. The local binary pattern (LBP) operator is defined as a gray-scale invariant texture measure, derived from a general definition of texture in a local neighborhood. Due to its discriminative power and computational simplicity, the LBP texture operator has become a popular approach in various applications, including visual inspection, image retrieval, remote sensing, biomedical image analysis, motion analysis, environment modeling, and outdoor scene analysis .

The most common method used for authentication is Textual passwords. But textual passwords are vulnerable to eves dropping, dictionary attacks, social engineering and shoulder surfing. Graphical passwords are introduced as alternative techniques to textual passwords. Most of the graphical schemes are vulnerable to shoulder surfing. To address this problem, text can be combined with images or colors to generate session passwords for authentication. Session passwords can be used only once and every time a new password is generated. In this paper, we proposed a scheme related to the grayscale images which will be advantageous as compared to many of the well known formats. Here we are going to use Visual secret sharing (VSS) scheme for security of grayscale images And to generate session passwords using text, colors and grayscale images. In this paper, two new authentication schemes are proposed. These schemes authenticate the user by session passwords which are used only once. Once the session is terminated, the session password is no longer useful. For every login process, users input different passwords. The session passwords provide better security against dictionary and brute force attacks as password changes for every session. The proposed authentication schemes use text and colors for generating session passwords.

In areas such as computer vision and mage processing, image segmentation has been and still is a relevant research area due to its wide spread usage and application. This paper provides a survey of achievements, problems being encountered, and the open issues in the research area of image segmentation and usage of the techniques in different areas.. We considered the techniques under the following three groups: Threshold-based, Edge-based and Region-based. Region Growing is an approach to image segmentation in which neighboring pixels are examined and added to a region class if no edges are detected. This process is iterated for each boundary pixel in the region. If adjacent regions are found, a region-merging algorithm is used in which weak edges are dissolved and strong edges are left in tact. Region Growing offers several advantages over conventional segmentation techniques. Unlike gradient and Laplacian methods, the borders of regions found by region growing are perfectly thin (since we only add pixels to the exterior of our region) and connected. The algorithm is also very stable with respect to noise.

This paper provides an up-to-date critical survey of enhancement of infrared images by two popular algorithms namely spatial and spatiotemporal homomorphic filtering (SHF and STHF) based upon a infrared imaging model. Although spatiotemporal homomorphic filtering may reduce the number of iterations greatly in comparison to spatial one for a similar degree of convergence by making explicit use of the additional information provided temporally, the enhanced results from SHF are in general better than those from STHF. To provide this comprehensive survey we have not only focused on concept of infrared image but also two enhancement techniques are presented and discussed along with their advantages and disadvantages .

The measurement of instantaneous solar radiation and daily ambient temperature in Chandrapur, India (latitude 20.060N’ and longitude 79.30E) is presented. The measurements covered one year from month of 1st July 2010 to 30th June 2011. The data were used to obtain the average monthly output, yearly average output and the average solar radiation. The polycrystalline PV module is considered for this study. Further, possible plant capacity is estimated for an arbitrary chosen area. The results supported justified the method proposed.