In this paper we propose a highly accurate method of automatically estimation of the Gaussian noise level in digital images, which is based on image filtering and analysis of the region of interest. Noise level is an important parameter to many digital image processing applications, for example, when removing noise. As the noise level its standard deviation is calculated.  The selection of the noise component in an image is performed by high-pass filtration, where the Laplacian difference is used as the filter kernel. Based on the noise component of the image, regions of interest with homogeneous areas of the image are calculated. Region of interest are selected by the iterative method using low-pass filtration, where Gaussian two-dimensional function is used as the filter kernel. The noise level is calculated only in the regions of interest that contain almost no edges and textures, because edges and textures cause errors during the noise level estimation. In order to improve the accuracy of the method, edges of images are detected and out of region of interest. The high accuracy of the proposed method provides the use of high-pass and low-pass filtrations, iterative selection of region of interest and analysis of image edges. The accuracy of the developed method has been tested on the processing of 100 test images with different levels of software added Gaussian noise, as well as the processing of real photos with noise. The proposed method for the noise level estimation can be used for optimal automatic image filtering and for assessing the quality of photosensitive sensors.

To obtain solar energy from solar photo-voltaic panels is not a new task now a days. Solar panels are designed to give their maximum possible output when exposed to the solar radiations. The operating conditions of solar panels affected by the atmospheric conditions like dust, dirt, solar INSOLATION, temperature etc. Here the effect of cement and bird deposits on the surface of solar panels is considered. Temperature rise due to cement and bird deposits develop hot-spots and affects the electrical power output of the solar panel. The cement and bird deposits can be seen by visually on solar panel surface but their effect can not be visualized by naked eye. The infrared THERMOGRAPHY helps to analyze these effects by using thermal imaging camera. This paper focus to study the temperature effects of cement and bird deposition on solar panel surface by capturing thermal images along with analyzing the thermal images using MATLAB digital image processing for better understanding. Image segmentation for cement and bird deposits is performed using watershed transform to achieve the desired region of interest from thermal images.

In recent years, near threshold computing is becoming a promising solution to achieve minimum energy consumption. In this paper, the Dynamic Threshold body MOS (DTMOS) technique is assessed in the context of 10T full subtractor circuit designed to operate in the near threshold region. The performance parameters – Energy, power, area, delay, and EDP were computed and compared with the conventional CMOS (C-CMOS) Full subtractor. The simulations were performed using cadence 90 nm technology with Ultra Low Voltage (ULV) of 0.3V. The results have been shown that the proposed 10T full subtractor circuit with DTMOS scheme achieves more than 18% savings in delay, 26% savings in energy consumption and 39% savings in EDP in comparison with the conventional CMOS configuration and other hybrid counterparts.

In this paper, an enhancement based traffic video has been proposed in the state of the art of computer vision. The main target is to develop a decision making criteria for removing the most probable video degradations. Such traffic video degradations would have an adverse impact on the transportation system. In order to establish the appropriate analysis, three types of video degradations have been added to the test video; salt and pepper noise, Gaussian noise, and speckle noise, we have simulated rainy, fog, and darkness conditions for the traffic video. First of all, back ground subtraction and Kalman filter techniques have been used for detecting and tracking vehicles respectively. By using such algorithms, it would be easily to estimate average number of assigned tracks which express the efficacy of correct detection and prediction of vehicles in each frame.  Furthermore, video degradations would be applied in order to studying its effect on the average number of assigned tracks which would be deviated than noiseless video. Spatial filtering system has been applied to state the most suitable filter mask which satisfy the least deviation in the average number of assigned tracks. Experimental results show that median filter satisfies the least deviation in all cases of video degradations.

Knee Osteoarthritis is most ordinary kind of joint inflammation, which often occurs in one or both the knee joints. Osteoarthritis is additionally called as 'wear and tear' process of joint that results in dynamic disintegration of articular cartilage. Cartilage is smooth substantial layer that ensures movement to occur effortlessly. In Osteoarthritis, the cartilage is inclined towards the destruction as it loses elasticity and becomes brittle.
Osteoarthritis is regularly investigated from radiographic evaluation after clinical examination. In any case, a visual evaluation made by the restorative physician depends on experience that varies subjectively and is profoundly reliant on their experience. Subsequently, in order to make diagnostic process more systematic and reliable, evolution of imaging based analysis for early recognition of Osteoarthritis is required. The objective of this study is to develop a machine vision approach for investigation of Knee Osteoarthritis using region based and active shape model. The computation involves histogram of oriented gradient (HOG) method. The processed HOG elements are computed using multiclass SVM for evaluating Osteoarthritis based on Kellgren and Lawrence (KL) grading system. The classification rate of 97.96% for Grade-0, 92.85% for Grade-1, 86.20% for Grade-2, 100% for Grade-3 & Grade-4 is obtained. The results are promising and competitive which are validated by the medical experts.

In this work, we propose and evaluate different scene based methods for non-uniformity corrections for optical remote sensing data sets. These methods can be used to correct or refine the existing radiometric calibrations, thereby improving the image quality. The performance of each algorithm against different datasets are analyzed and a quantitative comparison of different quality parameters viz. entropy, correlation coefficient, signal to noise ratio, peak signal to noise ratio and structural similarity index are carried out to recommend the best method for each scene.  For a given data set, the selected method depends on the severity, type of terrain it covered, etc.

An enormous amount of information in the form of image and video are dispersed all over the world like any other data therefore, retrieval of a query image from a large database of images is an important undertaking in the area of computer vision and image processing. The traditional text-based approaches for searching images are slow and inefficient. Content-based image retrieval (CBIR) provides the solution for efficient retrieval of the image from these image databases. In this paper, an efficient CBIR system is proposed using various colour and texture features. Colour features such as Colour Moments and HSV Histogram and Texture Features like Local Binary Patterns (LBP) are used. Various distance metrics are analysed for retrieval and their performance is compared to get the best distance metric for better retrieval performance. From the experimental analyses on benchmark (WANG) database, it is observed that the City block distance performs consistently encouraging from other measures. Also this paper has introduced the combination of HSV and LBP histogram and evaluated the retrieval performance. The obtained results are very promising than other variants of colour and texture features.