Informatics Publishing Limited

S. Lakshmi Devi ¹, S. Nithyanandam ², K. Sangeetha ³

Affiliations
1 Ponnaiyah Ramajayam College of Engineering & Technology/Department of CSE, Thanjavur, IN
2 Ponnaiyah Ramajayam Engineering College/Department of CSE, Thanjavur, IN
3 Ponnaiyah Ramajayam Engineering College/Department of PG CS, Thanjavur, IN

Abstract

DSMS need to control its resources adaptively as stream characteristics and its query workload vary over time. To analysis an approach to adaptive resource management to adjusts window sizes and time granularities for continuous sliding window queries to keep resource usage within limits. In order to quantify the impact of the two techniques on a query plan, to evaluating the cost based system for the resource allocation. An throughout the experimental analyzes to demonstrate the effectiveness, scalability and accuracy of the cost based system.

Keywords

DSMS, Window Size, Sliding Window, Time Granularity.

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S. Nithyanandam ¹, M. Dharan Chandar ¹

Affiliations
1 Department of Computer Science and Engineering, PRIST University, Thanjavur, IN

Abstract

Camera calibration has been studied extensively in computer vision and photogrammetry. A camera calibration scheme that uses a simple reference object is superior in real environments, as it yields systems that can respond quickly to various camera configurations in indoors and outdoors. In this work a new calibration technique using 1D objects (points aligned on a line), and filling the missing dimension in calibration is proposed. A closed-form solution is developed if five observations of such a 1D object are made a simple and practical calibration technique is proposed that effectively estimates camera parameters from just five points on two orthogonal 1-D objects, each has three collinear points, one of which is shared. The basic equations needed to realize camera calibration are derived from just five points observed on a single image that captures the objects. A new camera calibration algorithm that estimates the camera parameters based on the basic equations (Closed-form solution) and optimizes them by the Bundle Adjustment Technique (BAT).

Keywords

Camera Calibration, Photogrammetry, Reference Object, Closed-Form Solution, Bundle Adjustment Technique.

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B. Bharathikannan ¹, S. Nithyanandam ²

Affiliations
1 Kings College of Engineering, IN
2 Prist University, IN

Abstract

We propose a Classical method for image expansion such as bilinear interpolation or splines can be understood as linear filtering operations on a given image. This interpolator that is trained in advance with training data, based on sparse Bayesian estimation for determining the optimal and compact support for efficient image expansion. In our framework, the interpolator expands the image by replacing each pixel in the given low-resolution image by an (r x r) high-resolution image patch. Since estimating pixel values is impossible from only one pixel value, we use the low-resolution pixel patch surrounding the pixel to be replaced. This local interpolation is repeated for every pixel in the given image, and the expanded image is constructed by fitting the high-resolution patches. The compactness of the support is beneficial when we want a fast and high-quality image interpolator, especially when we apply it in small embedded systems such as digital cameras and mobile phones. In this paper, we aim to resolve the tradeoff between high quality and low cost. thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel. This is done by formulating the kernel learning problem in SVR form as a convex optimization problem, specifically a semi-definite programming (SDP) problem.

Keywords

Kernel Matrix, Nonlinear Regression, Resolution Synthesis, Superresolution, Support Vector Regression (SVR), Sparse Bayesian Method.

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