Showing papers by "V. N. Manjunath Aradhya published in 2013"

An Application of K-Means Clustering for Improving Video Text Detection

Abstract: In the present work, we explore an extensive applications of Gabor filter and K-means clustering algorithm in detection of text in an unconstrained complex background and regular images. The system is a comprehensive of four stages: In the first stage, combination of wavelet transforms and Gabor filter is applied to extract sharpened edges and textural features of a given input image. In the second stage, the resultant Gabor output image is grouped into three clusters to classify the background, foreground and the true text pixels using K-means clustering algorithm. In the third stage of the system, morphological operations are performed to obtain connected components, then after a concept of linked list approach is in turn used to build a true text line sequence. In the final stage, wavelet entropy is imposed on an each connected component sequence, in order to determine the true text region of an input image. Experiments are conducted on 101 video images and on standard ICDAR 2003 database. The proposed method is evaluated by testing the 101 video images as well with the ICDAR 2003 database. Experimental results show that the proposed method is able to detect a text of different size, complex background and contrast. Withal, the system performance outreaches the existing method in terms of detection accuracy.

An Evaluation of Neural Networks Approaches used for Software Effort Estimation

Abstract: Demand for software is increasing day by day due to its more usage in IT industries. It is a mega challenge for software industry to develop very high quality software effectively within stipulated time and budget. To accomplish this challenge, the software development process needs to be effectively managed and well planned. It is very important to have good effort estimation in order to manage well budget. This paper aims to highlight the application of data mining techniques that yields high software effort estimation accuracy in contrast with other well established effort estimation models using little features. The effect of the proposed work discussed in this paper consisting of several steps. These steps can provide cost-saving project effort estimation means to identify and select only relevant and necessary features. By applying data mining techniques project managers and experts can consume less time to predict software project effort and more time on more important issues in releasing the project in time to customers. Further, this work illustrates the advantages like less computation time and effort resulting in energy saving mandates that can be easily adapted in software development process.

A Novel Full-Reference Color Image Quality Assessment Based on Energy Computation in the Wavelet Domain

Abstract: Abstract This article presents a novel full-reference (FR) image quality assessment (QA) algorithm by depicting the sub-band characteristics in the wavelet domain. The proposed image quality assessment method is based on energy estimation in the wavelet-transformed image. Image QA is achieved by applying a multilevel wavelet decomposition on both the original and the enhanced image. Next, the wavelet energy (WE) and vector are computed to obtain the percentage of the energy that corresponds to the approximation and the details, respectively. Further, the approximate and detailed energy levels of both the original and the enhanced images are compared to formulate an image quality assessment. Numerous experiments are conducted on a dozen of image enhancement algorithms. The results presented show that the image with poor contrast in the foreground than the background has continuous regular coefficient values. The probability density function for such an image has a relatively lower WE and skewness compared with the background. The proposed scheme not only evaluates the global information of an image but also estimates the fine, detailed changes in an enhanced image. Thus, the proposed metric serves as an objective and effective FR criterion for color image QA. The experimental results presented confirm that the proposed WE metric is an efficient and useful metric for evaluating the quality of the color image enhancement.