Madhuri Kota

Bio: Madhuri Kota is an academic researcher from VIT University. The author has contributed to research in topics: Pipeline transport. The author has an hindex of 1, co-authored 2 publications receiving 7 citations.

Automated Assessment Tool for the Depth of Pipe Deterioration

Abstract: Defects in underground pipeline images are indicative of the condition of buried infrastructures like sewers and water mains. This paper entitled Automated Assessment Tool for the depth of pipe deterioration presents a three step method which is a simple, robust and efficient one to detect defects in the underground concrete pipes. It identifies and extracts defect-like structures from pipe images whose contrast has been enhanced. We propose to use segmentation and feature extraction using structural elements. The main objective behind using this tool is to find the dimensions of the defect such as the length, width and depth and also the type of defect. The detection of defects in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal operators. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousands of miles of pipeline because of fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the defects in underground pipes.

Automated Assessment Tool for the Depth of Pipe Deterioration.

Abstract: Defects in underground pipeline images are indicative of the condition of buried infrastructures like sewers and water mains. This paper entitled Automated Assessment Tool for the depth of pipe deterioration presents a three step method which is a simple, robust and efficient one to detect defects in the underground concrete pipes. It identifies and extracts defect-like structures from pipe images whose contrast has been enhanced. We propose to use segmentation and feature extraction using structural elements. The main objective behind using this tool is to find the dimensions of the defect such as the length, width and depth and also the type of defect. The detection of defects in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal operators. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousands of miles of pipeline because of fatigue, subjectivity and cost. Our objective is to reduce the effort and the labour of a person in detecting the defects in underground pipes.

A comparative study of RIFCM with other related algorithms from their suitability in analysis of satellite images using other supporting techniques

Abstract: Purpose – The purpose of this paper is to provide a way to analyze satellite images using various clustering algorithms and refined bitplane methods with other supporting techniques to prove the superiority of RIFCM. Design/methodology/approach – A comparative study has been carried out using RIFCM with other related algorithms from their suitability in analysis of satellite images with other supporting techniques which segments the images for further process for the benefit of societal problems. Four images were selected dealing with hills, freshwater, freshwatervally and drought satellite images. Findings – The superiority of the proposed algorithm, RIFCM with refined bitplane towards other clustering techniques with other supporting methods clustering, has been found and as such the comparison, has been made by applying four metrics (Otsu (Max-Min), PSNR and RMSE (40%-60%-Min-Max), histogram analysis (Max-Max), DB index and D index (Max-Min)) and proved that the RIFCM algorithm with refined bitplane yi...

Depth Reconstruction using Geometric Correction with Anaglyph Approach for Satellite Imagery

Abstract: The Images obtained through remote sensing systems are not often sufficient for high precision applications due to various distortions. The distortions can be due to errors like geometric errors, etc. Also multi-date satellite images of the same area under different conditions are difficult to compare because of change in atmospheric propagation, sensor response and illuminations. Keeping these points in view, in this paper, we deal with the first phase of pre-processing and we make the satellite images free from such errors and use clustering techniques With Geometric Correction (WGC) and Without Geometric Correction (WOGC) applied to the satellite images using our proposed algorithm. Finally, the image is reconstructed with depth dimension/depth map generation for the anaglyph image for better interpretation of satellite imagery. We have made experimental analysis of our algorithm using suitable satellite images and found the results to be very encouraging.

Automated assessment of buried pipeline defects by image processing

Abstract: Many underground water pipelines are old and approaching their service lives in a great number of cities. With the promotion of sustaining buried infrastructure, it's necessary to pay much attention on how to effectively extract defect characteristics of damaged pipelines. Detection of defects in underground pipes is a crucial step to assess the deterioration degree of pipeline for municipal operators. Based on the image processing theory, a defect feature extracting method under HSV color space is proposed in this paper. QFCM (Quick Fuzzy C-Mean clustering) segmentation arithmetic is applied to extract characteristics parameters. The proposed algorithm can identify defects from background, and the types of defects in the buried pipes can be categorized in the estimation stage. Then, different methodologies of parameters extraction are applied in different types of pipe defects, features like area, angle, length and width of defects can also be calculated. And then, a method of assessing the accuracy of feature extraction algorithm is discussed. Finally, the proposed detection approach has been experimentally tested using a group of images acquired by CCD camera from real inspection scenarios. The experimental results proved that it is feasible and effective to apply the system in feature extraction of pipe defects of the underground water-pipelines.

A Comparative Analysis of Depth Computation of Leukaemia Images using a Refined Bit Plane and Uncertainty Based Clustering Techniques

Abstract: Several image segmentation techniques have been developed over the years to analyze the characteristics of images. Among these, the uncertainty based approaches and their hybrids have been found to be more efficient than the conventional and individual ones. Very recently, a hybrid clustering algorithm, called Rough Intuitionistic Fuzzy C-Means RIFCM was proposed by the authors and proved to be more efficient than the conventional and other algorithms applied in this direction, using various datasets. Besides, in order to remove noise from the images, a Refined Bit Plane RBP algorithm was introduced by us. In this paper we use a combination of the RBP and RIFCM to propose an approach and apply it to leukemia images. The aim of the paper is twofold. First, it establishes the superiority of our approach in medical diagnosis in comparison to most of the conventional, as well as uncertainty based approaches. The other objective is to provide a computer-aided diagnosis system that will assist the doctors in evaluating medical images in general, and also in easy and better assessment of the disease in leukaemia patients. We have applied several measures like DB-index, D-index, RMSE, PSNR, time estimation in depth computation and histogram analysis to support our conclusions.