An Efficient QBIR System Using Adaptive Segmentation and Multiple Features

AbstractQuery by Image Content Retrieval abbreviated as QBIR, has become new thirst now a days. By using this systems, user can retrieve the similar images of an already existed image (or) a rough sketch (or) a symbolic representation. To make more efficient and user friendly QBIR multiple features areemployed. This paper proposes a novel approach for image retrieval using adaptive k-means clustering and shape, texture features. The experimental results portraystheperformance of the proposed retrieval system in terms of better precision. To evaluate the proposed method COIL and MPEG-7 shape 1 datasets are used

Optimal Path Planning using RRT for Dynamic Obstacles

513-516Rapidly Exploring Random Tree is a technique that utilizes samples as constraints for arranging the paths from given source to reach multiple goals. A sample is randomly selected from the configuration space. Each data point is represented as a step that helps in travelling of a robot from one point to another. By initiating the search process, the pursuit calculation endeavors to achieve the objective of the proposed work, further the position assisted regions are investigated. However, when compared with A* calculation, the proposed method has a more reasonable good performance. The Simulation results demonstrate for reaching the desired goal starting from a source to destination in dynamic environment