Informatics Publishing Limited

N. Kannaiya Raja ¹, K. Arulanandam ², R. Radha Krishnan ³, M. Nataraj ⁴

Affiliations
1 Department of CSE, Arulmigu Meenakshi Amman College of Engg., Thiruvannamalai Dt., Near Kanchipuram, IN
2 Department of CSE, Ganadipathy Tulsi's Jain Engineering College, Vellore, IN
3 Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, IN
4 Department of Computer Science and Engineering, Anna University, IN

Abstract

In this research, we consider the problems of registering multiple video sequences dynamic scenes which are not limited non rigid objects such as fireworks, blasting, high speed car moving taken from different vantage points. In this paper we propose a simple algorithm we can create different frames on particular videos moving for matching such complex scenes. Our algorithm does not require the cameras to be synchronized, and is not based on frame-by-frame or volume-by-volume registration. Instead, we model each video as the output of a linear dynamical system and transform the task of registering the video sequences to that of registering the parameters of the corresponding dynamical models. In this paper we use of a joint frame together to form distinct frame concurrently. The joint identification and the Jordan canonical form are not only applicable to the case of registering video sequences, but also to the entire genre of algorithms based on the dynamic texture model. We have also shown that out of all the possible choices for the method of identification and canonical form, the JID using JCF performs the best.

Keywords

Dynamic Textures, Video Registration, Nonrigid Dynamical Scenes.

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C. Lakshmi Devasena ¹, R. Radha Krishnan ², M. Hemalatha ³

Affiliations
1 Karpagam University, Coimbatore-21, IN
2 Park School of Aeronautical Sciences, Kaniyur, Coimbatore, IN
3 Department of Software System, Karpagam University, Coimbatore-21, IN

Abstract

Image mining is not mere an extension of data mining to image domain. Image mining is a technique normally used to extract knowledge and recognize objects directly from images. Image segmentation will normally be the first step in image mining. Image segmentation is difficult, but it is important problem in computer vision and machine perception. We can treat image segmentation as graph partitioning problem. The minimum spanning tree algorithm is capable of detecting clusters with irregular boundaries to mine images. This paper proposes the minimum spanning tree based clustering algorithm to detect color images using weighted Euclidean distance for edges, which is key element in building the graph from image. The algorithm produces n clusters with segments. An important characteristic of this method is its capacity to conserve information in low variability image regions while omitting detail in high-variability regions. The proposed algorithm has been employed using MATLAB. The implemented system produces promising results.

Keywords

Clustering, Color Images, Graph Partitioning, Image Mining, Image Segmentation, Weighted Euclidean Minimum Spanning Tree.

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