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Wahi, Amitabh
- Theft Detection System Through Thresholding Technique with Background Subtraction Method
Abstract Views :165 |
PDF Views:2
Authors
Affiliations
1 Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore-641042, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN
3 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN
1 Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore-641042, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN
3 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN
Source
Digital Image Processing, Vol 2, No 5 (2010), Pagination: 131-135Abstract
Image background segmentation is one of the most prominent steps in many applications of the image processing. Several algorithms exist for the background segmentation from the dynamic scenes of a video sequence. However, elimination of background from the static images still remains a challenging task. Although trivial background subtraction algorithms can execute quickly, they do not give useful results in most situations. This paper addresses the issue of identifying and segmenting theft images captured from the background regions of the image to alert the user. Objective is to segment foreground object from the background region. An alert signal is produced to enable the user to initiate suitable action.Keywords
Gray Scale Image, Histogram, Intensity Threshold, Segmentation, Theft Detection.- An Efficient Clustering Method in Unlabeled Data Sets Using KMBA Algorithm
Abstract Views :223 |
PDF Views:4
Authors
Affiliations
1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 11 (2013), Pagination: 403-409Abstract
Cluster analysis is one of the primary data analysis methods and K-means algorithm is well known for its efficiency in clustering large data sets. The K-means (KM) algorithm is one of the popular unsupervised learning clustering algorithms for cluster the large datasets but it is sensitive to the selection of initial cluster centroid, and selection of K value is an issue also sometimes it is hard to predict before the number of clusters that would be there in data. There are inefficient and universal methods for the selection of K value, till now we selected that as random value. In this paper, we propose a new metaheuristic method KMBA, the KM and Bat Algorithm (BA) based on the echolocation behavior of bats to identify the initial values for overcome the KM issues. The algorithm does not require the user to give in advance the number of clusters and cluster centre, it resolves the K-means (KM) cluster problem. This method finds the cluster centre which is generated by using the BA, and then it forms the cluster by using the KM. The combination of both KM and BA provides an efficient clustering and achieves higher efficiency. These clusters are formed by the minimal computational resources and time. The experimental result shows that proposed algorithm is better than the existing algorithms.Keywords
Centroid, Clustering, Metaheuristic, BAT Algorithm.- Improving the Cluster Performance by Combining PSO and K-Means Algorithm
Abstract Views :164 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamil Nadu, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Tamil Nadu, IN
1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Tamil Nadu, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Tamil Nadu, IN