Md. Mahbubur Rahman

Unsupervised Natural Image Segmentation Using Mean Histogram Features

A new histogram feature based natural image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. A non parametric Bayesean clustering is employed to make the segmentation framework fully unsupervised where no a priori knowledge about the number and types of regions are required. The performance of the proposed method have been demonstrated by various experiments using images of natural scenes. The experimental results indicates that superior segmentation results can be obtained through the proposed unsupervised natural image segmentation algorithm.

A fully unsupervised color textured image segmentation algorithm using weighted mean histograms features

A new integrated feature distribution-based color textured image segmentation algorithm has been proposed. Two novel histogram-based inherent color texture feature extraction methods have been presented. From the histogram features, mean color texture histogram is calculated. Instead of concatenating the feature channels, a multichannel nonparametric Bayesean clustering is employed for primary segmentation. A region homogeneity-based merging algorithm is used for final segmentation. The proposed feature extraction techniques inherently combine color texture features rather then explicitly extracting it. Use of nonparametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about the number of color texture regions is required. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments using color textured and natural images. The experimental results reveal that superior segmentation results can be obtained through the proposed unsupervised segmentation framework.

A new approach of Extendable Multicast Routing Protocol in Mobile Ad Hoc Networks

Multicasting is a challenging task that facilitates group communication among the nodes using the most efficient strategy to deliver the messages over each link of the network. In spite of significant research achievements in recent years, efficient and extendable multicast routing in Mobile Ad Hoc Networks (MANETs) is still a difficult issue. To enhance performance and to enable scalability we have proposed a domain-based Extendable Multicast Routing Protocol (EMRP) for hierarchical multicasting in MANET environments. In the proposed technique, each domain has a sub-source that reduces the path length between the original source and intended receiver which solute the scalability issue. We have analyzed the performance with respect to a variety of parameters for different mobility speed and group sizes. Results obtained through simulations demonstrate enhanced performance in packet delivery ratio and end-to-end delay of the proposed technique as compared to the existing ones.

A Hybrid Jumping Genes Genetic Algorithm Based Request Scheduling Approach in Multiple Destination Routing

This paper presents a hybrid jumping genes genetic algorithm (HJGGA) for solving the request scheduling problem in multiple destination routing (MDR). The problem incorporates the scheduling and routing process of a set of requests having single source and multiple destinations through a network. Our proposed HJGGA framework, that facilitates intelligent splitting of bandwidth requirement of requests as well as multiple optimal paths for transmission, searches for a near-optimal scheduling solution. We have also developed new chromosome-encoding and mutation technique for our HJGGA. Experimental result shows that our scheme reflects better real world situations and performs superior than previous researches.

An automatic color textured image segmentation algorithm using mean histogram features

A new integrated feature distributions based color textured image segmentation algorithm has been proposed. The proposed scheme uses histogram based new color texture extraction method which inherently combines color texture features rather then explicitly extracting it. Use of non parametric Bayesean clustering makes the segmentation framework fully unsupervised where no a priori knowledge about the number color textures regions are required. The feasibility and effectiveness of the proposed method have been demonstrated by various experiments using color textured images. The experimental results reveal that superior segmentation results can be obtained through the proposed unsupervised segmentation framework.

A new wide-sense nonblocking optical switching network

In this paper, we present a new wide-sense nonblocking optical switching network. The proposed network combines some building blocks, which is All-To-All Personalized Exchange Multistage Interconnection Network. The architectural details and some other parameters are evaluated and compared with other networks to show the superiority of the proposed network over other networks. To construct the proposed network we require less number of switching elements. In addition, it provides constant first order crosstalk, thus it scales well. The proposed network is a good choice for constructing nonblocking optical switch with less switching element and signal loss.