Disparity Map Generation from Illumination Variant Stereo Images Using Efficient Hierarchical Dynamic Programming

A novel hierarchical stereo matching algorithm is presented which gives disparity map as output from illumination variant stereo pair. Illumination difference between two stereo images can lead to undesirable output. Stereo image pair often experience illumination variations due to many factors like real and practical situation, spatially and temporally separated camera positions, environmental illumination fluctuation, and the change in the strength or position of the light sources. Window matching and dynamic programming techniques are employed for disparity map estimation. Good quality disparity map is obtained with the optimized path. Homomorphic filtering is used as a preprocessing step to lessen illumination variation between the stereo images. Anisotropic diffusion is used to refine disparity map to give high quality disparity map as a final output. The robust performance of the proposed approach is suitable for real life circumstances where there will be always illumination variation between the images. The matching is carried out in a sequence of images representing the same scene, however in different resolutions. The hierarchical approach adopted decreases the computation time of the stereo matching problem. This algorithm can be helpful in applications like robot navigation, extraction of information from aerial surveys, 3D scene reconstruction, and military and security applications. Similarity measure SAD is often sensitive to illumination variation. It produces unacceptable disparity map results for illumination variant left and right images. Experimental results show that our proposed algorithm produces quality disparity maps for both wide range of illumination variant and invariant stereo image pair

Multiobjective Based Resource Allocation and Scheduling for Postdisaster Management Using IoT

Disaster is an uncertain phenomenon that arises due to natural as well as man-made calamities. Disaster often causes a high degree of destruction, especially in a very densely populated region. To handle such a situation, efficient resource management strategies are required. Resource management is the most crucial phase of disaster management. Efficient and in-time allocation of resources is very important; otherwise, it may result in more fatalities. In this context, we propose the resource management algorithm, which deals with both over- and underdemand for resources. Resource management requires efficient resource allocation, and in case of overdemand for resources, it must be followed by resource scheduling. In this paper, we introduce a resource allocation technique which is based on multiple objectives having a different set of constraints. We also propose the resource scheduling algorithm based on various parameters. The proposed algorithm uses multiobjective theory for resource allocation which is followed by the implementation of priority-based scheduling technique, in the case of overdemand for resources. Our proposed methods are compared to the existing approaches in the literature. From the simulation results, it is clear that our methods perform optimum resource allocation and scheduling operations

Resource Scheduling for Postdisaster Management in IoT Environment

For postdisaster management, rescue and recovery operations are very critical. It is desired that the rescue and recovery operation should be handled through efficient resource management to minimize the postdisaster effects in terms of human loss and other types of damage. Resource management requires addressing various challenging issues like scheduling and monitoring of the resources which need real time information of various activities or events occurring anytime, anywhere, and anyplace. To satisfy such requirements, Internet of Things, an advanced upcoming technology, can be utilized for resource monitoring and scheduling. In this context, we propose resource scheduling algorithm for the postdisaster management. As mentioned above various tasks of rescue and recovery operation should be carried out with different priority and there should not be deadlock while availing the resources. In our approach, we estimate the waiting time using queuing theory for the availability of the resources for different activities that are to be performed at various locations. The simulation results of the proposed method are analyzed using different standard parameters like resource utilization and the waiting time for different activities. The proposed method is further visualized through real time annotation of resources and activities represented with the help of Google maps using android based application on the smartphone. The proposed algorithm is further compared in terms of computational complexity and fairness analysis for the effective utilization of the available resources

Clustering Approaches for Pragmatic Two-Layer IoT Architecture

Connecting all devices through Internet is now practical due to Internet of Things. IoT assures numerous applications in everyday life of common people, government bodies, business, and society as a whole. Collaboration among the devices in IoT to bring various applications in the real world is a challenging task. In this context, we introduce an application-based two-layer architectural framework for IoT which consists of sensing layer and IoT layer. For any real-time application, sensing devices play an important role. Both these layers are required for accomplishing IoT-based applications. The success of any IoT-based application relies on efficient communication and utilization of the devices and data acquired by the devices at both layers. The grouping of these devices helps to achieve the same, which leads to formation of cluster of devices at various levels. The clustering helps not only in collaboration but also in prolonging overall network lifetime. In this paper, we propose two clustering algorithms based on heuristic and graph, respectively. The proposed clustering approaches are evaluated on IoT platform using standard parameters and compared with different approaches reported in literature

Semisupervised Learning Based Opinion Summarization and Classification for Online Product Reviews

The growth of E-commerce has led to the invention of several websites that market and sell products as well as allow users to post reviews. It is typical for an online buyer to refer to these reviews before making a buying decision. Hence, automatic summarization of users’ reviews has a great commercial significance. However, since the product reviews are written by nonexperts in an unstructured, natural language text, the task of summarizing them is challenging. This paper presents a semisupervised approach for mining online user reviews to generate comparative feature-based statistical summaries that can guide a user in making an online purchase. It includes various phases like preprocessing and feature extraction and pruning followed by feature-based opinion summarization and overall opinion sentiment classification. Empirical studies indicate that the approach used in the paper can identify opinionated sentences from blog reviews with a high average precision of 91% and can classify the polarity of the reviews with a good average accuracy of 86%

Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification