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Start Over Topic computer science Topic image processing Publication Year Range Last 3 years Journal mathematical problems in engineering
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1. Boundedness of Convex Polytopes Networks via Local Fractional Metric Dimension.

Author

Javaid, Muhammad, Zafar, Hassan, Aljaedi, Amer, and Alanazi, Abdulaziz Mohammad

Subjects
*INTEGER programming, *IMAGE processing, *COMPUTER science, *POLYTOPES, *CHEMICAL properties, *NEIGHBORHOODS
Abstract

Metric dimension is one of the distance-based parameter which is frequently used to study the structural and chemical properties of the different networks in the various fields of computer science and chemistry such as image processing, pattern recognition, navigation, integer programming, optimal transportation models, and drugs discovery. In particular, it is used to find the locations of robots with respect to shortest distance among the destinations, minimum consumption of time, and lesser number of the utilized nodes and to characterize the chemical compounds having unique presentation in molecular networks. The fractional metric dimension being a latest developed weighted version of the metric dimension is used in the distance-related problems of the aforementioned fields to find their nonintegral optimal solutions. In this paper, we have formulated the local resolving neighborhoods with their cardinalities for all the edges of the convex polytopes networks to compute their local fractional metric dimensions in the form of exact values and sharp bounds. Moreover, the boundedness of all the obtained results is also proved. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Elementary Methods for Generating Three-Dimensional Coordinate Estimation and Image Reconstruction from Series of Two-Dimensional Images

Author

Debabrata Samanta, Mehedi Masud, Naived George Eapen, Jehad F. Al-Amri, and Manjit Kaur

Subjects
Article Subject, Computer science, business.industry, General Mathematics, General Engineering, Cognitive neuroscience of visual object recognition, Image processing, Iterative reconstruction, Virtual reality, Engineering (General). Civil engineering (General), Object (computer science), QA1-939, Computer vision, Augmented reality, Artificial intelligence, TA1-2040, business, Pose, Mathematics, Heap (data structure)
Abstract

The increase in computational power in recent years has opened a new door for image processing techniques. Three-dimensional object recognition, identification, pose estimation, and mapping are becoming popular. The need for real-world objects to be mapped into three-dimensional spatial representation is greatly increasing, especially considering the heap jump we obtained in the past decade in virtual reality and augmented reality. This paper discusses an algorithm to convert an array of captured images into estimated 3D coordinates of their external mappings. Elementary methods for generating three-dimensional models are also discussed. This framework will help the community in estimating three-dimensional coordinates of a convex-shaped object from a series of two-dimension images. The built model could be further processed for increasing the resemblance of the input object in terms of its shapes, contour, and texture.

Published
2021