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IEEE Final Year Project Topics for CSE

Base Paper Title

JigsawNet: Shredded Image Reassembly Using Convolutional Neural Network and Loop-based Composition

Our Title

IEEE Project Abstract

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge is to reliably compute correct pairwise matching,for which most existing algorithms use handcrafted features,and cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pair wise matches. To improve the network efficiency and accuracy, we transfer the calculation of Convolutional Neural Network to the stitching region and apply a boost training strategy. In the global composition stage, instead of using the widely adopted greedy edge selection strategies, we propose two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially challenging ones with many fragment pieces.

Existing System

Drawback of Existing System

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Advantage of Proposed System

Enhancement from Base Paper

Architecture

Technology Used : Hardware & Software

Existing Algorithm

Proposed Algorithm

Advantages of Proposed Algorithm

Project Modules

Literature Survey

Conclusion

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