Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Rashno, Abdolreza; Fadaei, Sadegh

doi:10.5829/ije.2023.36.02b.18

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…Frame-based methods assume that the video frames contain sufficient information to reconstruct the document content and typically consist of three steps: frame selection, frame rectification, and frame stitching (14). Brown et al (15) proposed a minimal solution for panoramic stitching based on homography estimation.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancing Book and Document Digitization from Videos: A Feature Fusion-Based Approach

Buddhawar,

Jariwala,

Chattopadhyay

2024

IJE

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In an age where preserving knowledge and information from books and documents is crucial, traditional manual scanning methods are tedious and error-prone. It involves a lot of human intervention and, as a result, sometimes results in erroneous digitization, which makes the downstream tasks, such as optical character recognition, difficult. Therefore, innovative techniques are required to be proposed that not only reduce human effort in terms of digitization but also give highly accurate results over the recently proposed state-of-the-art techniques. We proposed a novel computer vision-based algorithm that combines Gray-Level Co-occurrence Matrix (GLCM) features with Thepade's 10-ary texture features (TSBTC) for video frame classification. This hybrid approach significantly enhances frame selection accuracy, ensures high-quality digitization, and accommodates multiple languages and document types. We also proposed a dataset of 54,000 diverse images to demonstrate our algorithm's effectiveness in realworld scenarios and compare it to existing methods, making a valuable contribution to document digitization. The proposed dataset can be utilized for several document image analysis tasks.

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Section: Literature Surveymentioning

confidence: 99%

Enhancing Book and Document Digitization from Videos: A Feature Fusion-Based Approach

Buddhawar,

Jariwala,

Chattopadhyay

2024

IJE

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“…Among them, evolutionary algorithms have attracted more considerations for linear/non-linear, convex/non-convex and constrained/non-constrained problems (8)(9)(10). In which, the Genetic Algorithm (GA) (11) and Particle Swarm Optimization (PSO) (12) as the most prominent and wellknown approaches have been broadly implemented in the scientific research studies (13)(14)(15).…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Mahmoodabadi,

Nemati,

Danesh

2024

IJE

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To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

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“…To overcome this limitation, [ 13 ] utilizes majorization-minimization algorithm which enhances the image smoothing and protect the necessary information present in the image however it lacks in computation complexity. This can be improved in [ 14 ], author adopted swarm particle optimization for image restoration as its known this bio inspired model reduces the computation. This model finds the common point of image to project the convex set.…”

Section: Introductionmentioning

confidence: 99%

Convex-based lightweight feature descriptor for Augmented Reality Tracking

S.,

Clement J.

2024

PLoS ONE

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Feature description is a critical task in Augmented Reality Tracking. This article introduces a Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting, and blur variations while remaining computationally efficient. We have developed two filters capable of computing pixel intensity variations, followed by the covariance matrix of the polynomial to describe the features. The superiority of CBFD is validated through precision, recall, computation time, and feature location distance. Additionally, we provide a solution to determine the optimal block size for describing nonlinear regions, thereby enhancing resolution. The results demonstrate that CBFD achieves a average precision of 0.97 for the test image, outperforming Superpoint, Directional Intensified Tertiary Filtering (DITF), Binary Robust Independent Elementary Features (BRIEF), Binary Robust Invariant Scalable Keypoints (BRISK), Speeded Up Robust Features (SURF), and Scale Invariant Feature Transform (SIFT), which achieve scores of 0.95, 0.92, 0.72, 0.66, 0.63 and 0.50 respectively. Noteworthy is CBFD’s recall value of 0.87 representing at the maximum of a 13.6% improvement over Superpoint, DITF, BRIEF, BRISK, SURF, and SIFT. Furthermore, the matching score for the test image is 0.975. The computation time for CBFD is 2.8 ms, which is at least 6.7% lower than that of other algorithms. Finally, the plot of location feature distance illustrates that CBFD exhibits minimal distance compared to DITF and Histogram of Oriented Gradients (HOG). These results highlight the speed and robustness of CBFD across various transformations.

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Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Cited by 6 publications

References 31 publications

Enhancing Book and Document Digitization from Videos: A Feature Fusion-Based Approach

Enhancing Book and Document Digitization from Videos: A Feature Fusion-Based Approach

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Convex-based lightweight feature descriptor for Augmented Reality Tracking

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