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All reviewers have confirmed that the authors have addressed their comments.
[# PeerJ Staff Note - this decision was reviewed and approved by Jyotismita Chaki, a PeerJ Section Editor covering this Section #]
The author has incorporated the comments, it can be accepted.
The author has incorporated the comments, it can be accepted.
The author has incorporated the comments, it can be accepted.
The authors have addressed all the issues raised in my previous report so in my opinion the paper should be accepted for publication.
No comments.
No comments.
No comments.
ok
ok
ok
Nil
Please see the reviewers' detailed comments. The reviewers highlight the need for clearer structure, justification of method choices, quantitative metrics, dataset details, comparisons with baselines, scalability analysis, limitations, and broader impact. They suggest improving readability, adding pseudocode/diagrams, discussing hyperparameters, preprocessing effects, novelty, real-time suitability, and cross-domain generalizability while ensuring uniform formatting and stronger technical rigor.
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1. Provide an analysis of shortcomings in the related work references of prior studies.
2. Specifically mention the novelty of the proposed approach.
3. How the preprocessing affected the produced results.
4. The references should be in a uniform format.
5. Elaborate on the conclusion; it reflects the findings, however, the broader impact of this work should be discussed.
6. Explicitly provide the proper justification for selecting the optimization technique. Alternative optimization techniques exist that can also be considered.
7. The generalizability should be analyzed by testing the model on some other dataset. The cross-domain analysis should be performed.
8. It is unclear which components of the method contributed the most to the results.
9. Explicitly mention the assessment of whether the method is suitable for real-time applications.
This paper proposes an intelligent registration method and is fairly well written. However, the following suggestions can further strengthen it.
A step-by-step explanation is required of how the algorithm computes the input data. The authors can provide a pseudocode or a workflow diagram for clarity.
The results are well-presented, but there is a need to compare them with state-of-the-art techniques.
The authors should provide the details of the characteristics of the dataset, such as size, diversity, etc. Also, provide the justification for selecting the dataset.
There is a need to include the hardware requirements to clarify the feasibility.
Provide a discussion about how the variations in hyperparameters can affect the significance of the model.
Provide the limitations and future improvements related to the proposed approach.
The scalability of the proposed method is unclear. Scalability testing would make the findings more applicable.
The reason for selecting the specific tools or models should be mentioned, such as wavelet transform optimization being used; there is a need to mention the reason for selecting this technique.
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This research proposes an intelligent method for registering multidimensional visual communication projection images, addressing common issues like blurred contours and incomplete shapes after registration. The approach combines wavelet transform for image enhancement, a Laplacian of Gaussian (LOG) operator for edge detection, and Fourier descriptors for contour matching. By applying projection and affine transformations, the method accurately maps images based on matched features. Experimental results demonstrate a high registration accuracy of 99.1% and a low root mean square error (RMSE) of 0.31, showcasing the method’s effectiveness in preserving image clarity and structure during registration, however, following comments incorporation will make it a good paper.
• The abstract is information-heavy and uses long, complicated sentences. Breaking it into clearer logical steps (problem → method → evaluation → results) would significantly improve readability.
• While it mentions issues like "blurred contours" and "incomplete shapes," the abstract does not define the specific challenges of "multidimensional visual communication projection images" clearly enough for a broad academic audience.
• Many techniques (wavelet, LOG, Fourier descriptors, affine transformation) are listed, but the transitions between them are abrupt. The logical flow between these techniques needs clearer justification — why this specific sequence? How do they complement each other?
• The abstract claims a 99.1% accuracy and 0.31 RMSE but does not mention:
o What dataset was used?
o How many images were tested?
o Against which baseline methods was the proposed approach compared.
• Some Awkward Phrasing:
Phrases like “it can be seen that” and “did not show evident confusion” are informal and not suitable for a high-level scientific abstract.
• Terms like "clear contours" and "smoothness" are subjective without quantitative measures or standard image quality metrics.
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