Defective Edge Detection Using Cascaded Ensemble Canny Operator

Anjali Nambiyar Rajkumar Kannan

REVIEW 4 major objections 4 minor 12 references

Defective Edge Detection Using Cascaded Ensemble Canny Operator

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A cascaded quaternion Canny operator is claimed to detect object edges with 99.8 percent accuracy, beating the listed deep edge-detection networks.

desk verdict Under-specified Canny variant with a protocol-free comparison; the claimed win over deep edge detectors is not supported as written. read the letter →

arxiv 2411.14868 v4 pith:JK5MFCMT submitted 2024-11-22 cs.CV

Anjali Nambiyar Rajkumar Kannan This is my paper · ORCID

classification cs.CV

keywords defectiveedgedetectionensemblelearningCascadedCannyoperatorquaternioncolorimagefruitdefectbenchmark

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a classical, training-free edge detector—built by cascading quaternion-based Canny filtering into an ensemble operator—can find object edges, including weak edges on defective fruit, more accurately and with cleaner output than the deep-learning edge detectors used as baselines. It reports accuracy in the high 99 percent range (99 percent in the comparison table, 99.8 percent in the conclusion) on a fresh-and-rotten fruit dataset and a standard segmentation benchmark. If the claim holds, it matters because it would mean a hand-crafted operator with no learned weights can outperform learned detectors on the tested images while reducing processing complexity.

What carries the argument

The load-bearing object is the Cascaded Ensemble Canny (CEC) operator, a quaternion-based extension of the classical Canny detector. It encodes the color image as a quaternion-valued signal and applies filtering with a pure unit quaternion $\mu=(i+j+k)/\sqrt{3}$ and rotation angle $\theta=\pi/2$, then combines the horizontal and vertical modulus components $q_1(p,q)$ and $q_2(p,q)$ into an overall modulus, computes the gradient orientation, and thresholds pixels into strong edges, weak edges, and non-edges. The 'ensemble' part comes from iterating the operation so that residual vectors are aligned with atoms from a selected dictionary, refining the distinction between edge and non-edge pixels. This machinery is what lets a classical operator handle color edges as a multidimensional signal and recover weak edges that simple gray-scale Canny would discard.

What would settle it

Take one shared test split of a public edge-detection benchmark with pixel-level ground-truth edge maps, run CEC and the listed learned baselines on the identical images, and compute accuracy with one formula; if CEC's accuracy falls to the baselines' 78–94 percent range rather than staying near 99 percent, the central outperformance claim is refuted.

Watch

Extended reading notes

Core claim

The central claim is that the Cascaded Ensemble Canny (CEC) operator extends the classical Canny edge detector to quaternion-valued color images and, by cascading the filtering and thresholding steps, produces edge maps that are both more accurate and visually cleaner than those of the compared edge-detection networks. The paper reports CEC at 99 percent accuracy and 98 percent specificity against baselines ranging from 78 to 94 percent accuracy, and concludes with a 99.8 percent accuracy figure. It also claims the method can identify weak edge pixels on defective fruit images, which the deep baselines miss. On the paper's own terms, the discovery is that a no-training, quaternion-based ensemble Canny operator can serve as a competitive edge detector for difficult real-world images.

Load-bearing premise

The claim stands on the comparison being fair: CEC's 99 percent accuracy must have been measured on the same images, the same ground-truth definition, and the same metric as the baselines' 78–94 percent; if those numbers come from different protocols, the outperformance does not follow.

Editorial extensions

If this is right

A training-free classical edge detector can, if the reported numbers hold, match or beat learned detectors on the fruit-defect and segmentation images tested.
The method's reported ability to catch weak edge pixels makes it a candidate preprocessing step for defect detection and early disease diagnosis in fruit imaging.
Because CEC operates on quaternion-encoded color rather than per-channel gray values, the same operator could be applied to any multidimensional image without retraining.
The paper's complexity claim suggests that replacing two-dimensional deep networks with a cascaded operator could lower processing time for edge extraction.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct head-to-head on a single shared benchmark with one ground-truth convention would be a cheap, decisive test; the paper's comparison table does not state whether baseline numbers were recomputed under the same protocol.
If the quaternion encoding generalizes, CEC could be adapted to video edges by treating time as a fourth quaternion component, or to multispectral imagery by encoding bands as quaternion parts.
The 99.8 percent figure is surprising enough that a per-class breakdown (fresh vs. rotten) would clarify whether the operator is finding general edges or exploiting contrast differences specific to the fruit dataset.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is not a usable paper yet. The core idea — a quaternion-encoded Canny variant combined with some ensemble voting — is plausible on its face, but the manuscript never specifies the algorithm well enough to reproduce it, and Table 1's numbers cannot be interpreted because the baselines were not evaluated under a shared protocol.

What's genuinely there: the problem of detecting defective fruit edges for post-harvest sorting is real, and a no-training classical operator that beat heavy deep networks would be genuinely useful in low-resource settings. The paper also makes a reasonable choice to work in the quaternion domain for color images, and the CEC operator is at least described as a combination of quaternion magnitude computation, gradient thresholding, and an ensemble step. But these pieces are all standard; the paper cites none of the prior quaternion Canny or ensemble edge-detection work, so the novelty claim is unsubstantiated.

Where it falls apart: Section 3.2 is a collection of equations (1)–(7) that do not connect into a cascaded algorithm. The thresholds, the ensemble rule, and the exact cascade are never stated. The evaluation is worse: Table 1 compares CEC against Sobel, ERRNet, SASM, PiDiNet, and LDC, but never says what dataset each number was measured on, what ground truth was used, or what the accuracy/specificity formulas are. Two of those baselines (ERRNet, a camouflaged-object detector, and SASM, a hyperspectral reconstruction method) are not edge detectors at all and could not have produced edge-detection accuracy on the Berkeley or fruit datasets without substantial adjustment. The prose says 99.8% accuracy; Table 1 says 99%. No error bars, no code, no protocol. Under those conditions, the central claim — CEC outperforms deep networks — is not a claim the reader can check.

The stress-test note is right: even a perfect implementation wouldn't save the comparison if the numbers come from different protocols. I'd go further: the algorithm description is not close to complete, so there isn't yet a 'method' to evaluate.

Who is this for? Possibly someone looking at low-resource classical edge detection, but only after a complete rewrite with a specified algorithm, a same-protocol comparison, and released code. As is, it doesn't deserve referee time. I'd recommend a desk reject, with an invitation to resubmit a full version that actually describes the operator and runs the baselines on the same images.

Referee Report

4 major / 4 minor

Summary. The paper proposes a Cascaded Ensemble Canny (CEC) operator for edge detection, combining quaternion-based Canny filtering with a cascade/ensemble scheme. It claims to outperform existing edge detection networks on the Berkeley segmentation dataset and a fresh/rotten fruit dataset, reporting accuracy and specificity values in Table 1. The central claim is that a classical, non-trained filter can beat modern deep learning edge detectors, with the supporting evidence being a single comparative table and qualitative output images.

Significance. If the central claim were established, it would be significant: a non-trained classical operator outperforming deep edge detectors would challenge the prevailing deep-learning approach to low-level vision and could offer a lightweight alternative. However, the significance is not realized in the manuscript because the comparison is not controlled: the evaluation protocol is absent, baseline methods are drawn from unrelated tasks, and the reported performance is internally inconsistent. As presented, the paper provides no reliable evidence for its headline claim.

major comments (4)

[Section 4.2, Table 1] Table 1 is the sole quantitative support for the claim of outperformance, but no evaluation protocol is described. The manuscript does not state which test images were used, whether ground truth is pixel-level edge maps or fruit class labels, how accuracy and specificity are computed, or whether baseline numbers were recomputed on the same data or taken from previous publications. Without a shared protocol, the comparison is uninterpretable and the central claim is unsupported.
[Section 4.2 and Section 5] The reported accuracy is internally inconsistent: Table 1 lists CEC accuracy as 99%, while the text in Section 4.2 and the conclusion states 'a high level of accuracy (99.8%)' and 'a high range of accuracy (99.8%)'. This discrepancy must be resolved before any performance claim can be taken seriously.
[Section 4.2, Table 1 and References] At least two of the baseline methods are not edge detection methods: ERRNet (cited as [1]) is a camouflaged object detection method, and SASM (cited as [3]) is a hyperspectral image reconstruction method. Their published accuracy figures, if borrowed from their original papers, cannot be compared with edge detection accuracy on the Berkeley and fruit datasets. The paper provides no evidence that these baselines were evaluated under the same conditions as CEC, making the outperformance claim self-referential rather than empirically grounded.
[Section 3.2, Eqs. (1)-(7)] The algorithm is not described with sufficient detail to be reproduced. Equations (1)-(7) mix quaternion multiplication, modulus computations, standard deviation expressions, and gradient definitions, but the text does not explain how these components form the 'cascaded ensemble' operator, how thresholds are set, or how the ensemble decisions are combined. The free parameters (Canny hysteresis thresholds and the quaternion rotation parameters μ and θ) are only partly specified, and no sensitivity analysis is given.

minor comments (4)

[Section 1] The phrase 'Computing vision tasks' should be 'computer vision tasks'.
[Table 1 and References] The baseline labels are inconsistent: Table 1 uses 'LCD' while the reference list and the related work section refer to 'LDC' (Lightweight Dense CNN).
[Section 4.1] The dataset names are inconsistent: the text mentions 'Berkley' (presumably Berkeley) and 'Bangkit-JKT2-D' for the fruit dataset, but the dataset descriptions and figure captions do not clarify the dataset splits, sizes, or ground-truth generation process.
[Table 1 and References] The citation labels in Table 1 do not match the reference list: 'Sobel [1]' conflicts with reference [1] being a camouflaged object detection paper, and 'ERRNet [2]' conflicts with reference [2] being a salient object detection paper. All citations need to be verified and corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: CEC is a hand-built operator with no fitted parameters, and the paper's equations do not reduce to its performance claims.

full rationale

The paper's derivation chain consists of quaternion-based filtering, gradient computation, and thresholding (Eqs. 1–7). No parameter is fitted to data and then renamed as a prediction; no quantity used to define CEC is itself derived from the reported accuracy or specificity. The accuracy and specificity numbers in Table 1 are external measurements, not outputs of Eqs. (1)–(7), so the central outperformance claim is not forced by construction. There are no self-citations and no imported uniqueness theorems. The serious weaknesses of the paper—unstated evaluation protocol, possibly mismatched baseline tasks, and missing metric definitions—are validity and reproducibility concerns, not circularity. Under the requirement to identify an explicit equation-level reduction or self-citation chain, no circular step can be exhibited.

Assumptions & free parameters 2 free parameters · 3 assumptions · 1 invented entities

The central claim rests on several unsupported inputs: Canny hysteresis thresholds that are never given, hand-chosen quaternion rotation parameters (mu = (i+j+k)/sqrt(3), theta = pi/2), an asserted but unproven link between a covariance projection (equations 4-5) and edge enhancement, and an unverified ensemble/cascade assumption. The evaluation adds a further unstated input: an accuracy protocol comparable across methods built for different tasks. None of these are independently supported, and no code or data is released.

free parameters (2)

Canny high and low hysteresis thresholds = not reported
Section 3.1 says 'Applied high and low thresholds, pixels with gradient magnitudes above this value are classified as strong edges' but no values are given; these thresholds determine which edges survive and directly control the reported accuracy and specificity.
Quaternion rotation parameters mu and theta = mu = (i+j+k)/sqrt(3), theta = pi/2
Section 3.2 fixes these by hand in the rotation R = e^(mu*theta) with no justification or sensitivity analysis; they are part of the edge-enhancement step and affect the gradient modulus.

assumptions (3)

ad hoc to paper Encoding an image as a quaternion-valued quantity via R = e^(mu*theta) with the chosen mu and theta preserves the edge information needed for detection.
Section 3.2 introduces the quaternion encoding without deriving why this particular rotation axis and angle are appropriate for edge detection.
domain assumption The covariance-like projection in equations (4)-(5) constitutes an edge-enhancement step.
The text jumps from a covariance computation and projection Phi = (R - mu)^R to 'an appropriate thresholding to find the locations with the sharpest contrast'; the connection between the projection and edge magnitudes is asserted, not derived.
domain assumption Cascading the CEC operator suppresses false edges without removing true weak edges.
Section 3.1 states the classification of strong versus weak edges and the ensemble design reduce false positives, but no analysis or ablation supports this claim.

invented entities (1)

Cascaded Ensemble Canny (CEC) operator
purpose: A named operator claimed to detect weak edge pixels on defective fruit images and natural images with about 99 percent accuracy.
The operator's precise definition is not recoverable from the paper: the equations in Section 3.2 do not specify the cascade structure or ensemble combination, and no code or reproducible protocol is provided, so there is no falsifiable handle outside the paper's own table.

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Cite this review

Pith. "Pith review of Defective Edge Detection Using Cascaded Ensemble Canny Operator." pith.science (2026). https://pith.science/paper/JK5MFCMT

@misc{pith2026241114868,
  author       = {Pith},
  title        = {Pith review of: Defective Edge Detection Using Cascaded Ensemble Canny Operator},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JK5MFCMT}},
  note         = {Machine review of arXiv:2411.14868}
}

read the original abstract

Edge detection has been one of the most difficult challenges in computer vision because of the difficulty in identifying the borders and edges from the real-world images including objects of varying kinds and sizes. Methods based on ensemble learning, which use a combination of backbones and attention modules, outperformed more conventional approaches, such as Sobel and Canny edge detection. Nevertheless, these algorithms are still challenged when faced with complicated scene photos. In addition, the identified edges utilizing the current methods are not refined and often include incorrect edges. In this work, we used a Cascaded Ensemble Canny operator to solve these problems and detect the object edges. The most difficult Fresh and Rotten and Berkeley datasets are used to test the suggested approach in Python. In terms of performance metrics and output picture quality, the acquired results outperform the specified edge detection networks

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 6 canonical work pages

[1]

Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

Ji, G. P., Zhu, L., Zhuge, M., & Fu, K. (2022). Fast camouflaged object detection via edge -based reversible re -calibration network. Pattern Recognition, 123, 108414 DOI: https://doi.org/10.48550/arXiv.2111.03216

work page Pith review arXiv doi:10.48550/arxiv.2111.03216 2022
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Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

Lin, J., Cai, Y., Hu, X., Wang, H., Yuan, X., Zhang, Y., ... Van Gool, L. (2022). Coarse-to-fine sparse transformer for hyperspectral image reconstruction. arXiv preprint arXiv:2203.04845. DOI: https://doi.org/10.48550/arXiv.2203.04845

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[2]

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arXiv 2022
[4]

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Le, M., & Kayal, S. (2021, July). Revisiting Edge Detection in Convolutional Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE. DOI: https://doi.org/10.48550/arXiv.2012.13576

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Jing, J., Liu, S., Wang, G., Zhang, W., & Sun, C. (2022). Recent advances on image edge de tection: A comprehensive review. Neurocomputing. DOI: https://doi.org/10.1016/j.neucom.2022.06.083

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Le, T., & Duan, Y. (2020). REDN: a recursive encoder-decoder network for edge detection. IEEE Access, 8, 90153-90164. DOI: https://doi.org/10.1109/ACCESS.2020.2994160

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Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., ... & Liu, L. (2021). Pixel difference networks for efficient edge detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 5117 - 5127). DOI: https://doi.org/10.48550/arXiv.2108.07009

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Fast Camouflaged Object Detection via Edge-based Reversible Re-calibration Network

Ji, G. P., Zhu, L., Zhuge, M., & Fu, K. (2022). Fast camouflaged object detection via edge -based reversible re -calibration network. Pattern Recognition, 123, 108414 DOI: https://doi.org/10.48550/arXiv.2111.03216

work page Pith review arXiv doi:10.48550/arxiv.2111.03216 2022

[2] [3]

Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction

Lin, J., Cai, Y., Hu, X., Wang, H., Yuan, X., Zhang, Y., ... Van Gool, L. (2022). Coarse-to-fine sparse transformer for hyperspectral image reconstruction. arXiv preprint arXiv:2203.04845. DOI: https://doi.org/10.48550/arXiv.2203.04845

work page Pith review arXiv doi:10.48550/arxiv.2203.04845 2022

[3] [2]

Xu, X., Chen, J., Zhang, H., Han, G. (2022). SA -DPNet: Structureaware dual pyramid network for salient object detection. Pattern Recognition, 127, 108624. DOI: https://doi.org/10.1016/j.patcog.2022.108624

arXiv 2022

[4] [4]

Revisiting Edge Detection in Convolutional Neural Networks

Le, M., & Kayal, S. (2021, July). Revisiting Edge Detection in Convolutional Neural Networks. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE. DOI: https://doi.org/10.48550/arXiv.2012.13576

work page Pith review arXiv doi:10.48550/arxiv.2012.13576 2021

[5] [5]

Orhei, C., Bogdan, V., Bonchis, C., & Vasiu, R. (2021). Dilated Filters for Edge- Detection Algorithms. Applied Sciences, 11(22), 10716. DOI: http://dx.doi.org/10.48550/arXiv.2106.07395

work page Pith review arXiv doi:10.48550/arxiv.2106.07395 2021

[6] [6]

Elharrouss, O., Akbari, Y., Almaadeed, N., & Al -Maadeed, S. (2022). Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches. arXiv preprint arXiv:2206.08016. DOI: https://doi.org/10.48550/arXiv.2206.08016

arXiv doi:10.48550/arxiv.2206.08016 2022

[7] [7]

Jing, J., Liu, S., Wang, G., Zhang, W., & Sun, C. (2022). Recent advances on image edge de tection: A comprehensive review. Neurocomputing. DOI: https://doi.org/10.1016/j.neucom.2022.06.083

work page doi:10.1016/j.neucom.2022.06.083 2022

[8] [8]

Le, T., & Duan, Y. (2020). REDN: a recursive encoder-decoder network for edge detection. IEEE Access, 8, 90153-90164. DOI: https://doi.org/10.1109/ACCESS.2020.2994160

arXiv 2020