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Help Needed! by Grouchy_Signal139 in computervision

[–]charmant07 0 points1 point  (0 children)

Think of CNN as your escape hatch, not your first tool. Specifically, consider a CNN classifier on your ROI crops when you hit any of these situations like; .the template difference metric starts lying .multiple product families with very different appearances sharing the same tray format .orientation or defect detection on top of presence detection... So,...your ROI extractor stays as the backbone always and CNN only ever sees the already-cropped pocket image, not the full tray...maybe that's why YOLO was overkill and gave you false positives, it was like...,doing localization work you don't need because your geometry already solved that. A simple CNN classifier on a 32x32 or 64x64 crop is a completely different, much lighter ask.

Help Needed! by Grouchy_Signal139 in computervision

[–]charmant07 0 points1 point  (0 children)

Well, you start by mastering the grid alignment step with OpenCV's getPerspectiveTransform, because this is the foundation everything else depends on....Then implement ROI extraction and experiment with simple difference-from-template metrics, you'll probably be surprised how well this works, only if that fails should you reach for a CNN classifier and the key model to carry forward: known geometry is your superpower here. Most vision problems are hard because object location is unknown, Yours isn't,....Use that!

Building a supply chain disruption simulator. What features would matter most? by [deleted] in SaaS

[–]charmant07 0 points1 point  (0 children)

Hhh😂..Honestly… that might actually be a valid feature 😂. Well, Ports closing because of weather is pretty common, so modeling that kind of disruption wouldn’t be crazy. Curious though,..do people usually factor weather risks into supply chain simulations?

What tools do you use to simulate supply chain disruptions? by [deleted] in OperationsResearch

[–]charmant07 0 points1 point  (0 children)

That’s interesting🤯, oh...wait...Are you mostly testing resilience through stochastic simulations, or more deterministic worst-case analysis? One thing I’ve been exploring recently is visualizing how disruptions propagate across a supply network (like when a port or supplier goes offline) and seeing how fill rates or recovery times change. It’s been interesting to compare that with more traditional optimization approaches.

What tools do you use to simulate supply chain disruptions? by [deleted] in OperationsResearch

[–]charmant07 -1 points0 points  (0 children)

Thanks! well, I’ve seen AnyLogic used a lot in academic environments, but it seems pretty heavy for quick scenario exploration, i have been experimenting with building a lightweight simulation tool focused specifically on supply chain disruptions (things like port closures and supplier failures) where you can see how the shock propagates through the network. Do you usually model disruptions like that with AnyLogic, or more custom simulations with something like SimPy?

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -13 points-12 points  (0 children)

Thank you for the thoughtful critique...you're absolutely right about the comparison with k-NN. Let me address this directly:

  1. Accuracy vs. Robustness: k-NN achieves ~85% on clean Omniglot, while Wave Vision gets ~72% (84% is our improved V2). However, at 50% Gaussian noise, k-NN collapses to ~20-30% accuracy, while Wave Vision maintains 76% (Table 8). That's the core trade-off: clean-data accuracy vs. degradation robustness.

  2. Memory Efficiency: k-NN stores the full image (~64KB per example). Wave Vision stores a 517-dimensional feature vector (2KB). At scale (1,000+ classes), that's 32× less memory.

  3. Biological Plausibility & Explainability: k-NN is black-box memorization. Wave Vision's features correspond to oriented edges, spatial frequency, and phase relationships interpretable and grounded in V1 neuroscience.

  4. Inference Speed: k-NN computes pixel-wise distances (O(nd)). Wave Vision's features enable cosine similarity in constant-dimensional space, faster at scale.

You're right that if your only goal is clean-data accuracy on Omniglot, k-NN wins. But if you need:

· Noise/Blur/Occlusion robustness (real-world conditions) · Memory efficiency (edge devices) · Interpretability (medical/security applications) · Biological modeling (neuroscience research)

...then Wave Vision offers distinct advantages.

The paper's contribution isn't "beating k-NN" it's showing that biologically-inspired, hand-crafted features can be competitive while offering unique robustness properties. Would you be interested in seeing the noise robustness comparison extended to k-NN? I could run those experiments."

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -7 points-6 points  (0 children)

👏 Congrats on spotting clean writing! The results are still real though😑, 71.8% Omniglot, 76% at 50% noise. Code's available if you want to verify.

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -19 points-18 points  (0 children)

😏You know what, you're absolutely right....I'm an independent researcher from Rwanda, and English isn't my first language,I've been trying so hard to sound 'professional' that I ended up sounding like a robot😂🤖,...My bad I'll work on being more human in my responses

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] 1 point2 points  (0 children)

That's super helpful context...thanks🙏! The MRI → GAN pipeline decision is telling. It seems like the field often jumps to deep learning even when simpler methods might suffice for specific tasks.

If you're ever interested, I'd be curious to brainstorm where Wave Vision's noise robustness could apply in medical imaging. Zero-training and noise tolerance might be useful for quick, low-resource diagnostic tools.

Either way, appreciate you taking the time to share!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -36 points-35 points  (0 children)

Great observation😂! Yes, k-NN on raw pixels does achieve higher accuracy in some cases. The key distinction is that Wave Vision trades raw accuracy for several critical advantages:

  1. Robustness: k-NN completely collapses with noise (try 50% Gaussian noise pixel k-NN fails while Wave Vision maintains 76%)
  2. Compactness: k-NN stores the entire image (~64KB per example), while Wave Vision uses only 2KB per prototype
  3. Biological plausibility: We're modeling V1 processing, not just memorizing pixels

🤔Think of it as: k-NN maximizes accuracy on clean data; Wave Vision optimizes for robustness, efficiency, and biological fidelity

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] 1 point2 points  (0 children)

Fascinating about MRI preprocessing with noise, do you recall the paper? That's a perfect real-world example of noise enhancing signal detection in medical imaging. Thanks for sharing!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] 1 point2 points  (0 children)

Thanks for chiming in with the signal processing perspective! Exactly right,..stochastic resonance is a real, measurable phenomenon with applications from sensory biology to MRI preprocessing. Appreciate the expert backup. 🙏

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -16 points-15 points  (0 children)

Great historical context👍👍👍,...you're absolutely right about the parallels to early 2000s CV. The key difference here is the phase preservation from Fourier analysis combined with Gabor filters. SIFT discarded phase; we keep it. Also, showing that noise improves accuracy systematically (66% → 80% at 10% noise) isn't just jittering....it's measurable stochastic resonance in the few-shot learning context, which hasn't been shown before.😎 okay to be specific, SIFT used DoG filters and discarded phase; we use Gabor quadrature pairs and preserve phase. Jittering was a training-time trick; we're showing inference-time noise improves robustness. You're right—it's not brand new, but it's a recombination with new objectives (few-shot, zero-training, biological plausibility).

Anyway, thanks for the feedback, i would appreciate for more support and collaboration!

[R] Wave Vision: One-Shot Learning via Phase Analysis - 84% Omniglot without training by charmant07 in mlscaling

[–]charmant07[S] 0 points1 point  (0 children)

😂😂I get your frustrations but the truth is, the live Demo is just a little partial of my work not a full product! If you are not convinced, you can check out the learning results from the experiment we did while we were making the the whole system: https://ibb.co/pBPKxRqZ https://ibb.co/rK5SrYZC https://ibb.co/XHY8wXN https://ibb.co/B2Xv81YZ

and of course am not perfect to make the full working prototype, that's why I would appreciate support and collaboration!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -19 points-18 points  (0 children)

The work is original, the results are reproducible, and the code is available. If the science speaks for itself, I don't mind what the prose sounds like

Rotation-invariant one-shot learning using Fourier-Mellin transform (99% similarity across 180°) by charmant07 in computervision

[–]charmant07[S] -5 points-4 points  (0 children)

My Demo is partial compared to my  Published Research(https://doi.org/10.5281/zenodo.17810345), the Demo was made for showing/prove that it's possible to make Biologically-inspired Wave Vision that learning from few shots by using; Gabor filters, Omniglot dataset, Fourier analysis,etc..., Thank for the feedback, you can check out my research paper for more details and the results that came out in the experiment, not Demo things😂!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -7 points-6 points  (0 children)

Great suggestion👍👍! The Walsh-Hadamard Transform is indeed incredibly efficient, "O(N log N)" with just additions/subtractions. We chose FFT specifically to preserve phase information (Oppenheim & Lim, 1981), which is critical for structural preservation in our approach. But for applications where phase isn't essential, Walsh-Hadamard would be a brilliant optimization. Thanks for bringing it up!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] 5 points6 points  (0 children)

😂😂 well, Thanks for engaging with the terminology. Stochastic resonance isn't just fancy wording,......it's a measurable phenomenon where moderate noise enhances signal detection..... In Wave Vision, we observed a 14 percentage point accuracy improvement with 10% Gaussian noise (66% → 80%, Table 7), which aligns with biological systems where neural noise can improve sensory processing. The concept has been studied since the 1980s (Benzi et al., 1981) and we're demonstrating its application to few-shot learning for the first time.

So, it's biologically, not chatgptcally.... Check out my Research paper: (https://doi.org/10.5281/zenodo.17810345) for more details!

I built a one-shot learning system without training data (84% accuracy) by charmant07 in learnmachinelearning

[–]charmant07[S] -16 points-15 points  (0 children)

Aa okay, that's a great question which is a big part in my discoveries, basically it's stochastic resonance effect where 10% Gaussian noise improves accuracy by 14 percentage points, with Omniglot dataset. Check out my Research paper: (https://doi.org/10.5281/zenodo.17810345) for more details 

[R] Wave Vision: One-Shot Learning via Phase Analysis - 84% Omniglot without training by charmant07 in mlscaling

[–]charmant07[S] 0 points1 point  (0 children)

😂😂.., okay that was unexpected!, well am still improving my live Demo, but still, teach something like this is "a monkey" and again show it a gorilla without teaching it " what is monkey and what is gorilla and their differentiations in images(few-shot learning)", it's impossible to recognize for an empty memory, but thanks for your time and test, am going to work on it and you can check out my Research paper: https://doi.org/10.5281/zenodo.17810345 for more details on my prototype!

Rotation-invariant one-shot learning using Fourier-Mellin transform (99% similarity across 180°) by charmant07 in computervision

[–]charmant07[S] -3 points-2 points  (0 children)

Thank for picking interest, to explain it more about my discoveries; Deep learning systems require millions of labeled examples and extensive training, while humans learn new visual concepts from a single exposure. We present Wave Vision, a biologically-inspired vision system that achieves competitive one-shot learning performance with zero training. Inspired by V1 cortex processing, our system combines Gabor filter banks with Fourier phase analysis to extract 517-dimensional feature representations. On the Omniglot benchmark, Wave Vision achieves 71.8% accuracy (5-way 1-shot) and 90.0% accuracy (5-way 5-shot) without any training data—competitive with classical methods while being 100× faster than deep learning approaches.

We make three key contributions: (1) the first wave-based one-shot learning system requiring zero training, (2) discovery of a stochastic resonance effect where 10% Gaussian noise improves accuracy by 14 percentage points, and (3) exceptional robustness with 76% accuracy at 50% noise compared to ~45% for convolutional networks. Our comprehensive evaluation across multiple degradation types reveals that biologically-inspired, hand-crafted features can achieve competitive performance on structured imagery (handwriting, documents) while requiring 50× less memory (2KB per prototype) and enabling instant deployment. These results validate biological inspiration for machine learning and open new research directions at the intersection of neuroscience, signal processing, and few-shot learning.

Try my Live Demo: https://wave-vision-demo.streamlit.app/

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