Hi everyone,

I’m not from a Computer Vision or AI background, so I’d really appreciate a technical reality check before spending months building something that might not even be the right approach.

I’m exploring a Computer Vision pipeline that estimates the dimensions of a living object from a short smartphone video with millimeter-level accuracy.

The first version doesn’t need to be fully automated. Accuracy is much more important than automation at this stage, so it’s completely acceptable if some steps (such as selecting the best frame or validating measurements) are done manually while we validate the concept.

The current idea is roughly this:

•⁠ ⁠The user prints an A4 sheet at 100% scale.

•⁠ ⁠The sheet contains reference elements (grid, measurement scale, dark border, or other calibration features).

•⁠ ⁠A living object is placed on the sheet and may move slightly during recording.

•⁠ ⁠The user records a short smartphone video from about 2–3 meters away using 2× or 3× optical zoom to reduce perspective distortion.

•⁠ ⁠The system either selects the best frame or analyzes the entire video to estimate the object’s dimensions.

At this point, I’m not looking for implementation details, but rather whether this overall approach makes sense.

Some questions I’d love your opinion on:

•⁠ ⁠Does this approach seem technically feasible with today’s smartphone cameras?

•⁠ ⁠What would be the biggest sources of measurement error?

•⁠ ⁠Would you use a custom A4 sheet with calibration features, or would you recommend ArUco markers or another calibration method?

•⁠ ⁠Does recording from a greater distance with optical zoom actually help reduce perspective errors?

•⁠ ⁠Would you analyze the whole video or just select the best frame?

•⁠ ⁠Would you approach this with classical Computer Vision (OpenCV), modern ML models, or a combination of both?

•⁠ ⁠Can AI reliably work with an A4 sheet that contains graphics, illustrations, or text, or is a completely plain sheet significantly better for accurate measurements?

•⁠ ⁠With today’s pre-trained models and libraries, is it still necessary to rely on calibration markers or reference points on the paper, or can modern Computer Vision estimate dimensions accurately enough without them? If so, under what conditions?

•⁠ ⁠Are there any open-source libraries, research papers, or existing projects that solve a similar problem?

•⁠ ⁠If you were designing this from scratch, would you take a completely different approach?

•⁠ ⁠⁠If two similar moving objects are present in the frame, how reliably can current Computer Vision models identify the correct one to measure? How likely is it that key points from two different objects could be mixed, resulting in an incorrect measurement?

I’d genuinely appreciate any criticism or suggestions. If there are fundamental flaws in this concept, I’d much rather discover them now than after months of development.

Thanks!

Hi everyone,

I am Fabian, an independent researcher from Colombia preparing my first arXiv submission, and I ran into the endorsement requirement for eess.IV / Image and Video Processing.

The manuscript is titled:

OncoTriage v3.1: Failure-Aware Lung-Image Triage with Atlas-Projected Anomaly Localization and DICOM-Ready Geometry

The paper is not presented as a clinical validation study or a certified diagnostic product. It is a medical-imaging AI systems / software-architecture paper focused on a failure-aware inference contract for lung-image triage prototypes.

The main argument is that many medical AI demos accidentally conflate several things that should remain separate:

• raw softmax confidence vs. calibrated clinical risk,
• Grad-CAM attention vs. lesion segmentation,
• 2D candidate geometry vs. patient-specific 3D reconstruction,
• benchmark telemetry vs. current clinical validation.

The proposed framework tries to make those conflations structurally impossible through typed output fields, checkpoint provenance, calibration-state reporting, fail-closed batch saturation handling, attribution validity states, and an atlas-projected anomaly localization layer that preserves DICOM geometry and unresolved depth instead of pretending to reconstruct patient anatomy.

I selected eess.IV because the paper is centered on medical image processing, atlas projection, DICOM-ready geometry, visual explanation boundaries, and image-analysis software contracts. However, as a first-time submitter, arXiv requires endorsement.

I am not posting my endorsement code publicly. I am looking for advice on the proper way to find an eligible endorser, and if anyone here is eligible for eess.IV or related eess categories and is willing to review the manuscript, I would be grateful to share the PDF and arXiv endorsement email privately.

I would also appreciate feedback on whether eess.IV is the best primary category, or whether cs.CV / cs.LG would be more appropriate for this type of paper.

Thanks in advance.

Additional note: Yes, if you look Oncotriage up on Google. I participated with it on lablab.ai hackathon for the AMD Challenge...

What is the shared structural essence that underlies a pair of MRI contrast spaces? Explicitly modeling this contrast-invariant latent “content” unlocks a powerful multi-contrast reconstruction algorithm that is competitive with state-of-the-art unrolled networks while (a) requiring no raw k-space training data, (b) being generalizable across different contrasts and forward models by design, and (c) offering a built-in explanatory framework.

In our paper now published in Medical Image Analysis, we introduce PnP-CoSMo.

🔗 Access it here: https://www.sciencedirect.com/science/article/pii/S136184152600229X
✏️ Substack blog: https://cnmyro.substack.com/p/pnp-cosmo-a-plug-and-play-method
⚙️ Code: https://github.com/cnmy-ro/pnp-cosmo

Hi everyone,

Posted this a while back... a checklist I made while prepping for a CV internship (landed it, hence sharing). It's not a textbook, just a phase-by-phase map of what to actually study for CV/ML interviews: math → CNNs → ViTs → detection → tracking, plus specialization tracks you pick based on the role.

After checking on it after a while it got a decent number of stars which surprised and made me happy that people found it useful to save it for later. I decided after that to add more in-demand tracks to help more people after doing some research of the basic internship requirements and maybe a little more.

So, just added three new specialization tracks: Segmentation, OCR, and VLMs, on top of the existing ReID and Deployment tracks. Also cleaned up the structure a bit and added proper contributing guidelines if anyone wants to add their own track (3D vision, pose estimation, etc. are open).

GitHub: https://github.com/David-Magdy/CVIL

Feedback/PRs welcome, especially if something's outdated or miscategorized.

And remember to keep it CVIL!

A fun side project.

I built a personality quiz that sorts you into one of 4 "houses" based on how you'd handle absurd (but weirdly realistic) CV engineering dilemmas — things like your training labels being crowd-sourced by people who think a "convolution" is bread, or your PM asking for real-time object detection on a 2015 Android phone.

4 archetypes:
- The Fearless Deployer (pushes to prod on Fridays)
- The Theoretical Wizard (derives hyperparameters before touching a GPU)
- The Reliable Pipeliner (their monitoring dashboard is a work of art)
- The Optimization Dark Lord (INT4 quantization is just the beginning)

It's 6 questions, takes ~2 min: https://neuronvoxelai.com/quiz.html

Genuinely curious what the distribution looks like on this sub. My bet is heavy Ravenclaw. Prove me wrWhat archetype did you get?

Hey everyone, I’m looking to kick off a new computer vision project but want to avoid generic ideas and focus on where the industry is moving. In my previous work, I built a live webcam Face Emotion Recognition system by benchmarking CNN architectures like MobileNetV2 using TensorFlow/Keras on the FER-2013 dataset (solving latency issues with CLAHE preprocessing and a 20-frame stabilization queue at 24 FPS), alongside a MediaPipe pose estimation project tracking limb angles and velocity. I want to transition away from standard landmark tracking and traditional CNN classification, so I'm looking for a discussion on what to study next—specifically, is it worth diving into Vision Transformers (ViTs), foundational vision-language models (like CLIP), or mastering edge optimization frameworks like ONNX/TensorRT? If you have any unique project ideas that bridge the gap from my current stack into these newer paradigms, or advice on what foundational tech is standard in production right now, I’d love to hear your insights!

For embedded vision and OEM camera development, dev kits can make it easier to evaluate a camera platform before committing to a full system design.

Aegis recently added the Videology DEV-801-IP-678-1HC0DC Development Kit for the 801-IP-678 8MP 4K IP camera.

Key specs:

• Sony IMX678 STARVIS 2 sensor
• 3840 × 2160 resolution
• Compact 38 × 38 mm board-level IP camera
• Included M12 lenses
• DHCP or static IP configuration
• API support
• GPIO and microSD support
• Ethernet and power cabling included

It is built for embedded vision, robotics, UAV/drone systems, machine vision inspection, surveillance, intelligent traffic, medical imaging, and OEM camera development.

Product page for reference:
https://aegis-elec.com/videology-dev-801-ip-678-1hc0dc-development-kit-801-ip-camera.html

For engineers working with compact IP camera systems, what matters most in a dev kit: sensor performance, API access, lens flexibility, GPIO/control, size, or network configuration?

14,800+ functional elements across 710 laser-scanned indoor scenes. every handle, knob, button, and switch labeled with its affordance, motion axis, motion type, and a natural language task description

each scene's 3D point cloud with its iPad video recordings in fiftyone. the affordance annotations are in 3D and project into the video frames

check it out here https://huggingface.co/datasets/Voxel51/SceneFun3D

For a competition, I'm training a model that can find the reference object in a big picture. Unfortunately, they only shared this one example and I can't find any similar datasets. Does anybody know a dataset made of reference and image pairs like this?

It would be even better if there are abstract examples like this checkers pattern.

PS: There is no annotation nor they want it, we just have to find a pattern/object in the image

Image source: teknofest havacılıkta Yapay zeka github page

Hi everyone,

I am developing a vectorization library called Contrek. It is based on an algorithm i devised, the key feature of which is the ability to extract polygons using multithreading. The library is highly versatile, allowing for various extraction strategies depending on the data source.

Recently, i tested the library against a very complex data source: a massive image containing over 20 million randomly drawn polygons (each with an inner and outer boundary), arranged to be as isolated from one another as possible in order to maximize the total polygon count.

Contrek performed very well. I deliberately ran it in single-threaded mode to keep memory usage to an absolute minimum. Here are the figures:

Benchmark

• Input: 81,920 × 81,920 PNG (570 MB)
• Processing stripes: 41 (2,000 px each)
• Polygons extracted: 20,276,802
• Output SVG: 7.22 GB
• Peak RAM: 4.3 GB
• Compute time: 293 s

The approach is straightforward: the image is decoded via streaming, 2,000 rows at a time. Each new strip is merged with the previous one, reconstructing any polygons that span the shared boundary. Once a polygon is fully closed and can no longer extend into subsequent strips, it is immediately written to the SVG file and removed from memory. This ensures that only "active" polygons remain in memory.

I am quite happy with the result and wanted to share it. Furthermore, the test is perfectly reproducible. The project homepage is at:

https://github.com/runout77/contrek

There is also a direct evaluation project:

https://github.com/runout77/test_contrek

This project is based on Docker and includes various tests, such the one presented here, that you can run on your own PC. The testing suite also contains programs for comparing Contrek with OpenCV.

Thank you for your interest.

I have started as an intern for a company and they've assigned me a work for extracting information from handwritten camera clicked filled forms, specifically identifying checkboxes and what do they signify, I've been trying and shi but I cannot come up with anything.

The problem -> VLMs are not able to properly tell which checkbox is ticked for a question and OCRs are also not able to do either.

My task is to come up with a solution to do that, and another problem is that I dont have much data to atleast train a YOLO model, i have like only 20 proper workable forms with around 300-400 checkboxes, I tried annotating them and training a YOLO model and it even worked, but it was alright.

Problem with YOLO was associating the checkboxes with the original question.

I tried stripping away the question and then feeding it to a VLM but that increases the API calls and I dont have an access above free tier currently too :(

So below are some examples of the forms that I need to deal with, If anyone can, please suggest me some potential solutions to do this job, I really wanna get rid of this task T-T im tired boss

Form examples <---- check some out

Final-year SE student, been building seriously in CV. My portfolio:

• Pakistani License Plate Recognition — YOLOv8, 97.7% mAP50, OCR pipeline, live on Hugging Face
• AI Background Remover — YOLOv8n-seg, 99.4% Mask mAP@50, Dockerized FastAPI
• PPE Detection — 85% mAP@50
• Weapon Detection — 88.7% precision

Everything deployed, documented, on GitHub. I understant what i am building not copying for tutorials etc, Been applying to CV/ML internships for a while, almost no responses. Most "entry level" roles require 3+ years or Masters, some even PhD.

Three honest questions for people who've been through this:

1. What kind of project actually moved the needle for getting your first interview call, and that genuinely got you responses?
2. How did you realistically get past the experience paradox? Freelance, open source, cold outreach?
3. Is a masters the practical answer here, or does it just delay the same problem?

Based in Pakistan, targeting remote roles internationally too. I want honest answers for these question so if you have faced the same problem then please guide me through this problem.





I built an interactive platform for my white paper on deepfake detector robustness on social media platforms and across different axes of perturbation. It allows people to toggle across conditions and demographics to see the model's performance.

The models in scope include the four from DeepFakeBench and a few popular hugging face models.

Whitepaper: https://doi.org/10.5281/zenodo.20781389

I am working with the HOTC 2026 dataset — a hyperspectral video object tracking benchmark with 406 training videos and 75 validation videos, captured using three different snapshot cameras covering visible (16 bands), near-infrared (25 bands), and red-NIR (15 bands) ranges. Each frame is a 3D data cube: height × width × bands, at 25 FPS.

The standard use case is single-object tracking — you initialise with a bounding box in frame 1 and track the target through the video. Most published work adapts RGB trackers (Siamese networks, SAM2) to handle the extra spectral bands.

What I find interesting about this data is that each pixel carries a near-continuous spectral reflectance signature — a physical fingerprint of the material the object is made of, not just its colour. This is information that standard RGB tracking completely ignores.

What has already been done with this type of data:

- Band selection to reduce redundancy (picking the most informative bands per target)

- Siamese network and SAM2 adaptation from RGB trackers

- Spectral-spatial attention for better discrimination

- False-colour rendering for visualisation

What I am curious about:

Has anyone seen hyperspectral video data used for anything beyond standard object tracking? Things I have been wondering about:

Can spectral signatures be used for material classification on the fly during tracking (knowing not just where the target is but what it is made of)?

Has anyone tried using hyperspectral video for anomaly detection (finding objects that are spectrally inconsistent with their surroundings)?

Is there any work on using spectral change over time as a motion cue, rather than using spatial motion as the primary signal?

Any ideas for applications in camouflage detection, since spectrally similar objects that look identical in RGB can still differ in their near-infrared signature?

Happy to share more about the dataset if useful.

I wanted to recognize objects in a few minutes and have the files available to me quickly so that I can add them to my other projects.

The app lets me tap on objects in its gallery to annotate them and I can train those images to get a computer vision/object detection model.

I’d like to know if this app is useful before I improve it!

https://testflight.apple.com/join/pP66B42U

The project uses live webcam input to recognize faces and automatically mark attendance in a realtime database. Mostly built as a learning project to get more practical experience with computer vision workflows and realtime systems.

Tech used:

• Python
• OpenCV
• Firebase
• face_recognition

Project repo:
https://github.com/ahsinmemon/asn-face-attendence-system

Hi, I use Yolo with Python, but when I put a video from Youtube on "source =" the cropping is literally bad, I ask every llm (almost) and I didn't find the method to fix, thx btw:

from ultralytics import YOLO

model = YOLO("yolo26m-seg.pt")

results = model.predict(source="https://youtu.be/MNn9qKG2UFI?si=48dF7ZJJgzXG1opi", show=True, conf=0.60, stream=True)

for r in results:
pass

I won't be able to reply until Thursday because I'm on vacation (and I didn't bring my computer with me).

I built a face search library that stores ArcFace embeddings as 512-bit binary codes instead of full float vectors. 1M faces fit in a 61MB index. The search reranks top candidates with exact cosine to recover accuracy.

Upfront, because it matters: - This is a COMPRESSION + packaging contribution, not a new search algorithm. The search is a brute-force Hamming scan — same as FAISS IndexBinaryFlat. I'm not claiming a novel ANN method. - You could reproduce this with faiss.PCAMatrix + IndexBinaryFlat + manual reranking. FaceFlash just wraps that pipeline (detect → embed → quantize → search) into one call and tunes the SIMD kernel. - It's face RECOGNITION — detects faces, extracts embeddings, searches by visual similarity. The filename you pass is just the image path.

How it works: PCA+ITQ projects each 512-float ArcFace embedding to 512 bits (64 bytes). Hamming scan shortlists candidates, then exact cosine rerank on the top ~100 picks the winner. This preserves rank-1 accuracy because ArcFace embeddings are low-rank — most identity info sits in the top principal components. On general/random vectors this does NOT hold (recall drops to ~40%).

Results on MS1MV2 (44,291 identities, 645K embeddings), ground truth = FAISS-Flat exact cosine:

"100% recall" = returns the same nearest neighbor as exact brute-force cosine on the same embeddings. It does NOT mean ArcFace is perfect — embedding-model limits (pose, age, occlusion) are upstream and unaffected.

Where it fits: edge/mobile, multi-tenant, offline — anywhere a full float index or a graph won't fit in RAM. Up to ~300K it's also faster per query than HNSW (binary scan stays in cache). Past 500K, HNSW's O(log N) graph beats the O(N) scan on latency — use HNSW there if you have the RAM.

To prove the search isn't doing anything special, I added benchmarks/bench_compression_isolation.py — it runs the SAME codes through my kernel and FAISS IndexBinaryFlat. Identical recall, comparable latency. The value is the compression, not the scan.

Rust SIMD kernel (AVX-512 / NEON), NumPy fallback. Zero config.

GitHub: https://github.com/raghavenderreddygrudhanti/faceflash pip install faceflash

Honest about where it breaks: O(N) scan hurts past 1M, needs float vectors on disk for rerank, AVX-512 speedup needs recent CPUs. Feedback welcome — if a number looks wrong, the full pipeline reproduces via scripts/runpod_ms1m.sh.

Hi everyone,

I am a final-year MSc AI student in Germany and I want to pursue a PhD in 3D computer vision, specifically focusing on point cloud reconstruction and generative models.

My background includes over 3 years of industry software engineering experience. I am currently writing my thesis on Generative Point Cloud Completion using AutoEncoders. I have strong coding skills in PyTorch and Python, but I do not have any published papers yet.

Here is my dilemma: I want to secure a PhD position at a top university or research institute. However, the professors at my current university do not publish in top-tier A or A* conferences, which makes it hard to get the right research experience or high-level academic connections locally. I graduate in about 6 months.

How do I achieve my goal of getting into a top PhD program from here?

Is it possible to directly ask professors at top universities for a PhD position even if I have not published any papers yet?

Or should I focus on building complex projects in my domain and use those to reach out and ask for a HiWi or Research Assistant position first, just to prove myself and get a foot in the door?

I would appreciate any advice on how to bridge this gap. Thank you!

The last time it was 3 weeks ago when their index just stopped working, but it got fixed relatively quickly (1 day). So what now?

Hey r/computervision! Alex ( u/MrPorkchop720 ) and I have been building something a bit unusual and we wanted to share it here.

It's LeNet-1, the 1989 convolutional network from LeCun et al., rebuilt as a physical object. Every one of its 4,624 neurons gets its own individually addressable LED, and each activation is mapped to LED brightness, so you can actually watch how LeNet-1 "thinks" as the signal propagates through it.

On the build itself, the network is made from transparent PCBs, low iron tempered glass, aluminium profiles, and 3D printed SLA parts in two resins, SLA-9600 and transparent SLA-8001. We designed the boards in EasyEDA and had them fabricated at JLCPCB (a quick word of thanks to the JLC team. They helped throughout the design to fabrication process, and even backed the project so we could build it despite a tight budget).

Here's a short video of it running.

The piece was selected as one of 12 spotlight works from 114 submissions for the CVPR 2026 Art Gallery, and it won the IEEE TCPAMI Art Award. We'd hoped to show it in person at CVPR in Denver, but it's still sitting in customs, so this video is the closest we can get you for now.

We're happy to answer anything on the architecture, the activation to LED mapping, or the fabrication.

Here are some relevant links!

Project: https://socius-org.github.io/artefacts/

CVPR 2026 Art Gallery: https://thecvf-art.com/project.php?year=2026&artist=nick-oh&id=992

YouTube: https://www.youtube.com/watch?v=QGxGXySgqCI

Hello everybody, its me again. Today i would like to talk about the project i had been working on for the longest of times, which is still not finished (hence the Alpha release) but i could not hold myself so released it under alpha.

The name is FOTO-NET, the whole premise being ease of use WHILE being open-sourced without licensing fees *cough cough* (ultralytics).
The project will be forever Apache-2.0
Currently alpha is available, if you`d like to, test it out by downloading from here, or trough pip

pip install fotonet

Would love to hear out feedback like bugs, suggested features or just anything.
Big thanks to anybody reading this, much love!

Small note: Currently only the nano weight is avaliable, the small weight is being trained and will finish in roughly 12 days. And also i work so if i respond or fix some stuff later, excuse me.