I have a homework that consists on me having these following 2 images and, through homography, I have to create a front view of the image and eliminate the person in front of it

https://preview.redd.it/xc9beb5eq4tg1.jpg?width=1920&format=pjpg&auto=webp&s=1bbfb112201d2821aaa541f08a3cd1d035a6ae95

The two images in question

I managed to warp the first photo so both pictures now are in the same plane, pictured below:

https://preview.redd.it/0j3wshsoq4tg1.jpg?width=1920&format=pjpg&auto=webp&s=abc8cac993a36d2a437fd22eb9e3e912c3182dc3

But, I don't really know how to continue from here, I'm not sure how to remove the person from the picture aside from maybe splitting each picture in half and stitching both halves?? But I doubt that's what my professor wants me to do.

And besides, I'm honestly not even completely sure if this photos are actually in a front view perspective, because when I tried comparing them with the actual image that the professor gave us to help, the ones I got still look a bit skewed, and it's not like I can use the solution in order to help get the real coordinates so... I'm a bit lost on what to do.

In case it helps, these are the exact instructions we have:

1. Writing a program to read JPG images, calculating the homography matrixes between them, and try to project part of them into a front view. Note: the frame of the painting is a circle.

2. Please manually find at least 5 matching points in both images to find the homography, and eleminate the people to have a clean painting. Finally, please convert into (ex. fill in) a perfect circle. Save your result as a JPG file (named as Student_ID.jpg).

3. In this homework, you can use any method including third-party lib. to perform, but please do NOT directly use any commercial software to create the image for this assignment.

Hi there everyone! I recently received a couple of project proposals for implementation of a MV system for quality control of spare parts. Ive studied the case with an expert and deep learning approach might be the best option. Mainly because cycle times are pretty short and differences are too tight for using metrology or other approach.

Having said that, anyone with experience in MVTec, Keyence and vision pro from cognex? Bearing in mind that I live in Europe, id like to know about their tech support, price and learning curve.

Related to MVTEC, What's the conventional hw for embedding? I recently read that thatthey suggest arm ones so not pretty sure if a Jetson or an industrial IPC might fit.

Thanks a lot!

Hi everyone, I want to run a 6D pose estimation algorithm on an Android phone. I don’t need a high frame rate, around one frame per second is sufficient. The target is a known object (e.g., a table or chair), and I already have its 3D model from photogrammetry. I only have a standard RGB camera (no depth sensor).

What is the best 6D pose estimation library or algorithm for this setup? Ideally, it should be easy to use, lightweight enough to run on a mobile device, and preferably free or open-source. Thanks!

I'm an avid amateur Astro imager. I can't write code anymore as I'm not 80 years old. I've always got ideas for things that I could use to improve my experience and capabilities. So, now that I have a ChatGPT subscription I decided to give it a whirl. What I learned was the ChatGPT was overly optimistic about its domain knowledge when it came to astro-imaging. It would congratulate me over and over again as I slowly but surely went down a rabbit hole with my first project. Eventually, it told me that my project and approach were simply "a fantasy". It was correct but it only told me that when I gave up. Lesson is that if you want code written by ChatGPT you'd better figure out exactly what you want. Don't expect it to have a clue about any specialized domains. Anyone have similar experiences?

I'm working on a computer vision project where merchandisers take pictures of store shelves. My task is to detect the products in the image so I can identify competitors vs. my company's products.

I thought about two approaches:

1. Use YOLO to detect products on the shelves, annotate them, and train a model to classify which products belong to my company.

2. Create folders with images of each company's products, generate embeddings for them (possibly using OCR to extract and embed text), and when a new image arrives use vector search to identify which company the product belongs to.

Does this make sense, or is there a better approach for this problem?

(note that I don't have big resources to train a big model)

thanks in advance

I have a series of photographs of different core boxes, which are a uniform rectangular container used to hold and display drill core. A tedious part of my job right now is manually cropping in on the core tray of each photograph, which is a task I'd rather automate.

Since the photographs are taken by hand, there is often a slight angle, so a bounding box parallel to the axis of the photograph won't be sufficient. I need a polygon which tightly encompasses the core tray, with four nodes, one for each corner of the tray. For this reason I believe I need instance segmentation rather than object recognition, please correct me if I'm wrong.

I started off by training a Yolo11m-seg model on 150 photographs which I annotated myself. I left all other parameters as their defaults. The results were subpar, the predictions were consistently significantly smaller than my annotations, which would cut off the edges of my core trays.

I think my model may have failed to learn that the core (highly variable) displayed withing the trays is irrelevant, the edges of the trays are all that matter.

I have tried to upgrade to a YOLO11l-seg model hoping it would be smarter but I always get a memory crash out on my 8GB of RAM even after setting the batch size to 2 and the number of workers to 0.

Any advice on how to train a model which can accurately make a tight bounding polygon based on the four corners of a core tray would be appreciated.

I tried building a simple self-hosted fitness tracker… and it kind of spiraled into this.

It actually started pretty dumb:
I was doing pushups in my basement and thought “couldn’t a camera just count reps and maybe draw a skeleton on top?”

I had played around with face recognition before, and since training isn’t really optional for me (Parkinson), I figured… why not try.

The first PoC was:

• Ubuntu 20.04
• an old NVIDIA Tesla P4
• a single Reolink IP cam

It worked… badly. But enough to get hooked.

Then things escalated:

• added more cameras (ended up with 3)
• tried doing proper multi-view + 3D reconstruction
• spent ~2 weeks in calibration hell (Charuco boards, triangulation, you name it)

At one point I thought I was clever and rotated the cameras 90° to get better vertical resolution.

That decision alone probably cost me several years of life:
cw/ccw confusion, projection errors, reprojection errors… everything was wrong in ways that almost looked right.

Even when pose detection worked perfectly per stream, 3D fusion would just refuse to cooperate.

Also learned the hard way:

• cheap IP cams + no real timestamps = synchronization nightmare
• Tesla P4 + 3D = technically possible, practically suffering

There was a brief detour with an Insta360 over USB (v4l2)… which was about as stable as you’d expect.

Current setup (less cursed, still questionable life choices):

• AMD server + NVIDIA A2
• 1× Basler 4K industrial cam (side view)
• 2× IP cams (front)
• RTMPose (133 keypoints) + MotionAGFormer (2D→3D)
• hybrid multi-view approach with an “anchor stream” + auxiliary views

Now it can (more or less):

• track full body (including hands/face)
• count reps (state-machine based)
• evaluate form (depth, symmetry, tempo, alignment, etc.)
• render a live 3D model on the TV
• identify the user via face recognition
• log everything down to individual reps in SQLite

There’s also a (very early) voice coach and a YAML-based exercise system.

Where I want to take this:

• better 3D visualization (SMPL-X instead of current prototype)
• more robust scoring (right now it’s still pretty basic)
• eventually a “real” coach that adapts workouts based on training history

Also worth mentioning:
Without tools like Codex / Claude I probably wouldn’t have been able to build this at all. This project is way beyond what I could realistically code solo from scratch.

What I’m curious about:

• multi-view CV setups: how do you handle sync/calibration reliably in real-world setups?
• better approaches for exercise phase detection than simple state machines?
• stabilizing 2D→3D lifting in noisy environments
• or just general “you’ve gone too far” feedback

Would love to hear thoughts or similar projects.

https://preview.redd.it/97c1kmglo0tg1.jpg?width=1080&format=pjpg&auto=webp&s=f08f5bd353354b87d89ab2739263c29d96b7a3fa

So I am new to coding And I genuinely need Help in coding and making things, So actually it's a problem Statement which is not from Our College or curriculum, and we are not thought as high end level of programming in our institution, So we have to make an Application which will take data from IOT devices or satellite images to detect disaster early and give warning , so is there any API or LLM made on this which would Help us to make it are there anything related to it which is missing by me, And sorry to interrupt cuz i am also learning sometimes from friends, classmate, internet or ChatGPT has been my Buddy , plss tell me imp sources related to this project

Can YOLO be used to track the position of an object as well as an April Tag? Or is YOLO Just good for saying hey found it but not so much for tracking movement in space over time?

Also for a pi 4 would April Tags be faster/cheaper and more accurate than YOLO?

I think a lot of document workflow pain comes from queue design, not just extraction quality.

A system can parse plenty of pages and still create operational drag if every unclear case lands in one generic review bucket.

What breaks

• Blurry images, layout shifts, changed versions, and missing fields all look the same in the queue
• Retries and review-worthy cases compete with each other
• Reviewers have to open each case before they even know what kind of issue they’re looking at

What I’d do

• Split exceptions by reason instead of one catch-all queue
• Attach source-page context and extracted output to each flagged case
• Separate infrastructure retries from document-specific review flow

Options shortlist

• General OCR/document APIs plus your own routing layer
• Internal review tooling with better queue metadata
• Queue/orchestration systems for prioritization and triage
• Document ops tools built around exception handling

My bias is that “human in the loop” only helps if the reviewer gets useful context fast.

Curious how others structure exception types in production. If you’ve found a cleaner queue pattern for messy documents, I’d genuinely like to hear it.

Hello,

I am preparing my first arXiv submission in the cs.CV category and I am currently looking for an endorser.

The paper focuses on thin-obstacle segmentation for UAV navigation (e.g., wires and branches), which are particularly challenging due to low contrast and extreme class imbalance. The approach is a modular early-fusion framework combining RGB, depth, and edge cues, evaluated on the DDOS dataset across multiple configurations (U-Net, DeepLabV3, pretrained and non-pretrained).

If anyone with cs.CV endorsement is open to taking a quick look and possibly endorsing, I would really appreciate it.

Thank you in advance!

I'm creating an attendance system for a capstone project that has facial recognition, and liveness detection. Problem is, I don't exactly know where to start with the facial recognition and liveness detection.

if there are any open source models, where do I get them, and what would be the downsides I could face in using them

and I don't think I'm equipped with the right things to train a model. how does training a model work and what would I need to do so?