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#computervision

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Karsten Schmidt

To avoid a massive OpenCV dependency for a current project I'm involved in, I ended up porting my own homemade, naive optical flow code from 2008 and just released it as a new package. Originally this was written for a gestural UI system for Nokia retail stores (prior to the Microsoft takeover), the package readme contains another short video showing the flow field being utilized to rotate a 3D cube:

thi.ng/pixel-flow

I've also created a small new example project for testing with either webcam or videos:

demo.thi.ng/umbrella/optical-f

GIF
#ThingUmbrella#OpticalFlow#ImageAnalysis
Public
deadprogram

Both of my talks from @fosdem last weekend are now available online.

"Return To Go Without Wires" about using Go/TinyGo to make your own AirTags without any Apple hardware:

cuddly.tube/w/p/2H3BJMkJZEJRUS

"Seeing Eye to Eye: Computer Vision using wasmVision" in the first ever WebAssembly dev room at FOSDEM:

video.fosdem.org/2025/k4601/fo

PeerTubeGo Devroom @ FOSDEM 2025By Go Devroom @ FOSDEM
#fosdem#fosdem2025#golang
Public
JimmyChezPants

Hey #OpenCV #ComputerVision #Python

I would like to point a camera at an area of the house and have it announce when a dog has entered the camera frame.

I am quite handy with Python and can muddle my way through C-like stuff if I have good documentation or example code.

Is this easy or hard? Hard is not a dealbreaker, just trying to tune my expectations a bit.

Public
Miguel Afonso Caetano

Beyond Fairness in Computer Vision: A Holistic Approach to
Mitigating Harms and Fostering Community-Rooted Computer
Vision Research

Timnit Gebru and Remi Denton

"ABSTRACT: The field of computer vision is now a multi-billion dollar enterprise, with its use in surveillance applications driving
this large market share. In the last six years, computer vision researchers have started to discuss the risks and harms of some of these systems, mostly using the lens of fairness introduced in the machine learning literature to perform this analysis. While this lens is useful to uncover and mitigate a narrow segment of the harms that can be enacted through computer vision systems, it is only one of the toolkits that researchers have available to uncover and mitigate the harms of the systems they build.

In this monograph, we discuss a wide range of risks and harms that can be enacted through the development and deployment of computer vision systems. We also discuss some existing technical approaches to mitigating these harms, as well as the shortcomings of these mitigation strategies.

Then, we introduce computer vision researchers to harm mitigation strategies proposed by journalists, human rights activists, individuals harmed by computer vision systems, and researchers in disciplines ranging from sociology to physics. We conclude the monograph by listing principles that researchers can follow to build what we call community rooted computer vision tools in the public interest, and give examples of such research directions. We hope that this monograph can serve as a starting point for researchers exploring the harms of current computer vision systems and attempting to steer the field into community-rooted work."

cdn.sanity.io/files/wc2kmxvk/r

#ComputerVision#STS#Surveillance
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