Today I tested Segment Anything Model (RX 6700 XT / 16 GB RAM)

SAM was developed by Meta and is a local FOSS AI model for image segmentation. It can detect objects in images and generate precise masks, making it possible to isolate people or objects from the background.

In my test, I automatically selected the largest detected mask, isolated it, and processed it further. This allows subjects to be separated quickly from the background for image editing, compositing, stickers, memes, or creative video effects.

Model used: sam_vit_h_4b8939.pth

SAM can be used for:
- Object cutouts
- Image editing & photomontage
- Video compositing & post-production

A newer version, Segment Anything Model 2, extends these capabilities and is especially designed for video segmentation, providing better temporal consistency across frames and more stable object tracking over time.

Video workflow:
- Recorded with OBS
- Edited in Kdenlive
- Transcoded with VAAPI (H.264)

No cloud, real hardware.
Everything runs on Linux, so anyone can set this up.
Works on CPU as well, but much slower.

Background music: Sweet but Psycho - Ava Max [Rock Version by Kenke] (https://www.youtube.com/watch?v=714BjMMUi-s)

#AI #MachineLearning #ComputerVision #ImageSegmentation #SAM #SegmentAnything #MetaAI #FOSS #OpenSource #DeepLearning #ImageEditing #Compositing #VideoEditing #Tech #Innovation

💡 Góc nhìn từ Nhân Hòa về Image Segmentation

Không có một phương pháp nào “tốt nhất” cho mọi bài toán.

👉 Thực tế:

Bài toán đơn giản → dùng kỹ thuật cổ điển
Bài toán phức tạp → Deep Learning là lựa chọn hàng đầu
Xu hướng mới → Hybrid + Foundation Model (SAM)

🔥 Điều này mở ra khả năng:

Zero-shot segmentation
Giảm phụ thuộc dữ liệu lớn

📌 Đọc phân tích đầy đủ tại Nhân Hòa:
https://nhanhoa.com/tin-tuc/image-segmentation-la-gi.html

#ImageSegmentation #phanvungcanh #ai #nhanhoa

We're reintroducing and open-sourcing project "See-through". Given a single anime illustration, it automatically decomposes the character into fully-inpainted semantic layers with depth ordering. One image in, layered PSD out. (1/n)

Repo:
https://github.com/shitagaki-lab/see-through…

https://x.com/ljsabc/status/2038907277017776441

#anime #imagesegmentation #semanticsegmentation #depthestimation #opensource

Want to use AI for good? 🌊

Check out this YOLOv8 Segmentation tutorial for real-world flood detection. Eran Feit breaks it all down:

💻 **Code**: [https://eranfeit.net/yolov8-segmentation-tutorial-for-real-flood-detection/](https://eranfeit.net/yolov8-segmentation-tutorial-for-real-flood-detection/)
📺 **Video**: [https://youtu.be/diZj_nPVLkE](https://youtu.be/diZj_nPVLkE)

#YoloV8 #ImageSegmentation #FloodDetection #AI

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**Would you like me to generate a shorter version specifically for a LinkedIn "hook" or a different social platform?**

Melvin Vivas (@donvito)

Google AI Edge 관련 소식으로, LiteRT가 실시간 가속 이미지 분할을 지원한다고 발표되었습니다. Android용 핸즈온 튜토리얼(Codelab)이 제공되어 엣지 디바이스에서 이미지 분할을 가속하는 개발을 바로 실습해볼 수 있습니다.

https://x.com/donvito/status/2013158201567354899

#google #litert #imagesegmentation #android #edgeai

Melvin Vivas (@donvito) on X

Google AI Edge LiteRT supports real-time accelerated image segmentation Hands-on tutorial for Android https://t.co/1tIVIw3znX

X (formerly Twitter)

Top 10 Image Annotation Services Transforming Computer Vision in 2026

Explore the leading Image Annotation Services transforming AI in 2026. These top providers offer expert labeling for object detection, segmentation, and classification, helping build robust computer vision models across industries like healthcare, autonomous driving.

Know More: https://telegra.ph/Top-10-Image-Annotation-Services-Shaping-Computer-Vision-in-2026-12-08

#imageannotation #datalabeling #imagesegmentation #objectdetection #techtrends2026

🌟 Get ready to catch The Perfect Wave: AI-Powered Advances in Image Segmentation at #ISMRM2025, featuring a presentation by Jong Sung Park!
🗓️ Date: Monday, May 12, 2025
🕓 Session Time: 4:00 PM (Hawaii Time)
🕒 Presentation Time: 4:36 PM (Hawaii Time)
Join Jong Sung Park as they dive into the latest breakthroughs in AI-driven image segmentation. A must-see for anyone passionate about innovation in neuroimaging!
#ISMRM #ImageSegmentation #ArtificialIntelligence #MedicalImaging

How to segment X-Ray lungs using U-Net and Tensorflow

You can find link for the code in the blog : https://eranfeit.net/how-to-segment-x-ray-lungs-using-u-net-and-tensorflow/

Check out our tutorial here : https://youtu.be/-AejMcdeOOM&list=UULFTiWJJhaH6BviSWKLJUM9sg

Enjoy
Eran

#Python #openCV #TensorFlow #ImageSegmentation #Unet #Resunet #Segmentation

How to segment X-Ray lungs using U-Net and Tensorflow – Eran Feit

Building a simple medical image segmentation tool in #JuliaLang.

So far the method only creates raw contours. Next I need to add contour cutting, merging, spline based smoothing, growing/shrinking, manual drawing, etc. and before you know it we have a high performance and #opensource tool for medical image segmentation!

#ImageSegmentation #ImageProcessing #Biomechanics #Makie

Medical Melanoma Detection | TensorFlow U-Net Tutorial using Unet

Check out our tutorial here : https://youtu.be/P7DnY0Prb2U&list=UULFTiWJJhaH6BviSWKLJUM9sg

My blog : https://eranfeit.net/blog/

Enjoy
Eran

#Python #openCV #TensorFlow #Deeplearning #ImageSegmentation #Unet #Resunet

Medical Melanoma Detection | TensorFlow U-Net Tutorial using Unet

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