标签：#computervision

🚀 Hello, DEV Community! I'm Nader Al Shawki , a final-year AI Engineering student at Al-Razi University, Yemen. This is my first post here, and I'm excited to start sharing my journey with this amazing community. 🎯 Who Am I? I'm passionate about building production-grade AI systems that solve real-world problems. My main areas of focus are: 🖼️ Computer Vision & Deep Learning 🤖 ML Model Deployment (Docker, FastAPI, REST APIs) 🧠 LLMs, RAG, and AI Agents (currently learning) 📊 Data Visualization & Analytics (Power BI) 💡 What I've Built So Far 1. 🍅 Tomato Leaf Disease Detection Platform Tech: YOLOv8, PyTorch, FastAPI, Docker What it does: Detects tomato leaf diseases from images with real-time inference. Containerized with Docker for easy deployment. 2. 🫁 Pneumonia Detection System Tech: PyTorch, CNN Architecture, Medical Imaging What it does: A deep learning model that detects pneumonia from chest X-ray images. 3. 📊 Sales Profit Analysis Dashboard Tech: Power BI, DAX, Data Analysis What it does: Interactive dashboard for tracking sales KPIs. 4. 😀 Face Detection & Emotion Recognition Tech: OpenCV, Deep Learning What it does: Real-time face detection, age estimation, emotion recognition, and gender classification. 5. 🍽️ Restaurant Website Tech: HTML5, CSS3, JavaScript What it does: Fully responsive restaurant website with interactive UI. 🌱 What I'm Currently Learning LLMs (Large Language Models) RAG (Retrieval-Augmented Generation) LangChain & AI Agents Workflow automation with n8n 🔗 Let's Connect 🐙 GitHub: Naderalshawki 💼 LinkedIn: in/nader-al-shawky 📫 Email: naderalshawki@gmail.com Thanks for reading! I'll be posting regularly about AI projects, tutorials, and lessons learned. Stay tuned! 🚀

Every hand-tracking demo shows you 21 dots. The interesting part is what nobody shows: turning dots into numbers someone can act on. Dots are a capability, not a product Run any modern hand-tracking model and you get 21 beautifully stable landmarks per hand at 30 FPS. Impressive — and by itself, worthless. No client has ever paid for dots. They pay for measurements : is this clearance compliant, is this part aligned, did this patient's range of motion improve. I learned this on utility infrastructure work, where the deliverable was never "we detected the wire" — it was the attachment height of that wire, and whether it violates clearance rules . Keypoints were step one of three. The demo: live metrics, not just a skeleton My portfolio's keypoint demo derives three measurements per hand, every frame: const wrist = lm [ 0 ]; const palm = distance ( wrist , lm [ 9 ]); // scale reference const pinch = distance ( lm [ 4 ], lm [ 8 ]) / palm ; // thumb tip ↔ index tip The crucial line is the scale reference . Pixel distances are meaningless — they change as you move toward the camera. Dividing by palm length (wrist to middle knuckle) gives a relative measurement that's stable under distance, and multiplying by the average adult palm length (~8.5 cm) converts it into an approximate real-world gap — the demo shows "≈ 3.2 cm" floating on the pinch line. In infrastructure work the same role is played by a known object dimension — a standard crossarm, a pole class height. Every measurement-from-pixels system needs its ruler. Finger counting is a geometric test (is each fingertip farther from the wrist than its middle joint?), and "hand openness" averages fingertip extension — three lines of geometry each, but they convert a model output into a readout a human understands instantly. Honest layering The landmarks come from MediaPipe's pretrained pipeline (palm detector → landmark regressor → gesture classifier, float16, WASM + GPU delegate) — Google's models, credited on the page

Camouflage has always been graded by human eyes. But the thing hunting for you in 2026 is increasingly a detection model — so test against that. The premise Surveillance is automated now: drones, trail cameras, perimeter systems — most of what "sees" runs an object-detection network. Which makes traditional camouflage evaluation (a person squinting at a photo) the wrong test. The right test is adversarial: run the actual detector against your concealment and measure what it finds. That's the whole demo: upload a photo, and an object-detection model hunts for people in it — at four simulated distances — producing a detection-range profile and a stealth score. Simulating distance with pixels You can't move the camera after the photo is taken, but you can simulate the dominant factor in long-range detection: pixels on target . A person at 50 m simply occupies far fewer pixels than at 5 m. So each analysis run downscales the image progressively and re-runs detection: const DISTANCE_LEVELS = [ { label : ' Close (~5m) ' , scale : 1 }, { label : ' Mid (~15m) ' , scale : 0.45 }, { label : ' Far (~30m) ' , scale : 0.22 }, { label : ' Very far (~50m) ' , scale : 0.12 }, ]; for ( const level of DISTANCE_LEVELS ) { const scaled = drawScaled ( image , level . scale ); const detections = await model . detect ( scaled , 10 , 0.15 ); // best 'person' confidence at this simulated range } The output reads like a range card: detected at 5 m with 96% confidence, 41% at 15 m, invisible beyond 30 m. A stealth score aggregates it: how poorly did the adversary see you, averaged across ranges? Honest about the model The detector is COCO-SSD (a pretrained MobileNet-based model from the TensorFlow.js team) running entirely on-device — I didn't train it, and the demo says so on the page. The contribution here is the evaluation framework : using detectors as adversaries, simulating range, and turning subjective "good camo" into a measurable profile. The full version of this concept goes further

A 1990s Nobel-adjacent therapy, a webcam, and 21 hand keypoints — recreating the mirror-box illusion for phantom limb pain, no hardware required. A therapy built on an illusion In the 1990s, neuroscientist V.S. Ramachandran discovered something remarkable: amputees suffering phantom limb pain often felt relief just by seeing their missing limb move again. His apparatus was almost comically simple — a box with a mirror. Put your intact hand in, look at its reflection where the missing hand would be, and move. The brain, watching the "missing" hand obey commands again, often dials the pain down. The limitation was never the science. It was the box: a physical apparatus, used in clinics, hard to scale, impossible to measure. Replacing glass with keypoints A webcam plus real-time hand tracking can produce the same illusion with better properties: webcam frame → hand landmark model (21 keypoints, on-device) → reflect: phantom[i] = { x: 1 − x, y, z } → render real hand (solid) + phantom twin (ghost) on canvas The reflection is one line of math. Everything around it is what makes the illusion land: const phantom = real . map ( p => ({ x : 1 - p . x , y : p . y , z : p . z })); The visual treatment matters more than I expected. The phantom hand is rendered as a ghostly cyan skeleton with a translucent palm fill, a "breathing" glow that pulses on a ~3 second cycle, and a fading afterimage trail of its last few frames — it reads as present but ethereal , which is exactly the perceptual story mirror therapy needs to tell. A dashed mirror plane down the center of the frame makes the reflection relationship legible at a glance. The engineering details that matter Tracking : MediaPipe HandLandmarker (Google's pretrained model — credit where due), running via WebAssembly with GPU delegate. ~30 FPS on a laptop. Privacy by architecture : every frame is processed on-device. For a medical-adjacent application, "video never leaves your browser" isn't a feature, it's a requirement. Lazy

[Paper Notes] JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture 🔗 TL;DR: JEPA learns a a generalized semantic representation with less data pairs by predicting missing information in the embedding space , which helps it disregard unnecessary noisy from input(pixel)-level details and learns at a higher abstraction level with good semantic generalization. 1. Innovation & Significance The Bottleneck: Image-text data pair labels are hard to find Pixel level pre-training paired & data augmentation are strongly biased towards trained data distribution, hard to determine proper generalization and level of abstraction. JEA's (Joint Embedding Architecture) collapse probelm: encoder & decoder attempts to cheat by always landing on trivial constant when predicting itself (reconstruction) and gets away with an easy Error=0. The Solution: > Chain-of-thought ⭕ Mask pre-training to reduce data & generalize↓❌ Bad/lower semantic representation without semantic target, could be learning noisy local pixel correlation↓⭕ Learn at the embedding level to omit pixel input and generalize⭕ Adds context encoder & positional encoding to inject context and force model to pick up image inherent structure from reconstructing multiple masked patches with one target.↓❌ JEAs wants to cheat: if I always map all pixels to a constant for both the predictor and end target encoder then the reconstruction error is always collapsed to zero! Hehe~ ↓ ⭕ EMA (Exponential moving avg.): Update target encoder parameters from the EMA of context encoders. This 'delays' the target encoder to prevent collapsing (a trick from the BYOL paper[2020], proven essential to training JEAs with ViT). 2. Model & High-Level Intuitions 2.1 Model Architecture Input: randomly samples block masks from original image within certain aspect ratio changes, and apply mask for context image 2.1.2 Context Context Encoder: ViT encodes context image to embedding SxS_x S x ​ Mask Token : an [1,D] random

Why your age-gating algorithm is probably doomed to fail in the wild For developers building in the computer vision and biometrics space, there is a massive gap between a model that passes a NIST benchmark and a system that survives the "child-with-a-VPN" test. Recent data indicates that roughly 32% of children are successfully bypassing age-gating tech. As engineers, our first instinct is often to blame the model—to tweak the weights, gather more training data, or tighten the threshold. But the technical reality is more sobering: the failure isn't in the algorithm; it's in the deployment architecture. The Problem with Probabilistic Logic in Binary Workflows Most age estimation models rely on analyzing biometric markers—skin texture, bone structure ratios, and periocular geometry. They produce a probabilistic age range. However, according to NIST's evaluation of age estimation software, to maintain a low false-positive rate, systems often need to set a "challenge age" between 29 and 33 years. If you are a dev tasked with keeping 17-year-olds off a platform, you are essentially forced to build a "buffer zone" of over a decade. If the system flags anyone who might be under 30, the UX becomes a nightmare. If you lower the threshold to 18, the false-negative rate skyrockets. This is the fundamental trade-off of probabilistic facial analysis: precision and recall are at constant war, and in a high-traffic production environment, the "noise" of real-world variables (poor lighting, low-res sensors, off-axis angles) makes consistency nearly impossible. The Breakdown of the Identity Handoff Beyond the model, there are three technical failure points that no amount of Euclidean distance analysis can fix if the pipeline is broken: The Signal-to-Noise Ratio at Source: Evaluation datasets are clean. Production images are taken on scratched lenses in low-light bedrooms. The delta between training distribution and inference-time reality is where the first 10% of accuracy vanishes.