Techniques for Computer Vision

Computer Vision (CV) algorithms are the "eyes" of AI. They allow machines to not just capture pixels, but to understand 𝐨𝐛𝐣𝐞𝐜𝐭𝐬, 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬, 𝐚𝐧𝐝 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬. From autonomous driving to medical imaging, choosing the right algorithm is a balance of 𝐬𝐩𝐞𝐞𝐝, 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲, 𝐚𝐧𝐝 𝐡𝐚𝐫𝐝𝐰𝐚𝐫𝐞 constraints. 𝟏. 𝐎𝐁𝐉𝐄𝐂𝐓 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 (𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐯𝐬. 𝐇𝐢𝐠𝐡 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧) 𝐘𝐎𝐋𝐎 (𝐘𝐨𝐮 𝐎𝐧𝐥𝐲 𝐋𝐨𝐨𝐤 𝐎𝐧𝐜𝐞): The industry standard for 𝐬𝐩𝐞𝐞𝐝. It processes the entire image in a single pass, making it ideal for real-time video feeds (e.g., security cameras, self-driving cars). 𝐑-𝐂𝐍𝐍 / 𝐅𝐚𝐬𝐭𝐞𝐫 𝐑-𝐂𝐍𝐍: Focuses on 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲. It uses region proposals to find objects, which is slower but much more precise for complex scenes. 𝟐. 𝐅𝐄𝐀𝐓𝐔𝐑𝐄 𝐌𝐀𝐓𝐂𝐇𝐈𝐍𝐆 & 𝐄𝐃𝐆𝐄 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 Before deep learning, we relied on mathematical feature extractors. These are still vital for low-power devices: 𝐎𝐑𝐁 (𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐅𝐀𝐒𝐓 𝐚𝐧𝐝 𝐑𝐨𝐭𝐚𝐭𝐞𝐝 𝐁𝐑𝐈𝐄𝐅): A fast, open-source alternative to SIFT/SURF. It identifies key points in an image to match them across different frames. 𝐂𝐚𝐧𝐧𝐲 𝐄𝐝𝐠𝐞 𝐃𝐞𝐭𝐞𝐜𝐭𝐨𝐫: A multi-stage algorithm used to detect a wide range of edges in images, providing the structural skeleton of an object. 𝟑. 𝐒𝐄𝐆𝐌𝐄𝐍𝐓𝐀𝐓𝐈𝐎𝐍 (𝐏𝐢𝐱𝐞𝐥-𝐋𝐞𝐯𝐞𝐥 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠) 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: Labels every pixel in an image with a category (e.g., "Road," "Sky," "Pedestrian"). 𝐈𝐧𝐬𝐭𝐚𝐧𝐜𝐞 𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 (𝐞.𝐠., 𝐌𝐚𝐬𝐤 𝐑-𝐂𝐍𝐍): Goes a step further by distinguishing between individual objects of the same class (e.g., identifying Person 1 vs. Person 2). 𝟒. 𝐓𝐇𝐄 𝐍𝐄𝐖 𝐅𝐑𝐎𝐍𝐓𝐈𝐄𝐑: 𝐕𝐈𝐒𝐈𝐎𝐍 𝐓𝐑𝐀𝐍𝐒𝐅𝐎𝐑𝐌𝐄𝐑𝐒 (𝐕𝐢𝐓) 𝐕𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬: Unlike traditional CNNs that look at local pixel neighborhoods, ViTs split images into patches and use 𝐒𝐞𝐥𝐟-𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 to capture global context. 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞: Handling highly complex patterns where the relationship between distant parts of an image is crucial. 💡 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐂 𝐓𝐑𝐀𝐃𝐄-𝐎𝐅𝐅𝐒 𝐋𝐢𝐦𝐢𝐭𝐞𝐝 𝐇𝐚𝐫𝐝𝐰𝐚𝐫𝐞? → Use 𝐎𝐑𝐁 or 𝐌𝐨𝐛𝐢𝐥𝐞𝐍𝐞𝐭 (Lightweight CNN). 𝐍𝐞𝐞𝐝 𝐌𝐢𝐥𝐢-𝐬𝐞𝐜𝐨𝐧𝐝 𝐋𝐚𝐭𝐞𝐧𝐜𝐲? → Use 𝐘𝐎𝐋𝐎. 𝐃𝐞𝐞𝐩 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠? → Use 𝐕𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬. 🔥 𝐓𝐇𝐄 𝐁𝐎𝐓𝐓𝐎𝐌 𝐋𝐈𝐍𝐄: A great model is nothing without great data. In 2026, the focus has shifted from just "tuning algorithms" to 𝐝𝐚𝐭𝐚-𝐜𝐞𝐧𝐭𝐫𝐢𝐜 𝐀𝐈. Experimenting with data augmentation, annotation quality, and batch composition is often more effective than simply switching architectures. #𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐕𝐢𝐬𝐢𝐨𝐧 #𝐀𝐈 #𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 #𝐘𝐎𝐋𝐎 #𝐕𝐢𝐬𝐢𝐨𝐧𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫

Canny Edge Detection is one of the most carefully engineered algorithms in computer vision. 🤖 Rather than relying on heuristics, Canny formulated edge detection as a constrained optimization problem with explicit and competing objectives: maximizing detection probability, minimizing localization error, and suppressing multiple responses to a single edge. From this analysis emerged a complete and principled pipeline: Gaussian smoothing for noise suppression, gradient estimation, non-maximum suppression for spatial precision, and hysteresis thresholding for robust edge continuity. What makes Canny especially notable is how closely modern implementations still follow this original theoretical design. Nearly every practical variant used today is a direct consequence of the same mathematical reasoning introduced in 1986.

I have been teaching computer vision from scratch for the last 8 months on Vizuara's YouTube channel and have been receiving great feedback. This is an extremely comprehensive course in which 46 lectures have been released. I cover all the topic from tranditonal filters (before 2012), CNNs (2012-2020) and transformers for vision (2019 onwards). For anyone interested in mastering computer vision with zero pre-requisites and just interest, this is the best resource. This playlist will eventually become one of the most comprehensive computer vision lecture series on the internet. I plan to add another ~20 lectures on multimodal LLMs in this playlist in the next 3 months. Here are the lectures released so far. Introduction https://lnkd.in/gRTyJAke Filters and Convolution https://lnkd.in/gcV6-Srh Simple Neural Network https://lnkd.in/gfTrZK_R Image Classification Network https://lnkd.in/gTBGZxZu Hyperparameter Tuning W&B https://lnkd.in/gSzQTHrc Overfitting and Regularization https://lnkd.in/gaWSRzxD Transfer Learning Basics https://lnkd.in/gBMsCcQU AlexNet Explained https://lnkd.in/gPNFcjHD VGGNet Explained https://lnkd.in/g_-pkcrA Inception V1 Explained https://lnkd.in/gPEfNX2X SqueezeNet Story https://lnkd.in/g8UbtGh8 ResNet Explained https://lnkd.in/gZfxt78d MobileNet Overview https://lnkd.in/g8EwjF6d DenseNet EfficientNet https://lnkd.in/g2UidM3S NASNet Explained https://lnkd.in/gqqvue6n CNN Evolution Timeline https://lnkd.in/gZhxQEZi Hands-on CV Bootcamp https://lnkd.in/gqtVHQVc R-CNN Object Detection https://lnkd.in/g8T6_aUK Mask R-CNN Segmentation https://lnkd.in/gUPkwSeh UNet https://lnkd.in/gXAeUMAP YOLO Introduction https://lnkd.in/gZbg9MqS Roboflow Overview https://lnkd.in/guHVJ2uJ Fall Detection Project https://lnkd.in/gCzSvPRF Transformers for Vision https://lnkd.in/gZNesbFe CNN vs Transformer https://lnkd.in/gxFh5Evc Token Journey https://lnkd.in/gSAhzmMk Self-Attention Intro https://lnkd.in/g9eWE3Wq QKV Intuition https://lnkd.in/gkSstDKi Causal Attention https://lnkd.in/g-gKk-yk Multi-Head Attention https://lnkd.in/gKfvMcJ5 ViT from Scratch https://lnkd.in/gJDNpdqp Contrastive Learning https://lnkd.in/g28tr62u NanoVLM https://lnkd.in/geusb-fZ

Computer vision isn't just for photo filters anymore. It's preventing accidents in real-time. I’m fascinated by this demonstration of a predictive AI safety system. It's a masterclass in how multiple computer vision tasks can work together to create something incredibly powerful. Here's the breakdown of the tech in action: ► Detection & Classification: It accurately identifies cars, buses, and even pedestrians. ► Tracking & Speed Analysis: It follows objects frame-by-frame, continuously calculating their speed. ► Collision Prediction: The system uses speed and trajectory data to calculate Time-to-Collision (TTC) and proximity warnings. The "DANGER ALERT" isn't just a guess; it's a data-driven prediction. The fact that this works seamlessly from day to night is a huge testament to the sophistication of the algorithms. This is the kind of technology that will redefine what's possible for intelligent transportation systems and vehicle safety. Where else could this predictive capability be a game-changer? #deeplearning #computervision #python #opencv #speedtracking #trafficanalysis

𝐁𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐕𝐢𝐬𝐢𝐨𝐧 𝐭𝐨 𝐋𝐢𝐟𝐞: 𝐅𝐫𝐨𝐦 𝐊𝐞𝐲𝐩𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 3𝐃 𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐎𝐧 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞! Imagine labeling objects with precise keypoints, unlocking the ability to map the world in 3D, and performing accurate object detections all running seamlessly on a NVIDIA Jetson device powered by Ultralytics YOLOv11 Pose! The detections are laser focused, happening only within defined regions of interest (ROIs) without relying on complex zone trackers. Why this is innovative: ↳ 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐝 𝐏𝐫𝐞𝐜𝐢𝐬𝐢𝐨𝐧: Keypoint tagging builds detailed 3D blueprints for objects, ensuring accurate detection in designated areas. ↳ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐀𝐰𝐚𝐫𝐞𝐧𝐞𝐬𝐬: 3D detection provides insights into size, orientation, and spatial relationships within ROIs. ↳ 𝐎𝐧-𝐃𝐞𝐯𝐢𝐜𝐞 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲: Running on Jetson ensures fast, reliable processing with low latency, even for real-time applications. ↳ 𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧𝐬: By limiting processing to ROIs, resource usage is optimized, improving speed and accuracy without the need for zone trackers. 𝘏𝘰𝘸 𝘪𝘵 𝘸𝘰𝘳𝘬𝘴: ↳ Label objects with key points using YOLOv11 Pose for accurate pose estimation. ↳ Define regions of interest manually or programmatically to focus detections. ↳ Leverage annotations to build 3D models and refine them using warping techniques for accurate scaling and orientation. ↳ Perform object detection exclusively within these ROIs, reducing noise and enhancing performance all on the Jetson platform. 𝐏𝐫𝐨 𝐓𝐢𝐩: By focusing on ROIs instead of tracking zones, you simplify the pipeline, ensuring faster, more reliable detections while preserving the computational efficiency needed for edge devices like Jetson. 𝐖𝐡𝐚𝐭 𝐈 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐞𝐝: Integrating keypoint labeling, warping techniques, and ROI-based detections without relying on zone tracking allowed me to measure objects in 3D with unmatched precision. All this happens locally on a Jetson, making it perfect for edge solutions that demand accuracy and speed in real-time. Whether you’re building smarter robots, optimizing industrial processes, or creating AR/VR applications, this workflow revolutionizes computer vision at the edge with simplicity and power. What’s your take on combining keypoints, 3D detection, ROIs, and edge devices like Jetson? Let’s discuss in the comments! ♻️ Repost to your LinkedIn followers and follow Timothy Goebel for more actionable insights on AI and innovation. #ComputerVision #3DDetection #Keypoints #YOLOv11Pose #Jetson #EdgeAI #RegionOfInterest #ObjectDetection #Warping #AIInnovation

Check out our newest paper about using #Computer #Vision for Root #Nodules Quantification. We compared Rule-based computer vision, #YOLOv12-seg transfer learning, and #SAM zero-shot segmentation for #detecting and #evaluating fluorescently labeled rhizobial nodules. This work demonstrates that classical rule-based methods can achieve strong performance when fluorescence provides clear chromatic separation, offering an interpretable and computationally efficient baseline. Supervised deep learning approaches, particularly intermediate-capacity models such as YOLOv12-m, provided the most balanced trade-off between segmentation accuracy, counting reliability, and computational feasibility, while SAM delivered stable but systematically biased underestimation and lacked intrinsic class discrimination. Thank you, Mohamed Salem, for your hard work, and to all co-authors for your significant contributions. Igathinathane Cannayen, Amanda Pease, Chandan Gautam, and Barney A. Geddes. You can access the full article here: https://lnkd.in/e6wZGAEp North Dakota State University NDSU Agriculture Rabia Lab #NewPublication #AI #Farming #AgTech #PrecisionAgriculture #ArtificialIntelligence