Techniques for Computer Vision

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

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

👩🏻🏫 Vision Language Models(VLM) Architectures Guide ✍ VLM architectures used by mainstream models such as CLIP, Flamingo, VisualBert... ➡️ Contrastive Learning ▸ This approach trains models to differentiate between matching and non-matching image-text pairs by computing similarity scores. Try minimizes the distance between related pairs and maximizes it for unrelated ones. ▸ CLIP (Contrastive Language-Image Pretraining) use separate encoders for images and text, enable zero-shot predictions by jointly training these encoders and convert dataset classes into captions. ALIGN uses a distance metric to handle noisy datasets, minimizes embedding distances b/w matched pairs. ➡️ Prefix Language Modeling (PrefixLM) ▸ Images are treated as prefixes to textual input. Vision Transformers (ViTs) process images by dividing them into patch sequences, allowing the model to predict text based on visual context. ▸ SimVLM features a transformer architecture with an encoder-decoder structure, with strong zero-shot learning and VirTex uses CNN based feature extraction with transformer-based text processing ➡️ Frozen PrefixLM ▸ It leverages pre-trained language models, keeping them fixed while only updating the image encoder parameters. It reduces computational resources and training complexity. ▸ Flamingo, integrates a CLIP-like vision encoder with a pre-trained language model, processing images via a Perceiver Resampler, excels in few-shot learning. ➡️ Multimodal Fusion with Cross-Attention ▸ It integrates visual information into language models using cross-attention mechanisms, allowing the model to focus on relevant parts of the image when generate or interpret text.  ▸ VisualGPT use visual encoders for object detection, feed this into decoder layers, implements Self-Resurrecting Activation Units (SRAU) ➡️ Masked Language Modeling(MLM) & Image-Text Matching(ITM) ▸ Combine these two techniques, model predicts masked portions of text based on visual context (MLM) and determines whether a given caption matches an image (ITM) ▸ VisualBERT integrates with object detection frameworks to jointly train on both objectives, align text and image regions implicitly and efficient in visual reasoning tasks ➡️ Training-Free ▸ Some modern VLMs eliminate the need for extensive training,use existing embeddings ▸ MAGIC use CLIP-generated embeddings enable zero-shot multimodal tasks without additional training and ASIF use similarity b/w images and text to match query images with candidate descriptions ➡️ Knowledge Distillation ▸ Transferring knowledge from a large, well-trained teacher model to a lighter student model with fewer parameters ▸ ViLD (Vision and Language Knowledge Distillation), pre-trained open-vocabulary image classification model as the teacher to train a two-stage detector (student) 📌 Find high-quality version : https://lnkd.in/e--nfk4z #VLM #Architecture #VisionLanguage