Robotic Vision and Image Processing

🧠 New Research: "Foveated Active Vision" allows AI to dynamically adjust focus like human eyes do. This could slash computational costs while improving detail recognition. No extra training needed. From: @LearningLukeD from @SakanaAILabs. Let's dig in ⬇️ 🎯 THE PROBLEM: Current vision systems process entire images at full resolution - massively inefficient. Like reading a newspaper with a magnifying glass over every word simultaneously. Robots need smarter visual attention to operate in real environments. 🔬 NATURE'S BLUEPRINT: Your eye's fovea processes ~2° of sharp detail while the periphery handles context at 1000x lower resolution. This lets you read text while staying aware of movement around you - critical for survival and navigation. ⚡ THE SOLUTION: Continuous Thought Machines (CTMs) mimic this with: - High-res "fovea" for detail analysis - Low-res periphery for context - Dynamic attention without reinforcement learning Elegantly simple, naturally emergent. 🤖 ROBOTICS IMPACT: This could transform: - Autonomous vehicles (focus on pedestrians, read signs simultaneously) - Surgical robots (detailed tissue work + spatial awareness) - Inspection drones (zoom on defects, maintain flight path) - Warehouse robots (precise picking + obstacle avoidance) 📊 WHY IT MATTERS: Current CNNs need massive models to handle multi-scale objects. Foveated vision could enable: ✅ smaller models ✅ Real-time processing on edge devices ✅ Better human-robot interaction ✅ Adaptive visual attention Biology continues to be our best teacher for intelligent systems. 🌿

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

𝐓𝐡𝐞 𝐍𝐞𝐮𝐫𝐨𝐦𝐨𝐫𝐩𝐡𝐢𝐜 𝐄𝐲𝐞: 𝐑𝐞𝐝𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐕𝐢𝐬𝐢𝐨𝐧 𝐢𝐧 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 Event-based vision stands as one of the most extraordinary evolutions in modern computing — a departure from the static, frame-based way we’ve taught machines to see. Instead of capturing full images at regular intervals, these sensors function like living retinas, reacting only when change occurs. Each microsecond, they register light variation rather than redundant frames, building a world not of still pictures, but of motion, intent, and emergence. The impact is staggering. Dynamic Vision Sensors (DVS) now achieve over 140 dB of dynamic range and respond faster than the human eye, operating at power levels under a milliwatt per pixel. This means machines can navigate environments of blinding light or deep shadow with unmatched precision. In robotics, it enables drones to avoid obstacles at high speed, arms to grasp fluidly, and autonomous systems to map in real time — without the computational drag of processing irrelevant information. From human-machine interfaces and biometric recognition to environmental monitoring, astronomy, and healthcare, event-based vision transforms perception itself. It can read the subtle flicker of a heartbeat on a wrist, classify gestures at a thousand frames per second, and track stars or cellular motion with microscopic accuracy. These systems operate at the intersection of biology and computation — where vision becomes a pulse of thought rather than a captured image. Yet this revolution is only beginning. As spiking neural networks, multimodal sensor fusion, and native event-driven architectures mature, we will see machines capable of perceiving reality as fluidly as we do — with intuition, timing, and anticipation. Singularity Systems, the research arm of Cybersecurity Insiders, is exploring these neuromorphic pathways to redefine what machines can sense, understand, and become. #changetheworld

I've started a series of short experiments using advanced Vision-Language Models (#VLM) to improve #robot #perception. In the first article, I showed how simple prompt engineering can steer Grounded SAM 2 to produce impressive detection and segmentation results. However, the major challenge remains: most #robotic systems, including mine, lack GPUs powerful enough to run these large models in real time. In my latest experiment, I tackled this issue by using Grounded SAM 2 to auto-label a dataset and then fine-tuning a compact #YOLO v8 model. The result? A small, efficient model that detects and segments my SHL-1 robot in real time on its onboard #NVIDIA #Jetson computer! If you're working in #robotics or #computervision and want to skip the tedious process of manually labeling datasets, check out my article (code included). I explain how I fine-tuned a YOLO model in just a couple of hours instead of days. Thanks to Roboflow and its amazing #opensource tools for making all of this more straightforward. #AI #MachineLearning #DeepLearning

🚀 𝐇𝐎𝐖 𝐘𝐎𝐋𝐎 𝐖𝐎𝐑𝐊𝐒 (𝐒𝐓𝐄𝐏-𝐁𝐘-𝐒𝐓𝐄𝐏) 𝐘𝐎𝐋𝐎 (𝐘𝐨𝐮 𝐎𝐧𝐥𝐲 𝐋𝐨𝐨𝐤 𝐎𝐧𝐜𝐞) revolutionized Computer Vision by treating object detection as a single regression problem. Unlike older methods that looked at thousands of region proposals, YOLO looks at the entire image 𝐨𝐧𝐜𝐞 and predicts everything in a single forward pass. 𝟏. 𝐈𝐍𝐏𝐔𝐓 & 𝐏𝐑𝐄𝐏𝐑𝐎𝐂𝐄𝐒𝐒𝐈𝐍𝐆 Before the "magic" happens, the image must be standardized. 𝐑𝐞𝐬𝐢𝐳𝐢𝐧𝐠: YOLO requires a fixed input size (e.g., $416 \times 416$ or $640 \times 640$). 𝐍𝐨𝐫𝐦𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Pixel values are scaled (usually between 0 and 1) to help the neural network converge faster during processing. 𝟐. 𝐓𝐇𝐄 𝐁𝐀𝐂𝐊𝐁𝐎𝐍𝐄: 𝐂𝐍𝐍 𝐅𝐄𝐀𝐓𝐔𝐑𝐄 𝐄𝐗𝐓𝐑𝐀𝐂𝐓𝐈𝐎𝐍 The resized image is fed into a 𝐃𝐞𝐞𝐩 𝐂𝐨𝐧𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐚𝐥 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 (𝐂𝐍𝐍). 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧: The network identifies low-level features (edges, corners) and high-level features (eyes, wheels, textures). 𝐆𝐫𝐢𝐝 𝐃𝐢𝐯𝐢𝐬𝐢𝐨𝐧: YOLO conceptually divides the image into an $S \times S$ grid. Each grid cell is responsible for detecting an object if the center of that object falls within the cell. 𝟑. 𝐓𝐇𝐄 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 𝐇𝐄𝐀𝐃: 𝐓𝐇𝐑𝐄𝐄-𝐈𝐍-𝐎𝐍𝐄 𝐏𝐑𝐄𝐃𝐈𝐂𝐓𝐈𝐎𝐍 For every grid cell, the model predicts multiple 𝐁𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐁𝐨𝐱𝐞𝐬 and their properties simultaneously: 𝐀𝐧𝐜𝐡𝐨𝐫 𝐁𝐨𝐱𝐞𝐬: Pre-defined shapes that help the model guess the size of objects (like a tall rectangle for a person or a wide one for a car). 𝐂𝐥𝐚𝐬𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧𝐬: The probability of what the object is (Dog, Cat, Car). 𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐒𝐜𝐨𝐫𝐞: How sure the model is that an object actually exists in that box. 𝟒. 𝐅𝐈𝐋𝐓𝐄𝐑𝐈𝐍𝐆: 𝐓𝐇𝐑𝐄𝐒𝐇𝐎𝐋𝐃𝐈𝐍𝐆 & 𝐍𝐌𝐒 A single pass often results in "noisy" or overlapping detections. YOLO cleans this up using two steps: 𝐓𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝𝐢𝐧𝐠: Any box with a confidence score below a certain limit (e.g., $0.5$) is instantly discarded. 𝐍𝐨𝐧-𝐌𝐚𝐱 𝐒𝐮𝐩𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧 (𝐍𝐌𝐒): If multiple boxes detect the same object, YOLO calculates the 𝐈𝐨𝐔 (𝐈𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭𝐢𝐨𝐧 𝐨𝐯𝐞𝐫 𝐔𝐧𝐢𝐨𝐧). It keeps the box with the highest confidence and suppresses the others to ensure one label per object. 𝟓. 𝐅𝐈𝐍𝐀𝐋 𝐎𝐔𝐓𝐏𝐔𝐓: 𝐑𝐄𝐀𝐋-𝐓𝐈𝐌𝐄 𝐃𝐄𝐓𝐄𝐂𝐓𝐈𝐎𝐍 The final result is an image with bounding boxes, class labels, and accuracy percentages. Because this entire process happens in one mathematical flow, YOLO can process $𝟒𝟓$ to $𝟏𝟓𝟎$ frames per second, making it the king of real-time AI. 🔥 𝐓𝐇𝐄 𝐁𝐎𝐓𝐓𝐎𝐌 𝐋𝐈𝐍𝐄: Older models were like looking through a keyhole and moving it around the door. YOLO is like opening the door and taking in the whole room at once. That is why it is the "Gold Standard" for speed in Computer Vision. #𝐘𝐎𝐋𝐎 #𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐕𝐢𝐬𝐢𝐨𝐧 #𝐀𝐈 #𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 #𝐎𝐛𝐣𝐞𝐜𝐭𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 #𝐃𝐞𝐞𝐩𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠

In Robot Learning, connecting complex observations, like RGB images, to simple robotic actions is challenging because these two areas are very different. This becomes even harder with limited data. This is why researchers at the 𝐃𝐲𝐬𝐨𝐧 𝐑𝐨𝐛𝐨𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐋𝐚𝐛 and Imperial College London introduced 𝐑𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐝 𝐃𝐢𝐟𝐟𝐮𝐬𝐞, which connects low-level robot actions and RGB observations using virtual images of the robot's 3D model. Combining these observations and actions in the image space, 𝐑𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐝 𝐃𝐢𝐟𝐟𝐮𝐬𝐞 calculates low-level robot actions through a learned process that gradually updates the robot's virtual images. This approach simplifies the learning problem and includes helpful patterns for using fewer samples and generalizing to different spaces. The team tested several versions of 𝐑𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐝 𝐃𝐢𝐟𝐟𝐮𝐬𝐞 in simulations and demonstrated their use in six everyday tasks in the real world. The results showed that 𝐑𝐞𝐧𝐝𝐞𝐫 𝐚𝐧𝐝 𝐃𝐢𝐟𝐟𝐮𝐬𝐞 has strong spatial generalization abilities and is more efficient with samples than common image-to-action methods. 📝 Research Paper: https://lnkd.in/eW5tmVsh 📊 Project Page: https://lnkd.in/eX3df_JU #robotics #research

Feature Detection and Tracking Redefined: Leveraging DAVIS for Hybrid Vision Applications "Feature Detection and Tracking with the Dynamic and Active pixel Vision Sensor DAVIS" This research presents the first algorithm to detect and track visual features using the hybrid capabilities of the DAVIS sensor, which combines a standard camera and an event-based sensor in a single-pixel array. Key contributions include: - Detection of visual features in grayscale frames and asynchronous tracking during the blind time between frames using the event stream. - Feature design optimized for the DAVIS, leveraging large spatial contrast variations (visual edges) that generate most events. - An event-based iterative geometric registration algorithm for robust feature tracking. Advantages: - Provides high-frequency measurement updates during blind times. - Enables robust performance in high-speed vision and robotics applications. Evaluation: - The method is tested on real DAVIS sensor data, demonstrating its effectiveness. Video - https://lnkd.in/ecDDqkGN Paper - https://lnkd.in/eFygS69j -------------------------------- Join my WhatsApp Robotics Channel - https://lnkd.in/dYxB9iCh Join our Robotics Community - https://lnkd.in/e6twxYJF Opportunity_21: https://lnkd.in/ejcs8EEb -------------------------------- #robotics

I have been tinkering with a small robot this spring 🤖🍓. As someone deeply passionate about #AI and agricultural #robotics, I built a full perception-to-action pipeline using #SLAM and depth-aware computer vision, with real-time 3D visualization in #RViz to monitor and debug the robot's spatial understanding as it works. The demo below showcases #ROS2, real-time 3D mapping, and a vision-guided robotic arm autonomously picking a strawberry. Did it fail the first time? Absolutely 😉 Did it eventually pick the berry? You bet! All of this runs entirely on a RaspberryPi including #SLAM, vision models, and servo actuation, all on the edge! If this interests you, please find the relevant blog and GitHub repo below. Read more: https://lnkd.in/ewkH-WrR GitHub repo: https://lnkd.in/eycUENWM #ComputerVision #Robotics #ROS2 #PrecisionAgriculture #EdgeAI #DeepLearning Hiwonder

🎥🎥This AI Sees Depth from ONE Image 🤯 (Is It Cheating Physics?)🎥🎥 Latest video on Embodied Intelligence: https://lnkd.in/eRRvBSSh How do AI models predict depth from a single image - with no stereo cameras or LiDAR? In this video, we dive into monocular depth estimation using deep learning, breaking down how modern supervised models infer 3D structure from just pixels. We’ll cover: ✅ What monocular depth estimation is and why it’s fundamentally ill-posed ✅ How supervised learning enables depth prediction from large-scale datasets ✅ A deep dive into popular models: Intel MiDaS, ZoeDepth, DMD, Depth Anything, DepthCrafter, and Intrinsic LoRA-based approaches ✅ How these models differ in training data, supervision, and generalization ✅ Common failure modes - when monocular depth breaks down and why ✅ Why scale, lighting, texture, and scene bias still matter This video focuses on how these models actually work, not just how to run them. We’ll compare strengths and weaknesses across architectures, discuss why some models generalize better than others, and highlight where monocular depth still struggles in real-world robotics and autonomous systems. Whether you're new to robotics or an AI enthusiast, this video will give you a clear and fun introduction to the world of robots! 🔔 Subscribe for demystifying and deeper dives into perception, computer vision, AI, and robotics! 👍 Like this video if you enjoy learning about intelligent machines! 📩 Have questions? Drop them in the comments! #robotics #ai #computervision #automation #WhatIsARobot #technology #innovation #sensing #autonomy #artificialintelligence #embodiedintelligence #robot #computervision #deeplearning #monoculardepth #depthestimation #MiDaS #ZoeDepth #DepthAnything #DepthCrafter #DMD #AI #robotics #perception #embodiedintelligence #selfdriving #3Dvision

It is difficult to get robots to be both precise and general. We just released a new technique for precise manipulation that achieves millimeter-level precision while being robust to large visual variations. The key is a careful combination of visuo-tactile learning and RL. The insight here is: vision and tactile are complementary. Vision is good at spatial, semantic cues, while touch excels at local contact feedback. ViTaL is a recipe to combine the two to enable precise control at >90% success rates even in unseen environments. For the full paper, videos and open-sourced code: https://lnkd.in/eAfhz8sE This work was led by Zifan Zhao & Raunaq Bhirangi, and a collaboration with Siddhant Haldar & Jinda Cui.