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Computer Vision and Deep Learning are the superstars of today’s AI universe, fueling everything from cars that drive themselves to medical tools smart enough to spot issues even seasoned doctors
Image annotation is a crucial step in computer vision that involves adding meaningful information—such as shapes, labels, or markers—to an image. This process is widely used in applications like object
MedSAM2 introduces a robust foundation model for promptable segmentation in 3D medical images and temporal video data, built by fine-tuning SAM2.1 on a large-scale curated medical dataset. Key Highlights: Resources
Computer vision is one of artificial intelligence’s most dynamic and rapidly advancing areas, enabling machines to interpret and understand the visual world. From self-driving cars that detect and avoid pedestrians
Fingerprint matching plays a crucial role in various security applications, such as identity verification and criminal investigations. While most fingerprint matching systems rely on large machine learning models and sophisticated
The OpenCV community is set to gather for an exceptional event, The OpenCV-SID Conference on Computer Vision & AI (OSCCA). Our conference is one full day, and will occur on
GenZ-ICP introduces an innovative iterative Closest Point (ICP) method that enhances LiDAR-based pose estimation by adaptively integrating point-to-plane and point-to-point error metrics, ensuring robust performance across diverse and degenerative environments.
Imperial College London unveils MASt3R-SLAM: a cutting-edge monocular dense SLAM system built on the revolutionary MASt3R two-view 3D reconstruction prior, delivering unmatched real-time accuracy and global consistency. Key Highlights: MASt3R-SLAM
Fast3R breaks the pairwise bottleneck in multi-view 3D reconstruction. Building on DUSt3R, it introduces a transformer-based architecture that directly regresses dense 3D pointmaps from unposed, unordered RGB images-processing 1000+ views
AirSLAM introduces a hybrid visual SLAM approach that integrates deep learning for feature detection with traditional backend optimization. Key highlights: Resource Links Related articles from LearnOpenCV:
Imagine you’re using an AI writing assistant to draft an email. It’s excellent at creating clear and concise text, but you need the email to reference specific updates from a
Creating a product is not just about software; hardware plays a crucial role as well. Specifically, in computer vision, we need compact edge hardware to deploy our object detection models