🌟 Exciting News! Check out our latest blog post on enhancing digital hologram reconstruction using reverse-attention loss for untrained physics-driven deep learning models with uncertain distance. This pioneering approach addresses the Autofocusing challenge in untrained deep-learning methods, offering both theoretical analysis and experimental superiority in efficiency and accuracy. See the full article here: https://bit.ly/49WQPs7 #DigitalHologram #DeepLearning #PhysicsDrivenModels

❓ Are you curious how you can use #AI in #microscopy to transform your experiments and reveal hidden information in your #images? 🖥 Join our upcoming event to discover the secrets of using AI in microscopy! Register now! #Artificialintelligence #widefield #leicamicro

#PROGRACE_EMPHASIS 🫘What are the ways to automate the calculation of seed descriptor values and characterization of seeds? 💬Karsten Hartelius, Videometer A/S - "We measure what you see - and beyond", presents VideometerLab4 - a flexible lab and instrument for spectral imaging. The Videometer technology combines spectral imaging with elements of AI and machine learning and multivariate statistics. ℹ️ https://videometer.com/ #plantgeneticresources #cropbiodiversity

New release from MIT for Deep Learning based Structure from Motion achieving COLMAP-level accuracy. 🔥🔥 FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent FlowMap is the first self-supervised, end-to-end differentiable SfM method that provides COLMAP-level accuracy for 360° scenes. The model solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. This process normally requires unique image features across multiple view points, matching and triangulation to obtain camera poses and project the points to 3D space for reconstruction. It outperforms prior gradient-descent based bundle adjustment methods and surprisingly performs on par with COLMAP which has been the state-of-the-art SfM method for at least a decade. 👉 Check out the project page, paper and code here: https://lnkd.in/dVnx-JPA #reconstruction #sfm #artificialintelligence #deeplearning

💡Insightful Thursdays💡 Discover the cutting-edge Computer Vision algorithms driving advancements in object detection, image segmentation, motion estimation, and image reconstruction. #opencv #objectdetection #segmentation #computervision #deeplearning #ai

Neurocle AI software is a game-changer for machine vision systems. With its advanced algorithms, it enhances image analysis, enabling unparalleled precision in classification, segmentation, object detection, anomaly detection, and OCR as well as rotation. Transform your industry  with the power of Neurocle’s ai software to optimize your production processes! https://lnkd.in/d85GKxBE #Neurocle #airevolution #classification #segmentation #ojectdetection #anomalydetection #OCR #rotation

I am trilled to share that I have finished AI for medical diagnosis course in it I learned about diseases &medical images types, medical image processing, detection of diseases using computer vision ,image segmentation using U-Net architecture and model evaluation. #Ai #coursera

Training annotations can often be the bottleneck in developing deep learning algorithms for pathology applications. But what if there was a way to overcome this challenge?   Discover Visiopharm’s innovative patented method for labeling histopathological images and training deep learning models, offering you:   ·        Higher Accuracy ·        Consistency ·        Scalability ·        Cost Efficiency   With this method, you can achieve faster and more affordable time-to-market, with better algorithms in your development projects.    Visit our website to explore our deep learning applications for precision pathology: https://lnkd.in/dsyPdP8h   Learn more about our patent: https://lnkd.in/deU72p5X   #DeepLearning #TrainingAnnotations #PrecisionPathology #Visiopharm

We are excited to have multiple talks and papers accepted at the 2024 SPIE Defense + Commercial Sensing conference! 🧠 "Improving visual AI models with synthetic, hybrid, and perturbed training data," presented by Anthony Hoogs, covers recent advances in realistic data augmentation for applications where a target domain is known but has little test or training data. 🎯 "End-to-end machine learning for co-optimized sensing and automated target recognition," presented by Scott McCloskey, describes Kitware's two approaches to developing end-to-end machine learning methods for computational imaging that co-optimize the sensing hardware with downstream exploitation. 🛰 "Toward quantifying the real-versus-synthetic imagery training data ‘reality gap’ analysis and practical applications," authored by Colin Reinhardt, Anthony Hoogs, and Rusty Blue, reviews recent developments in the computer vision and AI research communities as they pertain to 3D reconstruction from overhead imagery. 🏅 "NRTK: an open source natural robustness toolkit for the evaluation of computer vision models," presented by Brian Hu, introduces the Natural Robustness Toolkit (NRTK), an open source platform for validated scene- and sensor-specific perturbations for evaluating the robustness of computer vision models. For more information on these papers and presentations, please visit our event page: https://lnkd.in/eJM356B4 #SPIEDCS #ai #remotesensing #sensors #algorithms #machinelearning #ATR #NRTK #opensource #computervision #3dreconstruction #syntheticimagery

📢 Call for reading a new paper publication titled “Automated Weld Defect Detection in Industrial Ultrasonic B-Scan Images Using Deep Learning” https://lnkd.in/eqtptH6W Author: Amir-M. Naddaf-Sh,Vinay S. Baburao and Hassan Zargarzadeh * #ndt #ultrasound #welddefectdetection #deeplearning