Chosun University Researchers Introduce a Machine Learning Framework for Precise Localization of Bleached Corals Using Bag-of-Hybrid Visual Feature Classification

Chosun University Researchers Introduce a Machine Learning Framework for Precise Localization of Bleached Corals Using Bag-of-Hybrid Visual Feature Classification AI News, AI, AI tools, Aneesh Tickoo, Innovation, itinai.com, LLM, MarkTechPost, t.me/itinai 🌊 The Value of AI in Precise Localization of Bleached Corals 🐠 Coral reefs are incredibly diverse ecosystems that provide important environmental and economic benefits. However, global warming has led to coral reef bleaching, causing significant problems. Monitoring and surveying marine ecology is crucial to mitigate the consequences of climate change. 🔍 Challenges in Monitoring Coral Reefs Monitoring coral reefs is challenging due to artifacts and ambient noise in underwater images. It's difficult for computer vision systems to discriminate between the target item and the background. Variations in lighting, size, orientation, perspective, occlusions, and background clutter also degrade the performance of localization models. 💡 The Solution: AI and Deep Learning Researchers from Chosun University are developing a machine learning framework that uses AI and deep learning techniques to accurately locate bleached coral reefs. They aim to overcome the geometric and visual variances found in photos of maritime environments. 🔬 Features Extraction and Classification The framework combines handmade feature extraction methods and deep neural network (DNN) models. DNN models like ResNet, DenseNet, VGGNet, and Inception achieve excellent performance across various applications. However, limited bleached examples in existing datasets can compromise the robustness of the features. Handmade features, on the other hand, are independent of training data strength but can be impacted by changes in depth, underwater light, and water turbidity. 🎒 Bag-of-Hybrid Visual Feature Classification The suggested framework uses a hybrid approach to extract raw features and then reduces dimensionality and introduces more invariance using the Bag-of-Features (BoF) technique. Local characteristics from the picture are used to improve photometric invariance. The use of BoF also reduces complexity and storage requirements. After extensive experimentation, the researchers have determined the optimal patch, cluster size, kernel combination, and classifier. 💼 Benefits for Businesses By utilizing the machine learning framework developed by Chosun University researchers, businesses can: ✅ Improve accuracy in localizing bleached coral reefs ✅ Optimize monitoring and surveying of marine ecology ✅ Enhance understanding of the impacts of climate change ✅ Explore potential for identifying new medicinal substances and treatments To learn more about the research conducted by Chosun University researchers, click here: [insert link] For practical AI solutions that can redefine your sales processes and customer engagement, consider the AI Sales Bot from itinai.com/aisalesbot. This tool automates customer engagement 24/7 and manages interactions across all customer journey stages, helping you streamline your sales operations. To explore how AI can transform your business and identify automation opportunities, define KPIs, select the right AI solution, and implement AI gradually, connect with us at hello@itinai.com. Stay updated with the latest AI research news, cool AI projects, and more by joining our 33k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and Email Newsletter. 🔗 List of Useful Links: 🔬 AI Lab in Telegram @aiscrumbot – free consultation 📰 Chosun University Researchers Introduce a Machine Learning Framework for Precise Localization of Bleached Corals Using Bag-of-Hybrid Visual Feature Classification - MarkTechPost 🐦 Twitter – @itinaicom