Rahat Naz

Este livro oferece uma explora o abrangente da Recupera o de Imagem Baseada em Conte do (CBIR), uma t cnica que supera as limita es da pesquisa tradicional baseada em texto, aproveitando carater sticas visuais intr nsecas, como cor, textura e forma. Destaca a evolu o da CBIR, as suas principais metodologias e aplica es pr ticas em diversos dom nios, como os cuidados de sa de, a educa o, o com rcio e a preven o do crime. O livro aprofunda as tecnologias avan adas, como a extra o de carater sticas, o feedback de relev ncia e os modelos de aprendizagem autom tica, centrando-se na forma como melhoram a precis o e a efici ncia da recupera o. Al m disso, aborda desafios significativos, incluindo a complexidade computacional, a escalabilidade e quest es ticas na implementa o de sistemas CBIR. Olhando para o futuro, o livro explora futuras inova es em CBIR, como a recupera o personalizada, a pesquisa multimodal e a sua integra o com tecnologias emergentes como a realidade aumentada. um recurso essencial para investigadores, profissionais e inovadores, real ando o potencial transformador da CBIR num mundo cada vez mais orientado por dados de imagem e multim dia.

This book offers a comprehensive exploration of Content-Based Image Retrieval (CBIR), a technique that overcomes the limitations of traditional text-based search by leveraging intrinsic visual features such as color, texture, and shape. It highlights the evolution of CBIR, its core methodologies, and practical applications across diverse fields like healthcare, education, commerce, and crime prevention. The book delves into advanced technologies such as feature extraction, relevance feedback, and machine learning models, focusing on how they improve retrieval accuracy and efficiency. Additionally, it addresses significant challenges, including computational complexity, scalability, and ethical issues in implementing CBIR systems. Looking forward, the book explores future innovations in CBIR, such as personalized retrieval, multimodal search, and its integration with emerging technologies like augmented reality. It serves as an essential resource for researchers, practitioners, and innovators, emphasizing CBIR's transformative potential in a world increasingly driven by image and multimedia data.

"Mastering Image Dehazing: Techniques and Algorithms in AI" explores advanced techniques in image dehazing, focusing on improving visual clarity for applications like aerial photography, surveillance, and remote sensing. It introduces innovative approaches such as Globally Guided Image Filtering (G-GIF) and deep learning models, offering solutions for single-image dehazing. With practical implementation in MATLAB and a focus on perceptual fog density, it presents significant advancements in recovering fine details and enhancing image quality. Aimed at researchers and professionals, this work serves as a concise guide to mastering modern dehazing methods.