1 option
Transformative Deep Learning for Image Colorization with Machine Learning-Based Classification John Deere
- Format:
- Book
- Conference/Event
- Author/Creator:
- Khalid, Maaz, author.
- Conference Name:
- Off-Highway Technical Conference 2025 (2025-11-06 : Pune, India)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- Imagine a user opening a technical manual, eager to troubleshoot an issue, only to find a mix of stark black-and-white illustrations alongside a few color images. This inconsistency not only detracts from the user experience but also complicates understanding. For technicians relying on these documents, grayscale graphics hinder quick interpretation of diagrams, extending diagnostics time and impacting overall productivity. Producing high-quality color graphics typically requires significant investment in time and resources, often necessitating a dedicated graphics team. Our innovative pipeline addresses this challenge by automating the colorization and classification of colored graphics. This approach delivers consistent, visually engaging content without the extensive investment in specialized teams, enhancing the visual appeal of materials and streamlining the diagnostic process for technicians. With clearer, more vibrant graphics, technicians can complete tasks more efficiently, ultimately saving time and money. Our project utilizes advanced deep learning techniques and a transformer-based architecture known as DDColor [2], focusing on: AUTOCOLORIZATION: Automating the colorization of grayscale graphics using a pixel decoder and a transformer-based color decoder that learns semantic-aware color representations. CLASSIFICATION: A classification model categorizes output images into "perfect" and "imperfect" buckets for rigorous quality control, ensuring only the best visuals are presented. AWS services are utilized to serve quick colorization requests, allowing for efficient processing and timely delivery of results. By implementing these technologies, we achieve consistent visuals while significantly reducing the time and resources required for graphic content development
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-28-0317
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.