Shadow Algorithms Data Miner (Paperback)
Andrew Woo
Sold by AussieBookSeller, Truganina, VIC, Australia
AbeBooks Seller since June 22, 2007
New - Soft cover
Condition: New
Ships from Australia to U.S.A.
Quantity: 1 available
Add to basketSold by AussieBookSeller, Truganina, VIC, Australia
AbeBooks Seller since June 22, 2007
Condition: New
Quantity: 1 available
Add to basketPaperback. Shadow Algorithms Data Miner provides a high-level understanding of the complete set of shadow concepts and algorithms, addressing their usefulness from a larger graphics system perspective. It discusses the applicability and limitations of all the direct illumination approaches for shadow generation.With an emphasis on shadow fundamentals, the book gives an organized picture of the motivations, complexities, and categorized algorithms available to generate digital shadows. It helps readers select the most relevant algorithms for their needs by placing the shadow algorithms in real-world contexts and looking at them from a larger graphics system perspective. As a result, readers know where to start for their application needs, which algorithms to begin considering, and which papers and supplemental material should be consulted for further details. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Seller Inventory # 9780367381240
Shadow Algorithms Data Miner provides a high-level understanding of the complete set of shadow concepts and algorithms, addressing their usefulness from a larger graphics system perspective. It discusses the applicability and limitations of all the direct illumination approaches for shadow generation.
With an emphasis on shadow fundamentals, the book gives an organized picture of the motivations, complexities, and categorized algorithms available to generate digital shadows. It helps readers select the most relevant algorithms for their needs by placing the shadow algorithms in real-world contexts and looking at them from a larger graphics system perspective. As a result, readers know where to start for their application needs, which algorithms to begin considering, and which papers and supplemental material should be consulted for further details.
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