High Performance Deformable Image Registration Algorithms for Manycore Processors (Paperback)

James Shackleford, Nagarajan Kandasamy, Gregory Sharp

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High Performance Deformable Image Registration Algorithms for Manycore Processors develops highly data-parallel image registration algorithms suitable for use on modern multi-core architectures, including graphics processing units (GPUs). Focusing on deformable registration, we show how to develop data-parallel versions of the registration algorithm suitable for execution on the GPU. Image registration is the process of aligning two or more images into a common coordinate frame and is a fundamental step to be able to compare or fuse data obtained from different sensor measurements. Extracting useful information from 2D/3D data is essential to realizing key technologies underlying our daily lives. Examples include autonomous vehicles and humanoid robots that can recognize and manipulate objects in cluttered environments using stereo vision and laser sensing and medical imaging to localize and diagnose tumors in internal organs using data captured by CT/MRI scans.

  • Demonstrates how to redesign widely used image registration algorithms so as to best expose the underlying parallelism available in these algorithms
  • Shows how to pose and implement the parallel versions of the algorithms within the single instruction, multiple data (SIMD) model supported by GPUs
  • Provides Programming "tricks" that can help readers develop other image processing algorithms, including registration algorithms for the GPU

商品描述(中文翻譯)

《高性能可變形影像配准演算法在多核心處理器上的應用》開發了適用於現代多核心架構(包括圖形處理單元(GPU))的高度數據並行影像配准演算法。著重於可變形配准,我們展示了如何開發適用於GPU執行的數據並行配准演算法版本。影像配准是將兩個或多個影像對齊到共同座標框架的過程,是能夠比較或融合來自不同感測器測量的數據的基本步驟。從2D/3D數據中提取有用信息對於實現我們日常生活中的關鍵技術至關重要。例如,自動駕駛車輛和人形機器人可以使用立體視覺和激光感測在雜亂環境中識別和操作物體,醫學影像可以使用CT/MRI掃描捕獲的數據在內部器官中定位和診斷腫瘤。

本書演示了如何重新設計廣泛使用的影像配准演算法,以最佳方式展示這些演算法中可用的並行性。並展示了如何在GPU支援的單指令多數據(SIMD)模型中提出和實現並行版本的演算法。提供了一些編程技巧,可以幫助讀者開發其他影像處理演算法,包括適用於GPU的配准演算法。