We have developed the CMAS system (Collaborative Medical Annotation System) so that medical professionals will be able to easily annotate digital medical records that contain medical imaging or procedure videos. The CMAS system enables a non-technical person to annotate a medical image or video with their recorded presence. The CMAS system displays medical images via a projector onto a screen; when a doctor (or patient) physically walks in front of this screen with the medical image and gives his/her opinion while gesturing at the image, the CMAS system intuitively captures this interaction by creating a video annotation with HP's Active Shadows technology. The CMAS system automatically transforms physical interactions, ranging from a laser pointer spot to a doctor's physical presence, into video annotation that then can be overlaid on top of the medical image or seamlessly inserted into the procedure video. Annotated in such a manner, the medical record retains the historical development of the diagnostic medical opinion, explained through presence of doctors and their respective annotations. The CMAS system structures the annotation of digital medical records such that image/video annotations from multiple sources, at different times, and from different locations can be maintained within a historical context and be consistently referenced among multiple annotations.
In this paper, we investigate the suitability of the GPU for a parallel implementation of the pinwheel error diffusion. We demonstrate a high-performance GPU implementation by efficiently parallelizing and unrolling the image processing algorithm. Our GPU implementation achieves a 10 − 30× speedup over a two-threaded CPU error diffusion implementation with comparable image quality. We have conducted experiments to study the performance and quality tradeoffs for differences in image block sizes. We also present a performance analysis at assembly level to understand the performance bottlenecks.
Historically, in the 35 years of digital printing research, raster image processing has always lagged behind marking engine technology, i.e., we have never been able to deliver rendered digital pages as fast as digital print engines can consume them. This trend has resulted in products based on throttled digital printers or expensive raster image processors (RIP) with hardware acceleration. The current trend in computer software architecture is to leverage graphic processing units (GPU) for computing tasks whenever appropriate. We discuss the issues for rendering fonts on such an architecture and present an implementation.
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