基于GPU的蜂窝和遥感成像实时微小变化检测

S. Kockara, Tansel Halic, Coskun Bayrak
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引用次数: 5

摘要

发现同一场景在不同时间间隔拍摄的成对图像的细微变化被称为微小变化检测问题。为了实现这一目标,我们开发了一个框架,可以捕捉和突出显示数字图像中的微小变化,否则这些变化对人眼来说是隐藏的。此外,在相似的观看条件下,以不同的时间间隔拍摄的图像对的不明显的差异被检测到。虽然我们的框架的应用领域涵盖了各种不同的学科,从医学到安全,天气预报,城市规划和监测自然灾害,但在这项研究中,我们的重点是实时微小变化检测和跟踪生物医学和卫星图像。在医疗等情况下,实时性能至关重要;我们通过使用图形处理单元(GPU)的广泛计算能力来增强这种方法。我们对分辨率为2600x2060的培养小鼠MC3T3-E1成骨细胞的光镜图像和通过GPU计算的遥感图像的细微变化检测实验结果表明,我们的算法可以实时检测图像上的微小差异。
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Real-time Minute Change Detection on GPU for Cellular and Remote Sensor Imaging
Discovering subtle alterations of pairs of images taken from the same scene at different time intervals is called minute change detection problem. To achieve this goal, we have developed a framework that captures and highlights minute changes in digital images that are otherwise hidden to the human eye. Moreover, unnoticeable differences from image pairs that are taken at different time intervals with similar viewing conditions are detected. Although our framework's application areas cover a wide variety of different disciplines, from medicine to security, weather forecasting, urban planning, and monitoring natural disasters, in this study our focus was real-time minute change detection and tracking on biomedical and satellite images. Real-time performance in cases such as medicine is crucial; we enhance this approach by using the extensive computational power of the graphical processing unit (GPU). Our experimental results in detection of subtle changes in light microscopic images of mouse MC3T3-E1 osteoblastic cells grown in culture with the resolution of 2600x2060 and remote sensor images performed by the GPU computations illustrate that our algorithm detects infinitesimal differences on images in real-time.
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