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Image scale measurement with correlation filters in a volume holographic optical correlator

机译:使用体积全息光学相关器中的相关滤波器进行图像比例测量

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A search engine containing various target images or different part of a large scene area is of great use for many applications, including object detection, biometric recognition, and image registration. The input image captured in realtime is compared with all the template images in the search engine. A volume holographic correlator is one type of these search engines. It performs thousands of comparisons among the images at a super high speed, with the correlation task accomplishing mainly in optics. However, the inputted target image always contains scale variation to the filtering template images. At the time, the correlation values cannot properly reflect the similarity of the images. It is essential to estimate and eliminate the scale variation of the inputted target image. There are three domains for performing the scale measurement, as spatial, spectral and time domains. Most methods dealing with the scale factor are based on the spatial or the spectral domains. In this paper, a method with the time domain is proposed to measure the scale factor of the input image. It is called a time-sequential scaled method. The method utilizes the relationship between the scale variation and the correlation value of two images. It sends a few artificially scaled input images to compare with the template images. The correlation value increases and decreases with the increasing of the scale factor at the intervals of 0.8~1 and 1~1.2, respectively. The original scale of the input image can be measured by estimating the largest correlation value through correlating the artificially scaled input image with the template images. The measurement range for the scale can be 0.8~4.8. Scale factor beyond 1.2 is measured by scaling the input image at the factor of 1/2, 1/3 and 1/4, correlating the artificially scaled input image with the template images, and estimating the new corresponding scale factor inside 0.8~1.2.
机译:包含各种目标图像或大场景区域不同部分的搜索引擎可用于许多应用程序,包括对象检测,生物识别和图像配准。将实时捕获的输入图像与搜索引擎中的所有模板图像进行比较。体积全息相关器是这些搜索引擎的一种。它以超高速执行图像之间的数千个比较,而相关任务主要在光学中完成。但是,输入的目标图像始终包含过滤模板图像的比例变化。那时,相关值不能正确反映图像的相似性。估计和消除输入目标图像的比例变化至关重要。存在三个用于执行比例尺测量的域,即空间域,频谱域和时域。大多数处理比例因子的方法都是基于空间或光谱域的。本文提出了一种时域方法来测量输入图像的比例因子。它称为时间顺序缩放方法。该方法利用了两个图像的比例变化和相关值之间的关系。它发送一些人工缩放的输入图像以与模板图像进行比较。随着比例因子的增加,相关值分别以0.8〜1和1〜1.2的间隔增大和减小。通过将人工缩放的输入图像与模板图像相关联来估计最大的相关值,可以测量输入图像的原始比例。秤的测量范围为0.8〜4.8。超过1.2的比例因子是通过将输入图像以1 / 2、1 / 3和1/4的比例缩放,将人工缩放的输入图像与模板图像相关联,并在0.8到1.2范围内估计新的相应比例因子来测量的。

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